U.S. patent application number 16/907125 was filed with the patent office on 2021-12-23 for using temporal filters for automated real-time classification.
The applicant listed for this patent is NVIDIA Corporation. Invention is credited to Niranjan Avadhanam, Shagan Sah, Sakthivel Sivaraman.
Application Number | 20210397885 16/907125 |
Document ID | / |
Family ID | 1000004926760 |
Filed Date | 2021-12-23 |
United States Patent
Application |
20210397885 |
Kind Code |
A1 |
Sivaraman; Sakthivel ; et
al. |
December 23, 2021 |
USING TEMPORAL FILTERS FOR AUTOMATED REAL-TIME CLASSIFICATION
Abstract
In various examples, the present disclosure relates to using
temporal filters for automated real-time classification. The
technology described herein improves the performance of a
multiclass classifier that may be used to classify a temporal
sequence of input signals--such as input signals representative of
video frames. A performance improvement may be achieved, at least
in part, by applying a temporal filter to an output of the
multiclass classifier. For example, the temporal filter may
leverage classifications associated with preceding input signals to
improve the final classification given to a subsequent signal. In
some embodiments, the temporal filter may also use data from a
confusion matrix to correct for the probable occurrence of certain
types of classification errors. The temporal filter may be a linear
filter, a nonlinear filter, an adaptive filter, and/or a
statistical filter.
Inventors: |
Sivaraman; Sakthivel; (Santa
Clara, CA) ; Sah; Shagan; (Santa Clara, CA) ;
Avadhanam; Niranjan; (Saratoga, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NVIDIA Corporation |
Santa Clara |
CA |
US |
|
|
Family ID: |
1000004926760 |
Appl. No.: |
16/907125 |
Filed: |
June 19, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06K 9/00389 20130101;
G06K 9/00355 20130101; G06K 9/42 20130101; G06K 9/00751 20130101;
G06N 3/0454 20130101; G06K 9/6227 20130101; G06K 9/6226
20130101 |
International
Class: |
G06K 9/62 20060101
G06K009/62; G06K 9/00 20060101 G06K009/00; G06N 3/04 20060101
G06N003/04; G06K 9/42 20060101 G06K009/42 |
Claims
1. A method for assigning a classification to an input signal
comprising: receiving, based at least in part on a multiclass
classifier processing the input signal, a raw classification output
representative of a first raw confidence score, the first raw
confidence score corresponding to a first class; computing a first
normalization amount corresponding to the first class by using a
confusion factor between the first class and a second class, the
confusion factor representative of a probability that the
multiclass classifier will compute, for a generic input signal
known to correspond to the second class, an output indicating that
the generic input signal corresponds to the first class; generating
a first normalized confidence score corresponding to the first
class by adjusting the first raw confidence score according to the
first normalization amount; applying a temporal filter to the first
normalized confidence score to generate a final confidence score
corresponding to the first class; and determining a final
classification for the input signal based at least in part on the
final confidence score corresponding to the first class.
2. The method of claim 1, further comprising computing a second
normalization amount corresponding to the second class by using the
confusion factor between the first class and the second class and
generating a second normalized confidence score corresponding to
the second class by adjusting a second raw confidence score
corresponding to the second class according to the second
normalization amount.
3. The method of claim 2, wherein the adjusting the second raw
confidence score according to the second normalization amount
includes adding the second normalization amount to the second raw
confidence score.
4. The method of claim 1, wherein the adjusting the first raw
confidence score according to the first normalization amount
includes subtracting the first normalization amount from the first
raw confidence score.
5. The method of claim 1, wherein the temporal filter includes an
adaptive filter.
6. The method of claim 1, wherein the first raw confidence score is
generated by combining an output of a softmax calculation with an
output of an angular visual hardness calculation.
7. A method for assigning a classification to an input signal
comprising: receiving a temporal series of raw classification
outputs that a multiclass classifier generated by processing a
temporal sequence of input signals, each raw classification output
including a class confidence score for each of a plurality of
classes the multiclass classifier is trained to identify; detecting
a classification state change within a first set of the raw
classification outputs indicating a probable classification change
from a first class to a second class; tuning, based at least in
part on the classification state change, an adaptive filter to
decrease weight given to older confidence scores corresponding to
the first class within a temporal sequence of confidence scores
corresponding to the first class when calculating a final
confidence score corresponding to the first class; applying the
adaptive filter to the temporal sequence of confidence scores
corresponding to the first class in a second set of classification
outputs to generate the final confidence score corresponding to the
first class; and generating a final classification for the input
signal using the final confidence score.
8. The method of claim 7, wherein the first set comprises fewer
classification outputs than the second set.
9. The method of claim 7, further comprising: tuning, based at
least in part on the classification state change, the adaptive
filter to decrease weight given to older confidence scores
corresponding to the second class within a temporal sequence of
confidence scores corresponding to the second class when
calculating a final confidence score corresponding to the second
class; and applying the adaptive filter to the temporal sequence of
confidence scores corresponding to the second class in the second
set of the raw classification outputs to generate the final
confidence score corresponding to the second class.
10. The method of claim 7, further comprising: applying the
adaptive filter to a temporal sequence of confidence scores
corresponding to a third class in the second set of the raw
classification outputs to generate a final confidence score
corresponding to the third class, wherein, in response to the third
class not being involved in the classification state change between
the first class and the second class, using default weights in the
adaptive filter when calculating the final confidence score
corresponding to the third class.
11. The method of claim 7, wherein the weight is decreased by
increasing a rate of decay in a decay function within the adaptive
filter.
12. The method of claim 7, further comprising: computing a first
normalization amount corresponding to the first class using a
confusion factor between the first class and the second class, the
confusion factor representative of a probability that the
multiclass classifier will compute, for a generic input signal
known to correspond to the second class, an output indicating that
the generic input signal corresponds to the first class; generating
a first normalized confidence score corresponding to the first
class by adjusting a first raw confidence score corresponding to
the first class according to the first normalization amount; and
wherein the temporal sequence of confidence scores corresponding to
the first class comprises the first normalized confidence
score.
13. The method of claim 12, wherein the adjusting the first raw
confidence score according to the first normalization amount
includes subtracting the first normalization amount from the first
raw confidence score.
14. The method of claim 7, further comprising: decreasing an amount
of raw classification outputs in the second set of the raw
classification outputs from a default amount to a
fresh-state-change amount in response to the classification state
change.
15. The method of claim 7, wherein the raw classification outputs
are generated by combining an output of a softmax calculation with
an output of an angular visual hardness calculation.
16. A method for assigning a classification to an input signal
comprising: receiving a temporal series of raw classification
outputs that a multiclass classifier generated by processing a
temporal sequence of input signals, each raw classification output
including a class confidence score for each of a plurality of
classes the multiclass classifier is trained to identify; detecting
a classification state change within a first set of the raw
classification outputs indicating a probable classification change
from first class to a second class; tuning, based at least in part
on the classification state change, an adaptive filter to decrease
weight given to older confidence scores corresponding to the first
class within a temporal sequence of confidence scores corresponding
to the first class when calculating a final confidence score
corresponding to the first class; computing a first normalization
amount corresponding to the first class using a confusion factor
between the first class and the second class, the confusion factor
representative of a probability that the multiclass classifier will
compute, for a generic input signal known to correspond to the
second class, an output indicating that the generic input signal
corresponds to the first class; generating a first normalized
confidence score corresponding to the first class by adjusting a
first raw confidence score corresponding to the first class
according to the first normalization amount; applying the adaptive
filter to a second set of classification outputs that includes the
first normalized confidence score to generate the final confidence
score corresponding to the first class, and generating a final
classification for the input signal using the final confidence
score corresponding to the first class as input.
17. The method of claim 16, wherein the method further comprises:
tuning, based at least in part on the classification state change,
an adaptive filter to decrease weight given to older confidence
scores corresponding to the second class within a temporal sequence
of confidence scores corresponding to the second class when
calculating a final confidence score corresponding to the second
class; and applying the adaptive filter to the temporal sequence of
confidence scores corresponding to the second class in the second
set of the raw classification outputs to generate the final
confidence score corresponding to the second class.
18. The method of claim 16, further comprising: applying the
adaptive filter to a temporal sequence of confidence scores
corresponding to a third class in the second set of the raw
classification outputs to generate a final confidence score
corresponding to the third class, wherein, in response to the third
class not being involved in the classification state change between
the first class and the second class, using default weights in the
adaptive filter when calculating the final confidence score
corresponding to the third class.
19. The method of claim 16, wherein the adjusting the first raw
confidence score according to the first normalization amount
includes subtracting the first normalization amount from the first
raw confidence score.
20. The method of claim 16, further comprising: decreasing an
amount of raw classification outputs in the second set of the raw
classification outputs from a default amount to a
fresh-state-change amount in response to the classification state
change.
Description
BACKGROUND
[0001] Multiclass classifiers are used to assign a class
distribution to an input signal. The class distribution may include
a confidence score indicating that the input signal should be
assigned to one or more of the classes. For example, multiclass
classifiers may be used to classify a temporal sequence of input
signals, where each signal in the sequence may be assigned a
corresponding class distribution.
[0002] The most common approach in existing solutions is to run the
classification network at a constant signal analysis rate (e.g.,
window size). However, this constant analysis rate approach may
suffer decreased accuracy during class transitions. For example,
mid-transition, the signals being analyzed may include half
representing a first class and half representing a second class. As
such, existing technologies fail to adapt the classification
process to account for possible class transitions.
[0003] Currently, despite best efforts at training, classifiers
will incorrectly classify some input signals into an improper
class. This performance can be measured in using confusion
analysis. For example, the current approach is to retrain
classifiers until the measured confusion is deemed acceptable for
deployment in the particular use case; however, this approach does
not account for the confusion when calculating the final
classification result. Instead, the typical approach is to use the
class assigned the highest confidence score without other
considerations.
SUMMARY
[0004] Embodiments of the present disclosure relate to using
temporal filters for automated real-time classification. Systems
and methods are disclosed for improving the performance of a
multiclass classifier that may be used to classify a temporal
sequence of input signals--such as input signals representative of
video frames. A performance improvement of the system may be
achieved, at least in part, by applying the temporal filter to an
output of the multiclass classifier. The temporal filters described
herein may correspond to, without limitation, a linear filter, a
nonlinear filter, an adaptive filter, and/or a statistical filter.
As an example, a temporal filter may leverage classifications
associated with preceding input signals to improve the final
classification given to a subsequent signal.
[0005] In contrast to conventional systems, such as those described
above, the technology described herein may leverage classifications
associated with preceding input signals to improve the final
classification given to a subsequent signal, while also factoring
in a confusion matrix to correct for the probable occurrence of
certain types of classification errors. In some embodiments, a
preliminary signal analysis may detect a presumptive class change
in the classifier output, for example, as evidenced by the highest
confidence score in the raw output transitioning from association
with a first class to a second class. A class shift may indicate
that older output data may be less relevant than the newer output
data and this information may be taken into account by the adaptive
filter by giving more weight to recent classification outputs when
the preliminary signal analysis detects a class shift.
[0006] In some embodiments, a normalization process may adjust the
raw classification confidence scores according to data from a
confusion matrix. In general, the confidence score assigned to a
first class (e.g., class A) may be lowered in proportion to the
probability that the first class is a false positive of the other
classes (e.g., class B, class C, class D). Conversely, the
confidence score for a given class may be increased in proportion
to the probabilities that other classes are false positives for the
given class. The normalization process may optimize or improve the
accuracy of the classification by accounting for the probability of
different kinds of errors occurring in the classification. As a
result, when the normalization process is combined with the
temporal filtering operation--which uses data from multiple
consecutive classifications--the overall classification accuracy of
the system may be meaningfully improved without a significant
contribution to the overall latency of the classification
pipeline.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The present systems and methods for using temporal filters
for automated real-time classification are described in detail
below with reference to the attached drawing figures, wherein:
[0008] FIG. 1 is an illustration of an example real-time signal
classification system, in accordance with some embodiments of the
present disclosure;
[0009] FIG. 2 is an illustration of an example softmax output and
an angular hardness output, in accordance with some embodiments of
the present disclosure;
[0010] FIG. 3 is an illustration of an example classification
change in response to a signal change, in accordance with some
embodiments of the present disclosure;
[0011] FIG. 4 is an illustration of an example confusion matrix, in
accordance with some embodiments of the present disclosure;
[0012] FIGS. 5-7 are flow charts showing methods of assigning a
classification to an input signal, in accordance with some
embodiments of the present disclosure;
[0013] FIG. 8A is an illustration of an example autonomous vehicle,
in accordance with some embodiments of the present disclosure;
[0014] FIG. 8B is an example of camera locations and fields of view
for the example autonomous vehicle of FIG. 8A, in accordance with
some embodiments of the present disclosure;
[0015] FIG. 8C is a block diagram of an example system architecture
for the example autonomous vehicle of FIG. 8A, in accordance with
some embodiments of the present disclosure;
[0016] FIG. 8D is a system diagram for communication between
cloud-based server(s) and the example autonomous vehicle of FIG.
8A, in accordance with some embodiments of the present disclosure;
and
[0017] FIG. 9 is a block diagram of an example computing device
suitable for use in implementing some embodiments of the present
disclosure.
DETAILED DESCRIPTION
[0018] Systems and methods are disclosed related to using temporal
filters for automated real-time classification. The technology
described herein improves the performance of a multiclass
classifier that may be used to classify a temporal sequence of
input signals--such as input signals representative of video
frames. A performance improvement of the system may be achieved, at
least in part, by applying the temporal filter to an output of the
multiclass classifier. For example, the temporal filter may
leverage classifications associated with preceding input signals to
improve the final classification given to a subsequent signal. In
some embodiments, the temporal filter may also use data from a
confusion matrix to correct for the probable occurrence of certain
types of classification errors.
[0019] Depending on the embodiment, the temporal filter may take
many different forms. For example, the temporal filter may be a
linear filter, a nonlinear filter, an adaptive filter, and/or a
statistical filter. In each example, the overall operation of the
filter may be similar. For example, the filter may receive a
temporal sequence of outputs from the multiclass classifier--e.g.,
x number of consecutive outputs generated by classifying x number
of consecutive input signals. In embodiments, the number of outputs
received may be described as an analysis window. As the outputs are
received, the outputs may be filtered together and a final
confidence score for each class in each instance of the output data
may be generated using the temporal filter.
[0020] Each individual output in the sequence may include a series
of confidence scores for each class the multiclass classifier is
trained to identify. For example, a classifier trained to assign
one of five different classes to an input signal would output a
confidence score for each of the five classes. As described herein,
the temporal filter may receive, as input, a sequence of outputs of
the multiclass classifier and generate a final confidence factor
for each class. The final confidence factor may correspond to the
final output of the process and effectively replace the newest raw
output within the sequence of outputs input to the temporal filter.
The final output may then be used to assign an active
classification to the corresponding input signal, and this process
may repeat as new outputs are received from the classifier--with
the oldest output dropping out of the sequence and the newest one
being added (e.g., as a rolling buffer of output signals).
[0021] Aspects of the technology described herein may account for
confusion between classes within the temporal filter by applying a
class normalization to the raw output data using data from a
confusion matrix. For example, the class confusion may be
determined by analyzing the performance of the trained classifier
using ground truth data. The ground truth data may be determined,
as a non-limiting example, by having a user assign a ground truth
label to the signal input used to test the classifier performance.
In some embodiments, the class confusion analysis may be an
off-line process that results in a class confusion matrix or other
memorialization of the confusion analysis. However, in other
embodiments, the class confusion analysis may be on an on-line
process, a process that occurs at initialization of the system,
and/or at another time.
[0022] Data from the confusion matrix may be used in a
normalization process. For example, because class confusion may
assign a probability of occurrence to certain types of
classification failures, then, for a given class, the confusion
matrix may include data indicating a probability that an input
signal with a ground truth classification in the given class is a
true positive or a false positive classification. A true positive
may indicate that the input signal was correctly classified into
the given class and a false positive may indicate that an input
signal was incorrectly classified into a different class. Each
different class may receive its own probability of receiving a
false positive classification for the given class.
[0023] In some embodiments, the normalization process may adjust
the raw classification confidence scores according to data from the
confusion matrix. In general, the confidence score assigned to a
first class (e.g., class A) may be lowered in proportion to the
probability that the first class is a false positive of the other
classes (e.g., class B, class C, class D). Conversely, the
confidence score for a given class may be increased in proportion
to the probabilities that other classes are false positives for the
given class. The normalization process may optimize or improve the
accuracy of the classification by accounting for the probability of
different kinds of errors occurring in the classification. As a
result, when the normalization process is combined with the
temporal filtering operation--which uses data from multiple
consecutive classifications--the overall classification accuracy of
the system may be meaningfully improved without a significant
contribution to the overall latency of the classification
pipeline.
[0024] As mentioned, the temporal filter may be a linear filter, a
nonlinear filter, an adaptive filter, and/or a statistical filter.
Where an adaptive filter is implemented, the adaptive filter may
use a preliminary signal analysis to change features of the
function used within the temporal filter. The preliminary signal
analysis may be, in embodiments, executed over a smaller output
window than is used by the temporal filter. For a non-limiting
example, the preliminary signal analysis may be over five
consecutive outputs, whereas a default window for the temporal
filter may be twenty or more consecutive outputs. In some
embodiments, the preliminary signal analysis may detect a
presumptive class change in the classifier output, for example, as
evidenced by the highest confidence score in the raw output
transitioning from association with a first class to a second
class. This may indicate a classification shift from the first
class to the second class.
[0025] A class shift may indicate that older output data may be
less relevant than the newer output data. This information may be
taken into account by the adaptive filter by giving more weight to
recent classification outputs when the preliminary signal analysis
detects a class shift. The change to the weighting values may be
applied to all classes or to just affected classes. For example, a
presumptive class shift between the first class and the second
class may cause the adaptive filter to adjust a decay function
within the adaptive filter to give less weight to older outputs
being considered by the filter that correspond to the first class,
while leaving the default weights in place for the other
classes.
[0026] Aspects of the technology described herein may work with a
variety of different multiclass classifiers, but will most often be
described herein in the context of convolutional neural networks
(CNNs). In some aspects, the multiclass classifier described herein
may not consider classifications assigned to preceding input
signals when generating a classification for a subsequent signal in
a temporal sequence. The technology described herein can serve as
an alternative to a recurrent neural network (RNN), such as Long
Short Term Memory (LSTM) networks, and other classifiers that
already consider preceding classification data when calculating a
subsequent classification. The use of the temporal filter on the
output of a CNN consumes less computer resources and contributes
less to latency than using an RNN--thereby decreasing runtime of
the system--while achieving performance improvements. The temporal
filter also allows for application specific classification tuning
that is not possible with an RNN. For example, different filter
parameters may be used on different class confidence scores where
avoiding a false positive for some classes is more important than
for other classes.
[0027] With reference to FIG. 1, FIG. 1 shows a real-time signal
classification system 100, in accordance with some embodiments of
the present disclosure. It should be understood that this and other
arrangements described herein are set forth only as examples. Other
arrangements and elements (e.g., machines, interfaces, functions,
orders, groupings of functions, etc.) may be used in addition to or
instead of those shown, and some elements may be omitted
altogether. Further, many of the elements described herein are
functional entities that may be implemented as discrete or
distributed components or in conjunction with other components, and
in any suitable combination and location. Various functions
described herein as being performed by entities may be carried out
by hardware, firmware, and/or software. For instance, various
functions may be carried out by a processor executing instructions
stored in memory. In some embodiments, components, features, and/or
functionality of the system 100 may be similar to that of vehicle
800 of FIGS. 8A-8D and/or example computing device 900 of FIG.
9.
[0028] At a high level, the real-time signal classification system
100 may assign a classification to an input signal in a temporal
series. The sensors 102 may capture a temporal sequence of input
signals--such as input signals representative of video frame--and
the preprocessor 106 may prepare the input signals for the
classifier 108. The classifier 108 may be a multiclass classifier
that uses one or more CNNs--or other deep neural network (DNN)
and/or machine learning models--to process the inputs. In some
embodiments, the classifier 108 may generate two different
confidence score distributions where one of the distributions is
generated by a softmax function 114 and the other distribution is
generated by an angular visual hardness function 112. The
classification merge component 122 may then combine these two
distributions into a single raw distribution used by subsequent
components in the system, such as the class normalization component
124 and/or the classification change detector 128.
[0029] The class normalization component 124 may normalize the raw
distribution using data from the confusion matrix 126. The
confusion matrix 126 may include values representative of a
probability that a given input assigned into a first class, for
example, should actually be assigned to a different class. The
normalization process can raise or lower a raw confidence score for
a class based on the confusion probabilities with other classes.
The normalized confidence score distribution can be sent to the
temporal filter 130 for use in making a final classification. The
classification change detector 128 may determine when a class
change has occurred within the temporal sequence of input signals
and this change detection may be used to tune the temporal filter
130 in real time to make a more accurate classification, especially
around class transitions.
[0030] The system 100 may include sensors 102 that may generate
dimensional data (e.g., one-dimensional (1D), 2D, 3D, etc.). For
example, one or more sensors 102 may generate data in a first
dimensional space, such as 2D, and one or more sensors 102 may
generate data in a second dimensional space, such as 3D. The sensor
data may include, without limitation, sensor data from any of the
sensors 102 of the vehicle 800 (and/or other vehicles or objects,
such as robotic devices, VR systems, AR systems, etc., in some
examples). For example, and with reference to FIGS. 8A-8C, the
sensor data may include the data generated by, without limitation,
RADAR sensor(s) 860, ultrasonic sensor(s) 862, LIDAR sensor(s) 864,
stereo camera(s) 868, wide-view camera(s) 870 (e.g., fisheye
cameras), infrared camera(s) 872, surround camera(s) 874 (e.g., 360
degree cameras), long-range and/or mid-range camera(s) 898, and/or
other sensor types. For example, although reference is primarily
made to the sensors 102 including cameras and depth sensors (e.g.,
LIDAR sensors 864, RADAR sensors 860, etc.), this is not intended
to be limiting, and the sensors 102 may alternatively or
additionally be generated by any of the sensors of the vehicle 800,
another vehicle, an object, a machine (e.g., a robot), and/or
another system (e.g., a virtual vehicle in a simulated environment,
a traffic system, a surveillance system, etc.).
[0031] In some examples, the sensor data may be generated by one or
more forward-facing sensors, side-view sensors, interior sensors,
and/or rear-view sensors of the vehicle 800 and/or other machine
type. This sensor data may be useful for identifying, detecting,
classifying, and/or tracking movement of objects around the vehicle
800 and/or other machines within the environment. In embodiments,
any number of sensors 102 may be used to incorporate multiple
fields of view (e.g., the fields of view of the long-range cameras
898, and/or the forward-facing stereo camera 868, and/or the
forward facing wide-view camera 870 of FIG. 8B). In some
embodiments, such as described herein, signals--e.g., representing
image data--generated by a camera(s) interior to the vehicle 800
designed to capture gestures made by a driver, passenger, or other
person in the vehicle 800 may be processed by one or more DNNs. The
classification of these signals by the DNNs into a gesture
class(es) may be used to control various components in the vehicle
800, such as a comfort system entertainment system, navigation
system, and/or the like.
[0032] As such, the inputs to the classifier 108 may include image
data representing an image(s) and/or image data representing a
video (e.g., snapshots of video), and/or may represent sensor data
generated by a sensor depicting a sensory field of the sensor.
Where the sensor data includes image data, any type of image data
format may be used, such as, for example and without limitation,
compressed images such as in Joint Photographic Experts Group
(JPEG) or Luminance/Chrominance (YUV) formats, compressed images as
frames stemming from a compressed video format such as
H.264/Advanced Video Coding (AVC) or H.265/High Efficiency Video
Coding (HEVC), raw images such as originating from Red Clear Blue
(RCCB), Red Clear (RCCC), or other type of imaging sensor, and/or
other formats. In addition, in some examples, the sensor data may
be used by the system 100 without any pre-processing (e.g., in a
raw or captured format), while in other examples, the sensor data
may undergo pre-processing by the sensor data preprocessor 106.
[0033] The sensor data preprocessor 106 may perform various
operations on the sensor data to generate preprocessed sensor data.
Non-limiting examples of preprocessing operations include noise
balancing, demosaicing, scaling, cropping, augmentation, white
balancing, tone curve adjustment, and the like. As used herein, the
sensor data applied to the classifier 108 may reference unprocessed
sensor data, pre-processed sensor data, or a combination
thereof.
[0034] Referring again to FIG. 1, the outputs of the preprocessor
106 may be applied to the classifier 108. The classifier 108 may
generate raw classification outputs using the sensor data as input.
In some embodiments, the classifier 108 may generate a temporal
series of raw classification outputs for a temporal sequence of
sensor data, such as a series of images. As an example, a temporal
series may be a series of data points arranged in time order and,
in embodiments, the temporal series of inputs may be a sequence of
data captured by the sensors 102 at successive equally-spaced
points in time (e.g., similar to that of a video feed). A temporal
series of classification distributions may comprise an individual
distribution for each input signal.
[0035] The classifier 108 may include a CNN (and/or another type of
DNN or machine learning model), an angular visual hardness function
112, and/or a softmax function 114. Where a CNN is implemented, the
CNN can take different forms depending on implementation
preferences. For example, the CNN can have different types and
combinations of layers (e.g., input layers, convolutional layers,
pooling layers, ReLU layers, deconvolutional layers, and fully
connected layers). In different embodiments, layers (e.g.,
convolutional layers) can have different dimensions that may be
selected based on dimensions of an input signal. While described as
a CNN herein, embodiments may use other machine learning models, as
described subsequently.
[0036] The softmax function 114 may generate a confidence score
distribution from data generated by the CNN 110. The softmax
function 114 may correspond to an activation function that turns
numbers (e.g., logits) into probabilities that sum to one. The
softmax function 114 may output a vector that represents the
probability distributions of a list of potential outcomes. This
probability distribution may be described as a confidence score
distribution herein. The softmax function 114 may turn logits
(numeric output of the last linear layer of a multi-class
classification CNN 110) into probabilities by taking the exponents
of each output and then normalizing each number by the sum of those
exponents so the entire output vector adds up to one--e.g., all
probabilities should add up to one.
[0037] The angular visual hardness (AVH) function 112 may also
generate a confidence score distribution from data generated by the
CNN 110. AVH may be computed using the weight vector and the
feature map in the last layer of the CNN 110. AVH may focus on the
angle between these vectors to generate a confidence score, and the
AVH function 112 may generate a confidence score distribution that
assigns a probability to each class the AVH function 112 (in
combination with CNN 110) is trained to recognize.
[0038] If the system 100 is for object detection and classification
by the vehicle 800, the classes may include, without limitation,
vehicles, pedestrians, and animals, or may include more granular
classes such as SUVs, sedans, busses, bicyclists, adults, children,
dogs, cats, horses, etc. Where the system 100 is for object
detection and classification by a robot, the classes may include,
without limitation, pedestrians, other robots, vehicles, etc. Where
the system 100 is for object detection and classification by an
aircraft or drone, the classes may include aircraft, drones, birds,
buildings, vehicles, pedestrians, etc. As such, depending on the
implementation of the system 100, the classes that the CNN 110 (and
softmax function 114 and AVH function 112) is trained to predict
may vary.
[0039] As described previously, the softmax function 114 and the
AVH function 112 may generate confidence score distributions, as
illustrated in FIG. 2. FIG. 2 is an illustration of an example
softmax output 210 and an example angular visual hardness output
220, in accordance with some embodiments of the present disclosure.
The softmax output 210 may be generated by the softmax function
114, and the softmax output 210 may include a confidence score for
each class the multiclass classifier is trained to identify. In
this example, the classes include class A, class B, class C, class
D, class B, class F, class G, class H, class I, and class J. Class
J is assigned the highest score of 0.88, while class A receives the
next highest score at 0.07. A score is also assigned to the other
classes with the sum of all assigned confidence scores equaling 1.
The softmax output 210 may be generated for each image processed in
a temporal series of images.
[0040] The angular visual hardness output 220 may also include a
score for each class, and may be generated by the angular visual
hardness function 112. Class J is assigned the highest score of
0.92, while class A receives the next highest score at 0.05. This
illustrates that the AVH output 220 and the softmax output 210 may
differ. A score is also assigned to the other classes with the sum
of all assigned confidence scores equaling 1. The angular visual
hardness output 220 may be generated for each image processed of a
temporal series of images.
[0041] The classification merge component 122 may accept the
angular visual hardness output 220 and the softmax output 210 as
input and generate a single confidence score distribution for a
single image input into the CNN 110. In some embodiments, the
outputs are merged by averaging the two outputs (e.g., with equal
weighting). In another embodiment, the highest output assigned to a
class in either output is accepted and the lower value dropped. In
further embodiments, the lowest output assigned to a class is
accepted and the higher value is dropped. In another embodiment,
more weight is given to one output than the other when the
classification merge component 122 generates the raw confidence
score distribution for an image. For example, the angular visual
hardness output 220 may be given 70% weight in calculating the
final combined confidence score distribution.
[0042] In one embodiment, a comparison is made between one or more
class confidence scores in the two outputs. For example, a
comparison of the class with the highest confidence score in each
output may be made and, if the class comparison does not agree
(e.g., if the softmax output 210 associates a first class with the
highest score and the angular visual hardness output 220 associates
a second class with the highest score), then the two outputs may
not be combined and only one of the outputs may be used, while the
other is dropped. In another embodiment, when the difference
between the highest class confidence score in the softmax output
210 and the highest class confidence score in the angular visual
hardness output 220 exceeds a difference threshold, then the higher
of the two scores may be used without averaging or otherwise
combining the two outputs. For example, if the softmax class A
confidence score is 0.91 and the angular visual hardness confidence
score for class A is 0.73, then only the softmax confidence score
would be used if the difference threshold was 0.15. Otherwise, if
the two scores are within the threshold, then the two scores are
averaged or otherwise combined. The combined confidence score
distribution generated by the classification merge component 122
may be communicated to both the class normalization component 124
and the classification change detector 128. The combined
distribution may be described as the raw confidence score
distribution or simply the raw distribution.
[0043] The classification merge component 122 may correspond to the
first component within the class assignment engine 120. The class
assignment engine 120 may include two parallel processes that may
be combined at the temporal filter 130 to generate a final class
assignment for a given input signal. One of the two parallel
processes may include a class normalization operation performed by
a class normalization component 124. Once generated, the normalized
confidence score distribution may then be communicated to the
temporal filter 130 for further processing. The second parallel
process is a classification change detection performed by the
classification change detector 128. Classification changes may be
communicated to the temporal filter 130 and used to tune the filter
in response the detected changes. The change detection process is
described with reference to FIG. 3. The normalization process is
described with reference to FIG. 4. While these processes may be
used together in some embodiments, the two processes may be used
without the other in some embodiments. Thus, the normalization
process may work without change detection and the change detection
may work without normalization.
[0044] FIG. 3 illustrates a classification change in response to a
signal change. The classifications of a temporal series of images
shown in FIG. 3 may be generated by a classifier, such as the
classifier 108 described herein. Example images from a temporal
series of images are shown in FIG. 3. The example images may be
captured by a gesture control system within a vehicle, such as
vehicle 800. The gesture control systems may control car functions
in response to gestures made by a user as captured in video of a
gesture performance area (e.g., a cabin of the vehicle 800, or a
portion thereof). When no gesture is being made, the images
captured should be classified as capturing no control gesture. When
the user makes a gesture within the gesture performance area, the
gesture control system may assign a classification to the captured
image and perform a corresponding function (e.g., increase the
volume). A user may make a single gesture or a series of gestures.
Making a single gesture may cause the classification system to
transition between a no gesture classification and a classification
of the gesture made. Making multiple gestures in series may cause
the classification system to transition between different gestures.
As such, in some examples, there may be a transition for the
classification system to handle.
[0045] Transitions may cause uncertainty in classification systems
that analyze a series of consecutive input signals to generate an
output. As an example, a classification for a current point in time
may be generated using the last 20 images captured in a temporal
sequence. After a transition occurs, some of the images used to
generate an output may capture an earlier gesture, while another
portion of the images capture a current gesture, and a third
portion may capture a user transitioning between gestures, which is
not a gesture at all. The images and corresponding classifications
shown in FIG. 3 illustrate this transitional challenge.
[0046] The example images from the temporal series include a first
finger-pointing image 310 and a second finger-pointing image 312.
The example images also include a first v-finger image 314 and a
second v-finger image 316. The first finger-pointing image 310
captures a user pointing an index finger forward. The second
finger-pointing image 312 also captures a user pointing an index
finger forward, but in a position that is slightly different from
the position captured by the first finger-pointing image 310. This
difference illustrates the challenge a classifier faces in
classifying an image content. Both images should receive the same
classification despite the differences between the images.
[0047] The first v-finger image 314 captures a user pointing two
fingers forward forming a V shape. The second v-finger image 316
also captures a user pointing two fingers forward forming a V
shape, but in a position that is slightly different from the
position captured by the first v-finger image 314. Posing fingers
in a V shape is a different gesture than pointing the index finger
forward and should be classified into a different class.
[0048] The class A graph 320 shows a classification distribution
assigned to class A over the temporal series of images and the
class B graph 330 shows a classification distribution assigned to
class B over the temporal series of images. In this illustration,
class A corresponds to the finger-pointing gesture and class B
corresponds to the V gesture. As can be seen, the confidence score
that the classifier assigned to class A ranges between one and 0.9
when images 310 and 312 are processed. The confidence score drops
sharply at transitional entry 322 until it continues fluctuating
below 0.1 after transitional exit 324. The class B graph 330 shows
the other side of the transition into class B, where the confidence
increases sharply at transitional entry 232 until it continues
fluctuating above 0.9 after transitional exit 334.
[0049] During a transition represented by entry points 322 and 332
and transitional exits 324 and 334, the classifier may be analyzing
images showing content in two or three different classifications
(e.g., class A, class B, and no class). A goal of the technology
described herein is to detect these transitions and adapt a
temporal filter in real time to more accurately classify signals
received during and after a transition. This improvement may be
achieved, in part, using the classification change detector 128 in
combination with the temporal filter 130.
[0050] The classification change detector 128 may analyze the
temporal series of raw classification distributions to detect a
class change. The classification change detector 128 may analyze a
smaller window of distributions than the temporal filter 130. For
example, the classification change detector 128 may look for a
change by analyzing six consecutive distributions, while the
temporal filter 130 may generate a final classification looking at
20 consecutive distributions. These numbers are simply used for the
sake of example and are not intended to be limiting. The
classification change detector 128 may detect a change by looking
at the class assigned the highest confidence score within a
distribution. When the class assigned the highest confidence score
changes over a threshold number of consecutive distributions, then
generation of a change notice may be triggered. The threshold
number may be selected to avoid triggering a change notice upon
detecting a change in just two consecutive distributions, which may
occur from time to time in response to processing a noisy signal. A
different threshold number may be selected for different
implementations depending on perceived classification jitter (e.g.,
occurrence of false class transitions between consecutive
distributions).
[0051] In some embodiments, a different threshold number may be
used for different class transitions. For example, the confusion
matrix 126 may show significant class confusion between class A and
class B. When two classes have a comparatively high amount of class
confusion, then a larger threshold number can be used, and when two
classes have a comparatively low amount of class confusion, then a
lower threshold number can be used. In an embodiment, the
classification change detector 128 may detect a presumptive class
change between two consecutive class distributions and then
determine the threshold to be used based on the two classes
involved in the change. Once the class-specific threshold is hit,
the change notification is generated.
[0052] Among other information, the change notification can
identify the two or more classes involved in the change. The change
notification can also identify an input signal that corresponds to
the transition entry and an input signal that corresponds to the
transition exit. In some embodiments, the transition entry and exit
can be used to tune the temporal filter 130, for example by
adjusting an analysis window to exclude class distributions
calculated before a transition entry and/or before a transition
exit. Once generated, the change notification may be communicated
to the temporal filter 130.
[0053] As mentioned, the class normalization component 124 may
generate a normalized class distribution that adjusts individual
confidence scores within the distribution according to a likelihood
of confusion between different classes. The likelihood of confusion
is illustrated by the confusion matrix 126 shown in FIG. 4.
[0054] FIG. 4 shows a confusion matrix 400 for a multiclass
classifier trained to assign an input to one of five different
classes. The class confusion may be determined by analyzing the
performance of the trained classifier using ground truth data. The
ground truth data may be determined, as a non-limiting example, by
having a user assign a ground truth label to the signal input used
to test the classifier performance. In some embodiments, the class
confusion analysis may be an off-line process that results in a
class confusion matrix or other memorialization of the confusion
analysis. However, in other embodiments, the class confusion
analysis may be on an on-line process, a process that occurs at
initialization of the system, and/or at another time.
[0055] Data from the confusion matrix 400 may be used in a
normalization process. For example, because class confusion may
assign a probability of occurrence to certain types of
classification failures, then, for a given class, the confusion
matrix may include data indicating a probability that an input
signal with a ground truth classification in the given class is a
true positive or a false positive classification. A true positive
may indicate that the input signal was correctly classified into
the given class and a false positive may indicate that an input
signal was incorrectly classified into a different class. Each
different class may receive its own probability of receiving a
false positive classification for the given class.
[0056] Each class is assigned both a row and a column in the matrix
400. In this example, the multiclass classifier is trained to
identify hand gestures. The figure point gesture is assigned to
column 410 and row 420, the finger V gesture is assigned to column
412 and row 422, the flat hand gesture is assigned to column 414
and row 424, the no gesture is assigned to column 416 and row 426,
and the thumbs-up gesture is assigned to column 418 and row
428.
[0057] The class confusion can be identified by looking at the
intersection of different rows and columns. The lighter the square
shading the higher the confusion. Each square is associated with a
probability (not shown). Taking the finger point gesture as an
example, the probability assigned to the intersection of column 410
and row 420 may be 94%. This box is the intersection of the finger
point gesture and the finger point gesture and represents the
baseline probability that a finger point gesture will be correctly
identified by the classifier (e.g., true positive). The probability
assigned to the intersection of column 412 and row 420 may be 1%,
which may indicate there is a 1% probability that the finger point
gesture will be incorrectly classified as a finger V gesture. The
probability assigned to the intersection of column 414 and row 420
may be 0%, which may indicate that the multiclass classifier does
not incorrectly assign finger point gestures as flat hand gestures.
The probability assigned to the intersection of column 416 and row
420 may be 5%, which may indicate there is a 5% probability that
the finger point gesture will be incorrectly classified as no
gesture. Similarly, the probability assigned to the intersection of
column 418 and row 420 may be 1%, which may indicate there is a 1%
probability that the finger point gesture will be incorrectly
classified as no gesture.
[0058] The probability that the finger point gesture class is
incorrectly assigned to a different class is recorded in column
410. The probability assigned to the intersection of column 410 and
row 422 may be 0.13%, which may indicate there is a 0.13%
probability that the finger V gesture will be incorrectly
classified as a finger point gesture. The probability assigned to
the intersection of column 410 and row 424 may be 0.16%, which may
indicate there is a 0.16% probability that the flat hand gesture
will be incorrectly classified as a finger point gesture. The
probability assigned to the intersection of column 410 and row 426
may be 13%, which may indicate there is a 13% probability that the
flat no gesture will be incorrectly classified as a finger point
gesture. The probability assigned to the intersection of column 410
and row 428 may be 17.5%, which may indicate there is a 17.5%
probability that the thumbs up gesture will be incorrectly
classified as a finger point gesture. All other boxes in the matrix
400 may also be associated with values.
[0059] The class normalization component 124 uses the confusion
matrix 126 to generate a normalized class distribution. The
normalization process may adjust the raw classification confidence
scores according to data from the confusion matrix 400. In general,
the confidence score assigned to a first class (e.g., class A) may
be lowered in proportion to the probability that the first class is
a false positive of the other classes (e.g., class B, class C,
class D). Conversely, the confidence score for a given class may be
increased in proportion to the probabilities that other classes are
false positives for the given class. The normalization process may
optimize or improve the accuracy of the classification by
accounting for the probability of different kinds of errors
occurring in the classification. As a result, when the
normalization process is combined with the temporal filtering
operation--which uses data from multiple consecutive
classifications--the overall classification accuracy of the system
may be meaningfully improved without a significant contribution to
the overall latency of the classification pipeline.
[0060] Using the example values described above for the finger
point gesture, the normalization of a raw confidence score within a
distribution may be illustrated. Assume, as an example, that a raw
confidence of 0.9 is assigned to the finger point gesture in the
raw distribution. The raw confidence value may be adjusted upward
based on the probabilities that a finger point gesture would be
assigned to a different class. This probability can be determined
by adding the values in the boxes of row 420, excluding the value
in the box representing the intersection of row 410 and 420, which
is a true positive. As described above, these other values total to
7%. The raw confidence value may be adjusted downward based on the
probabilities that a different gesture would be incorrectly
assigned as a finger point gesture. This probability can be
determined by adding the values in the boxes of column 410,
excluding the value in the box representing the intersection of row
410 and 420, which is a true positive. These other values total
31%. Taken together, the raw confidence score may be decreased by
24% (+7-31) to 0.684. Other methods of calculating the normalized
confidence score may be used in embodiments of the present
disclosure. The overall goal may be to increase the raw confidence
score in proportion to the probability a true first class input is
classified incorrectly into a different class and to reduce the
confidence score in proportion to the probability that a
first-class classification is assigned when the true class is other
than the first class. Here the first class is just used as an
example class. A similar adjustment can be determined for each
class.
[0061] The temporal filter 130 may assign a final class
distribution using a series of the normalized class distributions
as input. The class assignment engine 120 may use the final
distribution to select the final class, which may be the class
assigned the highest confidence score in the final distribution. In
general, the temporal filter 130 may use a temporal series of class
distributions to generate a single class distribution
representative of a classification of a single input. In this way,
past class distributions contribute to calculating the current
class distribution.
[0062] Depending on the embodiment, the temporal filter may take
many different forms. For example, the temporal filter may be a
linear filter, a nonlinear filter, an adaptive filter, and/or a
statistical filter. In each example, the overall operation of the
filter may be similar. For example, the filter may receive a
temporal sequence of outputs from the multiclass classifier
108--e.g., x number of consecutive outputs generated by classifying
x number of consecutive input signals. In embodiments, the number
of outputs received may be described as an analysis window. As the
outputs are received, the outputs may be filtered together and a
final confidence score for each class in each instance of the
output data may be generated using the temporal filter.
[0063] Each individual output in the sequence may include a series
of confidence scores for each class the multiclass classifier is
trained to identify. For example, a classifier trained to assign
one of five different classes to an input signal would output a
confidence score for each of the five classes. As described herein,
the temporal filter may receive, as input, a sequence of outputs of
the multiclass classifier and generate a final confidence factor
for each class. The final confidence factor may correspond to the
final output of the process and effectively replace the newest raw
output within the sequence of outputs input to the temporal filter.
The final output may then be used to assign an active
classification to the corresponding input signal, and this process
may repeat as new outputs are received from the classifier--with
the oldest output dropping out of the sequence and the newest one
being added (e.g., as a rolling buffer of output signals).
[0064] As mentioned, the temporal filter may be a linear filter, a
nonlinear filter, an adaptive filter, and/or a statistical filter.
Linear filters process time-varying input signals to produce output
signals, subject to the constraint of linearity (i.e., the results
can be graphed to form a line). The nonlinear filter may be an
exponential filter that smooths time series data using an
exponential window function. Whereas a simple moving average of the
past observations are weighted equally (e.g., statistical filter),
exponential functions are used to assign exponentially decreasing
weights over time. The statistical filter can calculate a moving
average or some other statistical measure over a window of
observations.
[0065] When an adaptive filter is implemented, the adaptive filter
may use a change notification generated by the classification
change detector 128. The preliminary signal analysis may be, in
embodiments, executed over a smaller output window than is used by
the temporal filter. For a non-limiting example, the preliminary
signal analysis may be over five consecutive outputs, whereas a
default window for the temporal filter may be twenty or more
consecutive outputs. In some embodiments, the preliminary signal
analysis may detect a presumptive class change in the classifier
output, for example, as evidenced by the highest confidence score
in the raw output transitioning from association with a first class
to a second class. This may indicate a classification shift from
the first class to the second class.
[0066] When the temporal filter 130 responds to a change
notification generated by the classification change detector 128,
the temporal filter may be described as an adaptive filter. The
adaptive filter may be a linear filter, a nonlinear filter, and/or
a statistical filter. The adaptive filter may adapt differently
depending on the underlying filter being implemented. For example,
an adaptive filter may adjust the window size for a statistical
filter or a linear filter. The window size may be temporarily
decreased in response to a change notification. The goal of the
decreased window size may be to emphasize more recent scores in the
calculation. Decreasing the window size has the effect of omitting
order calculations from the temporal series as the older
calculations are more likely to represent pre-transitional
observations that will tend to make the final determination less
accurate. Omitting these observations improves the accuracy of the
final classification result. For example, decreasing the window
size from 20 observations to 10 would cause the 10 oldest
observations to be omitted from the calculation of the class
distribution.
[0067] The original window size may be restored to its original
size upon processing a threshold number of observations (e.g.,
normalized classification distributions). In one embodiment, the
threshold number is equal to or greater than the window size
decrease. For example, if the window size is decreased by 10
observations then the window size could be increased to the
original size after processing 10 consecutive input signals within
a temporal sequence at the decreased window size. In one
embodiment, the increase is a step increase. For example, the
window may increase in size by an increment of one with each
additional observation processed until the original size is
reached.
[0068] In the case of an exponential filter (e.g., nonlinear), the
adaptive filter can maintain the same window size, but effectively
deemphasize older observations by increasing a decay rate within
the exponential filter. The increased decay rate gives less weight
to older observations and more weight to newer observations.
[0069] Now referring to FIGS. 5-7, each block of methods 500-700,
described herein, comprises a computing process that may be
performed using any combination of hardware, firmware, and/or
software. For instance, various functions may be carried out by a
processor executing instructions stored in memory. The methods may
also be embodied as computer-usable instructions stored on computer
storage media. The methods may be provided by a standalone
application, a service or hosted service (standalone or in
combination with another hosted service), or a plug-in to another
product, to name a few. In addition, methods 500-700 are described,
by way of example, with respect to the real-time signal
classification system 100 of FIG. 1. However, these methods may
additionally or alternatively be executed by any one system, or any
combination of systems, including, but not limited to, those
described herein.
[0070] With reference to FIG. 5, FIG. 5 is a flow diagram showing a
method 500 for assigning a classification to an input signal, in
accordance with some embodiments of the present disclosure. The
method 500, at block 502, includes receiving, based at least in
part on a multiclass classifier processing the input signal, a raw
classification output representative of a first raw confidence
score, the first raw confidence score corresponding to a first
class. The method 500, at block 504, includes computing a first
normalization amount corresponding to the first class by using a
confusion factor between the first class and a second class, the
confusion factor representative of a probability that the
multiclass classifier will compute, for a generic input signal
known to correspond to the second class, an output indicating that
the generic input signal corresponds to the first class. The method
500, at block 506, includes generating a first normalized
confidence score corresponding to the first class by adjusting the
first raw confidence score according to the first normalization
amount. The method 500, at block 508, includes applying a temporal
filter to the first normalized confidence score to generate a final
confidence score corresponding to the first class. The method 500,
at block 510, includes determining a final classification for the
input signal based at least in part on the final confidence score
corresponding to the first class.
[0071] Now referring to FIG. 6, FIG. 6 is a flow diagram showing a
method 600 for assigning a classification to an input signal, in
accordance with some embodiments of the present disclosure. The
method 600, at block 602, includes receiving a temporal series of
raw classification outputs that a multiclass classifier generated
by processing a temporal sequence of input signals, each raw
classification output including a class confidence score for each
of a plurality of classes the multiclass classifier is trained to
identify. The method 600, at block 604, includes detecting a
classification state change within a first set of the raw
classification outputs indicating a probable classification change
from a first class to a second class. The method 600, at block 606,
includes tuning, based at least in part on the classification state
change, an adaptive filter to decrease weight given to older
confidence scores corresponding to the first class within a
temporal sequence of confidence scores corresponding to the first
class when calculating a final confidence score corresponding to
the first class. The method 600, at block 610, includes applying
the adaptive filter to the temporal sequence of confidence scores
corresponding to the first class in a second set of classification
outputs to generate the final confidence score corresponding to the
first class. The method 600, at block 612, includes generating a
final classification for the input signal using the final
confidence score.
[0072] With reference to FIG. 7, FIG. 7 is a flow diagram showing a
method 700 for assigning a classification to an input signal, in
accordance with some embodiments of the present disclosure. The
method 700, at block 702, includes receiving a temporal series of
raw classification outputs that a multiclass classifier generated
by processing a temporal sequence of input signals, each raw
classification output including a class confidence score for each
of a plurality of classes the multiclass classifier is trained to
identify. The method 700, at block 704, includes detecting a
classification state change within a first set of the raw
classification outputs indicating a probable classification change
from first class to a second class. The method 700, at block 706,
includes tuning, based at least in part on the classification state
change, an adaptive filter to decrease weight given to older
confidence scores corresponding to the first class within a
temporal sequence of confidence scores corresponding to the first
class when calculating a final confidence score corresponding to
the first class. The method 700, at block 708, includes computing a
first normalization amount corresponding to the first class using a
confusion factor between the first class and the second class, the
confusion factor representative of a probability that the
multiclass classifier will compute, for a generic input signal
known to correspond to the second class, an output indicating that
the generic input signal corresponds to the first class. The method
700, at block 710, includes generating a first normalized
confidence score corresponding to the first class by adjusting a
first raw confidence score corresponding to the first class
according to the first normalization amount. The method 700, at
block 712, includes applying the adaptive filter to a second set of
classification outputs that includes the first normalized
confidence score to generate the final confidence score
corresponding to the first class.
[0073] The method 700, at block 714, includes generating a final
classification for the input signal using the final confidence
score corresponding to the first class as input.
[0074] Example Machine Learning Models
[0075] Although examples are described herein with respect to using
DNNs, and specifically CNNs, as for example CNN 110, this is not
intended to be limiting. For example, and without limitation, the
DNNs described herein may include any type of machine learning
model, such as a machine learning model(s) using linear regression,
logistic regression, decision trees, support vector machines (SVM),
Naive Bayes, k-nearest neighbor (Knn), K means clustering, random
forest, dimensionality reduction algorithms, gradient boosting
algorithms, neural networks (e.g., auto-encoders, convolutional,
recurrent, perceptrons, Long/Short Term Memory (LSTM), Hopfield,
Boltzmann, deep belief, deconvolutional, generative adversarial,
liquid state machine, etc.), and/or other types of machine learning
models.
[0076] In addition, in some embodiments, the DNNs described herein
may include a convolutional layer structure, including layers such
as those described herein. For example, the DNNs may include a full
architecture formulated for the task of generating various
outputs--such as classification confidences. Where a CNN is
implemented, one or more of the layers may include an input layer.
The input layer may hold values associated with the input (e.g.,
vectors, tensors, etc. corresponding to sensor data, voxelized
sensor data, feature vectors, etc.). For example, when the sensor
data is an image(s), the input layer may hold values representative
of the raw pixel values of the image(s) as a volume (e.g., a width,
W, a height, H, and color channels, C (e.g., RGB), such as
32.times.32.times.3), and/or a batch size, B (e.g., where batching
is used)
[0077] One or more layers of the DNNs may include 2D and/or 3D
convolutional layers. The convolutional layers may compute the
output of neurons that are connected to local regions in an input
layer (e.g., the input layer), each neuron computing a dot product
between their weights and a small region they are connected to in
the input volume. A result of a convolutional layer may be another
volume, with one of the dimensions based on the number of filters
applied (e.g., the width, the height, and the number of filters,
such as 32.times.32.times.12, if 12 were the number of
filters).
[0078] One or more of the layers may include a rectified linear
unit (ReLU) layer. The ReLU layer(s) may apply an elementwise
activation function, such as the max (0, x), thresholding at zero,
for example. The resulting volume of a ReLU layer may be the same
as the volume of the input of the ReLU layer.
[0079] One or more of the layers may include a pooling layer. The
pooling layer may perform a down-sampling operation along the
spatial dimensions (e.g., the height and the width), which may
result in a smaller volume than the input of the pooling layer
(e.g., 16.times.16.times.12 from the 32.times.32.times.12 input
volume). In some examples, the DNNs may not include any pooling
layers. In such examples, strided convolution layers may be used in
place of pooling layers. In some examples, the feature extractor
layer(s) may include alternating convolutional layers and pooling
layers.
[0080] One or more of the layers may include a fully connected
layer. Each neuron in the fully connected layer(s) may be connected
to each of the neurons in the previous volume. The fully connected
layer may compute class scores, and the resulting volume may be
1.times.1 x number of classes. In some example, no fully connected
layers may be used by the DNNs as a whole, in an effort to increase
processing times and reduce computing resource requirements. In
such examples, where no fully connected layers are used, the DNNs
may be referred to as a fully convolutional network.
[0081] One or more of the layers may, in some examples, include
deconvolutional layer(s). However, the use of the term
deconvolutional may be misleading and is not intended to be
limiting. For example, the deconvolutional layer(s) may
alternatively be referred to as transposed convolutional layers or
fractionally strided convolutional layers. The deconvolutional
layer(s) may be used to perform up-sampling on the output of a
prior layer. For example, the deconvolutional layer(s) may be used
to up-sample to a spatial resolution that is equal to the spatial
resolution of the input vector or tensor of the DNN, or used to
up-sample to the input spatial resolution of a next layer.
[0082] Although input layers, convolutional layers, pooling layers,
ReLU layers, deconvolutional layers, and fully connected layers are
discussed herein with respect to the DNN, this is not intended to
be limiting. For example, additional or alternative layers may be
used, such as normalization layers, SoftMax layers, and/or other
layer types.
[0083] Different orders and numbers of the layers of the DNNs may
be used depending on the embodiment. In addition, some of the
layers may include parameters (e.g., weights and/or biases), while
others may not, such as the ReLU layers and pooling layers, for
example. In some examples, the parameters may be learned by the
DNNs during training. Further, some of the layers may include
additional hyper-parameters (e.g., learning rate, stride, epochs,
kernel size, number of filters, type of pooling for pooling layers,
etc.)--such as the convolutional layer(s), the deconvolutional
layer(s), and the pooling layer(s)--while other layers may not,
such as the ReLU layer(s). Various activation functions may be
used, including but not limited to, ReLU, leaky ReLU, sigmoid,
hyperbolic tangent (tan h), exponential linear unit (ELU), etc. The
parameters, hyper-parameters, and/or activation functions are not
to be limited and may differ depending on the embodiment.
[0084] Example Autonomous Vehicle
[0085] FIG. 8A is an illustration of an example autonomous vehicle
800, in accordance with some embodiments of the present disclosure.
The autonomous vehicle 800 (alternatively referred to herein as the
"vehicle 800") may include, without limitation, a passenger
vehicle, such as a car, a truck, a bus, a first responder vehicle,
a shuttle, an electric or motorized bicycle, a motorcycle, a fire
truck, a police vehicle, an ambulance, a boat, a construction
vehicle, an underwater craft, a drone, and/or another type of
vehicle (e.g., that is unmanned and/or that accommodates one or
more passengers). Autonomous vehicles are generally described in
terms of automation levels, defined by the National Highway Traffic
Safety Administration (NHTSA), a division of the US Department of
Transportation, and the Society of Automotive Engineers (SAE)
"Taxonomy and Definitions for Terms Related to Driving Automation
Systems for On-Road Motor Vehicles" (Standard No. J3016-201806,
published on Jun. 15, 2018, Standard No. J3016-201609, published on
Sep. 30, 2016, and previous and future versions of this standard).
The vehicle 800 may be capable of functionality in accordance with
one or more of Level 3-Level 5 of the autonomous driving levels.
For example, the vehicle 800 may be capable of conditional
automation (Level 3), high automation (Level 4), and/or full
automation (Level 5), depending on the embodiment.
[0086] The vehicle 800 may include components such as a chassis, a
vehicle body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles,
and other components of a vehicle. The vehicle 800 may include a
propulsion system 850, such as an internal combustion engine,
hybrid electric power plant, an all-electric engine, and/or another
propulsion system type. The propulsion system 850 may be connected
to a drive train of the vehicle 800, which may include a
transmission, to enable the propulsion of the vehicle 800. The
propulsion system 850 may be controlled in response to receiving
signals from the throttle/accelerator 852.
[0087] A steering system 854, which may include a steering wheel,
may be used to steer the vehicle 800 (e.g., along a desired path or
route) when the propulsion system 850 is operating (e.g., when the
vehicle is in motion). The steering system 854 may receive signals
from a steering actuator 856. The steering wheel may be optional
for full automation (Level 5) functionality.
[0088] The brake sensor system 846 may be used to operate the
vehicle brakes in response to receiving signals from the brake
actuators 848 and/or brake sensors.
[0089] Controller(s) 836, which may include one or more system on
chips (SoCs) 804 (FIG. 8C) and/or GPU(s), may provide signals
(e.g., representative of commands) to one or more components and/or
systems of the vehicle 800. For example, the controller(s) may send
signals to operate the vehicle brakes via one or more brake
actuators 848, to operate the steering system 854 via one or more
steering actuators 856, to operate the propulsion system 850 via
one or more throttle/accelerators 852. The controller(s) 836 may
include one or more onboard (e.g., integrated) computing devices
(e.g., supercomputers) that process sensor signals, and output
operation commands (e.g., signals representing commands) to enable
autonomous driving and/or to assist a human driver in driving the
vehicle 800. The sensor signals may include video signals.
Processing the signals may include assigning a classification to
the video content using a multiclass classifier, such as classifier
108. The output from the classifier 108 may be adjusted using a
temporal filter, such as temporal filter 130. The controller(s) 836
may include a first controller 836 for autonomous driving
functions, a second controller 836 for functional safety functions,
a third controller 836 for artificial intelligence functionality
(e.g., computer vision), a fourth controller 836 for infotainment
functionality, a fifth controller 836 for redundancy in emergency
conditions, and/or other controllers. In some examples, a single
controller 836 may handle two or more of the above functionalities,
two or more controllers 836 may handle a single functionality,
and/or any combination thereof.
[0090] The controller(s) 836 may provide the signals for
controlling one or more components and/or systems of the vehicle
800 in response to sensor data received from one or more sensors
(e.g., sensor inputs). The sensor data may be received from, for
example and without limitation, global navigation satellite systems
sensor(s) 858 (e.g., Global Positioning System sensor(s)), RADAR
sensor(s) 860, ultrasonic sensor(s) 862, LIDAR sensor(s) 864,
inertial measurement unit (IMU) sensor(s) 866 (e.g.,
accelerometer(s), gyroscope(s), magnetic compass(es),
magnetometer(s), etc.), microphone(s) 896, stereo camera(s) 868,
wide-view camera(s) 870 (e.g., fisheye cameras), infrared camera(s)
872, surround camera(s) 874 (e.g., 360 degree cameras), long-range
and/or mid-range camera(s) 898, speed sensor(s) 844 (e.g., for
measuring the speed of the vehicle 800), vibration sensor(s) 842,
steering sensor(s) 840, brake sensor(s) (e.g., as part of the brake
sensor system 846), and/or other sensor types.
[0091] One or more of the controller(s) 836 may receive inputs
(e.g., represented by input data) from an instrument cluster 832 of
the vehicle 800 and provide outputs (e.g., represented by output
data, display data, etc.) via a human-machine interface (HMI)
display 834, an audible annunciator, a loudspeaker, and/or via
other components of the vehicle 800. The outputs may include
information such as vehicle velocity, speed, time, map data (e.g.,
the HD map 822 of FIG. 8C), location data (e.g., the vehicle's 800
location, such as on a map), direction, location of other vehicles
(e.g., an occupancy grid), information about objects and status of
objects as perceived by the controller(s) 836, etc. For example,
the HMI display 834 may display information about the presence of
one or more objects (e.g., a street sign, caution sign, traffic
light changing, etc.), and/or information about driving maneuvers
the vehicle has made, is making, or will make (e.g., changing lanes
now, taking exit 34B in two miles, etc.). Objects may be identified
using a multiclass classifier, such as classifier 108. The output
from the classifier 108 may be tuned using a temporal filter, such
as temporal filter 130.
[0092] The vehicle 800 further includes a network interface 824
which may use one or more wireless antenna(s) 826 and/or modem(s)
to communicate over one or more networks. For example, the network
interface 824 may be capable of communication over LTE, WCDMA,
UMTS, GSM, CDMA2000, etc. The wireless antenna(s) 826 may also
enable communication between objects in the environment (e.g.,
vehicles, mobile devices, etc.), using local area network(s), such
as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc., and/or low power
wide-area network(s) (LPWANs), such as LoRaWAN, SigFox, etc.
[0093] FIG. 8B is an example of camera locations and fields of view
for the example autonomous vehicle 800 of FIG. 8A, in accordance
with some embodiments of the present disclosure. The cameras and
respective fields of view are one example embodiment and are not
intended to be limiting. For example, additional and/or alternative
cameras may be included and/or the cameras may be located at
different locations on the vehicle 800.
[0094] The camera types for the cameras may include, but are not
limited to, digital cameras that may be adapted for use with the
components and/or systems of the vehicle 800. The camera(s) may
operate at automotive safety integrity level (ASIL) B and/or at
another ASIL. The camera types may be capable of any image capture
rate, such as 60 frames per second (fps), 820 fps, 240 fps, etc.,
depending on the embodiment. The cameras may be capable of using
rolling shutters, global shutters, another type of shutter, or a
combination thereof. In some examples, the color filter array may
include a red clear clear clear (RCCC) color filter array, a red
clear clear blue (RCCB) color filter array, a red blue green clear
(RBGC) color filter array, a Foveon X3 color filter array, a Bayer
sensors (RGGB) color filter array, a monochrome sensor color filter
array, and/or another type of color filter array. In some
embodiments, clear pixel cameras, such as cameras with an RCCC, an
RCCB, and/or an RBGC color filter array, may be used in an effort
to increase light sensitivity.
[0095] In some examples, one or more of the camera(s) may be used
to perform advanced driver assistance systems (ADAS) functions
(e.g., as part of a redundant or fail-safe design). For example, a
Multi-Function Mono Camera may be installed to provide functions
including lane departure warning, traffic sign assist and
intelligent headlamp control. One or more of the camera(s) (e.g.,
all of the cameras) may record and provide image data (e.g., video)
simultaneously.
[0096] One or more of the cameras may be mounted in a mounting
assembly, such as a custom designed (3-D printed) assembly, in
order to cut out stray light and reflections from within the car
(e.g., reflections from the dashboard reflected in the windshield
mirrors) which may interfere with the camera's image data capture
abilities. With reference to wing-mirror mounting assemblies, the
wing-mirror assemblies may be custom 3-D printed so that the camera
mounting plate matches the shape of the wing-mirror. In some
examples, the camera(s) may be integrated into the wing-mirror. For
side-view cameras, the camera(s) may also be integrated within the
four pillars at each corner of the cabin.
[0097] Cameras with a field of view that include portions of the
environment in front of the vehicle 800 (e.g., front-facing
cameras) may be used for surround view, to help identify forward
facing paths and obstacles, as well aid in, with the help of one or
more controllers 836 and/or control SoCs, providing information
critical to generating an occupancy grid and/or determining the
preferred vehicle paths. Front-facing cameras may be used to
perform many of the same ADAS functions as LIDAR, including
emergency braking, pedestrian detection, and collision avoidance.
Front-facing cameras may also be used for ADAS functions and
systems including Lane Departure Warnings ("LDW"), Autonomous
Cruise Control ("ACC"), and/or other functions such as traffic sign
recognition.
[0098] A variety of cameras may be used in a front-facing
configuration, including, for example, a monocular camera platform
that includes a CMOS (complementary metal oxide semiconductor)
color imager. Another example may be a wide-view camera(s) 870 that
may be used to perceive objects coming into view from the periphery
(e.g., pedestrians, crossing traffic or bicycles). Although only
one wide-view camera is illustrated in FIG. 8B, there may any
number of wide-view cameras 870 on the vehicle 800. In addition,
long-range camera(s) 898 (e.g., a long-view stereo camera pair) may
be used for depth-based object detection, especially for objects
for which a neural network has not yet been trained. The long-range
camera(s) 898 may also be used for object detection and
classification, as well as basic object tracking.
[0099] One or more stereo cameras 868 may also be included in a
front-facing configuration. The stereo camera(s) 868 may include an
integrated control unit comprising a scalable processing unit,
which may provide a programmable logic (FPGA) and a multi-core
micro-processor with an integrated CAN or Ethernet interface on a
single chip. Such a unit may be used to generate a 3-D map of the
vehicle's environment, including a distance estimate for all the
points in the image. An alternative stereo camera(s) 868 may
include a compact stereo vision sensor(s) that may include two
camera lenses (one each on the left and right) and an image
processing chip that may measure the distance from the vehicle to
the target object and use the generated information (e.g.,
metadata) to activate the autonomous emergency braking and lane
departure warning functions. Other types of stereo camera(s) 868
may be used in addition to, or alternatively from, those described
herein.
[0100] Cameras with a field of view that include portions of the
environment to the side of the vehicle 800 (e.g., side-view
cameras) may be used for surround view, providing information used
to create and update the occupancy grid, as well as to generate
side impact collision warnings. For example, surround camera(s) 874
(e.g., four surround cameras 874 as illustrated in FIG. 8B) may be
positioned to on the vehicle 800. The surround camera(s) 874 may
include wide-view camera(s) 870, fisheye camera(s), 360 degree
camera(s), and/or the like. Four example, four fisheye cameras may
be positioned on the vehicle's front, rear, and sides. In an
alternative arrangement, the vehicle may use three surround
camera(s) 874 (e.g., left, right, and rear), and may leverage one
or more other camera(s) (e.g., a forward-facing camera) as a fourth
surround view camera.
[0101] Cameras with a field of view that include portions of the
environment to the rear of the vehicle 800 (e.g., rear-view
cameras) may be used for park assistance, surround view, rear
collision warnings, and creating and updating the occupancy grid. A
wide variety of cameras may be used including, but not limited to,
cameras that are also suitable as a front-facing camera(s) (e.g.,
long-range and/or mid-range camera(s) 898, stereo camera(s) 868),
infrared camera(s) 872, etc.), as described herein.
[0102] FIG. 8C is a block diagram of an example system architecture
for the example autonomous vehicle 800 of FIG. 8A, in accordance
with some embodiments of the present disclosure. It should be
understood that this and other arrangements described herein are
set forth only as examples. Other arrangements and elements (e.g.,
machines, interfaces, functions, orders, groupings of functions,
etc.) may be used in addition to or instead of those shown, and
some elements may be omitted altogether. Further, many of the
elements described herein are functional entities that may be
implemented as discrete or distributed components or in conjunction
with other components, and in any suitable combination and
location. Various functions described herein as being performed by
entities may be carried out by hardware, firmware, and/or software.
For instance, various functions may be carried out by a processor
executing instructions stored in memory.
[0103] Each of the components, features, and systems of the vehicle
800 in FIG. 8C are illustrated as being connected via bus 802. The
bus 802 may include a Controller Area Network (CAN) data interface
(alternatively referred to herein as a "CAN bus"). A CAN may be a
network inside the vehicle 800 used to aid in control of various
features and functionality of the vehicle 800, such as actuation of
brakes, acceleration, braking, steering, windshield wipers, etc. A
CAN bus may be configured to have dozens or even hundreds of nodes,
each with its own unique identifier (e.g., a CAN ID). The CAN bus
may be read to find steering wheel angle, ground speed, engine
revolutions per minute (RPMs), button positions, and/or other
vehicle status indicators. The CAN bus may be ASIL B compliant.
[0104] Although the bus 802 is described herein as being a CAN bus,
this is not intended to be limiting. For example, in addition to,
or alternatively from, the CAN bus, FlexRay and/or Ethernet may be
used. Additionally, although a single line is used to represent the
bus 802, this is not intended to be limiting. For example, there
may be any number of busses 802, which may include one or more CAN
busses, one or more FlexRay busses, one or more Ethernet busses,
and/or one or more other types of busses using a different
protocol. In some examples, two or more busses 802 may be used to
perform different functions, and/or may be used for redundancy. For
example, a first bus 802 may be used for collision avoidance
functionality and a second bus 802 may be used for actuation
control. In any example, each bus 802 may communicate with any of
the components of the vehicle 800, and two or more busses 802 may
communicate with the same components. In some examples, each SoC
804, each controller 836, and/or each computer within the vehicle
may have access to the same input data (e.g., inputs from sensors
of the vehicle 800), and may be connected to a common bus, such the
CAN bus.
[0105] The vehicle 800 may include one or more controller(s) 836,
such as those described herein with respect to FIG. 8A. The
controller(s) 836 may be used for a variety of functions. The
controller(s) 836 may be coupled to any of the various other
components and systems of the vehicle 800, and may be used for
control of the vehicle 800, artificial intelligence of the vehicle
800, infotainment for the vehicle 800, and/or the like. The
controllers may include a multiclass classifier, such as classifier
108, and/or use the output of such a classifier. The output from
the classifier 108 may be tuned using a temporal filter, such as
temporal filter 130.
[0106] The vehicle 800 may include a system(s) on a chip (SoC) 804.
The SoC 804 may include CPU(s) 806, GPU(s) 808, processor(s) 810,
cache(s) 812, accelerator(s) 814, data store(s) 816, and/or other
components and features not illustrated. The SoC(s) 804 may be used
to control the vehicle 800 in a variety of platforms and systems.
For example, the SoC(s) 804 may be combined in a system (e.g., the
system of the vehicle 800) with an HD map 822 which may obtain map
refreshes and/or updates via a network interface 824 from one or
more servers (e.g., server(s) 878 of FIG. 8D).
[0107] The CPU(s) 806 may include a CPU cluster or CPU complex
(alternatively referred to herein as a "CCPLEX"). The CPU(s) 806
may include multiple cores and/or L2 caches. For example, in some
embodiments, the CPU(s) 806 may include eight cores in a coherent
multi-processor configuration. In some embodiments, the CPU(s) 806
may include four dual-core clusters where each cluster has a
dedicated L2 cache (e.g., a 2 MB L2 cache). The CPU(s) 806 (e.g.,
the CCPLEX) may be configured to support simultaneous cluster
operation enabling any combination of the clusters of the CPU(s)
806 to be active at any given time.
[0108] The CPU(s) 806 may implement power management capabilities
that include one or more of the following features: individual
hardware blocks may be clock-gated automatically when idle to save
dynamic power; each core clock may be gated when the core is not
actively executing instructions due to execution of WFI/WFE
instructions; each core may be independently power-gated; each core
cluster may be independently clock-gated when all cores are
clock-gated or power-gated; and/or each core cluster may be
independently power-gated when all cores are power-gated. The
CPU(s) 806 may further implement an enhanced algorithm for managing
power states, where allowed power states and expected wakeup times
are specified, and the hardware/microcode determines the best power
state to enter for the core, cluster, and CCPLEX. The processing
cores may support simplified power state entry sequences in
software with the work offloaded to microcode.
[0109] The GPU(s) 808 may include an integrated GPU (alternatively
referred to herein as an "iGPU"). The GPU(s) 808 may be
programmable and may be efficient for parallel workloads. The
GPU(s) 808, in some examples, may use an enhanced tensor
instruction set. The GPU(s) 808 may include one or more streaming
microprocessors, where each streaming microprocessor may include an
L1 cache (e.g., an L1 cache with at least 96 KB storage capacity),
and two or more of the streaming microprocessors may share an L2
cache (e.g., an L2 cache with a 512 KB storage capacity). In some
embodiments, the GPU(s) 808 may include at least eight streaming
microprocessors. The GPU(s) 808 may use compute application
programming interface(s) (API(s)). In addition, the GPU(s) 808 may
use one or more parallel computing platforms and/or programming
models (e.g., NVIDIA's CUDA).
[0110] The GPU(s) 808 may be power-optimized for best performance
in automotive and embedded use cases. For example, the GPU(s) 808
may be fabricated on a Fin field-effect transistor (FinFET).
However, this is not intended to be limiting and the GPU(s) 808 may
be fabricated using other semiconductor manufacturing processes.
Each streaming microprocessor may incorporate a number of
mixed-precision processing cores partitioned into multiple blocks.
For example, and without limitation, 64 PF32 cores and 32 PF64
cores may be partitioned into four processing blocks. In such an
example, each processing block may be allocated 16 FP32 cores, 8
FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs
for deep learning matrix arithmetic, an L0 instruction cache, a
warp scheduler, a dispatch unit, and/or a 64 KB register file. In
addition, the streaming microprocessors may include independent
parallel integer and floating-point data paths to provide for
efficient execution of workloads with a mix of computation and
addressing calculations. The streaming microprocessors may include
independent thread scheduling capability to enable finer-grain
synchronization and cooperation between parallel threads. The
streaming microprocessors may include a combined L1 data cache and
shared memory unit in order to improve performance while
simplifying programming.
[0111] The GPU(s) 808 may include a high bandwidth memory (HBM)
and/or a 16 GB HBM2 memory subsystem to provide, in some examples,
about 900 GB/second peak memory bandwidth. In some examples, in
addition to, or alternatively from, the HBM memory, a synchronous
graphics random-access memory (SGRAM) may be used, such as a
graphics double data rate type five synchronous random-access
memory (GDDR5).
[0112] The GPU(s) 808 may include unified memory technology
including access counters to allow for more accurate migration of
memory pages to the processor that accesses them most frequently,
thereby improving efficiency for memory ranges shared between
processors. In some examples, address translation services (ATS)
support may be used to allow the GPU(s) 808 to access the CPU(s)
806 page tables directly. In such examples, when the GPU(s) 808
memory management unit (MMU) experiences a miss, an address
translation request may be transmitted to the CPU(s) 806. In
response, the CPU(s) 806 may look in its page tables for the
virtual-to-physical mapping for the address and transmits the
translation back to the GPU(s) 808. As such, unified memory
technology may allow a single unified virtual address space for
memory of both the CPU(s) 806 and the GPU(s) 808, thereby
simplifying the GPU(s) 808 programming and porting of applications
to the GPU(s) 808.
[0113] In addition, the GPU(s) 808 may include an access counter
that may keep track of the frequency of access of the GPU(s) 808 to
memory of other processors. The access counter may help ensure that
memory pages are moved to the physical memory of the processor that
is accessing the pages most frequently.
[0114] The SoC(s) 804 may include any number of cache(s) 812,
including those described herein. For example, the cache(s) 812 may
include an L3 cache that is available to both the CPU(s) 806 and
the GPU(s) 808 (e.g., that is connected both the CPU(s) 806 and the
GPU(s) 808). The cache(s) 812 may include a write-back cache that
may keep track of states of lines, such as by using a cache
coherence protocol (e.g., MEI, MESI, MSI, etc.). The L3 cache may
include 4 MB or more, depending on the embodiment, although smaller
cache sizes may be used.
[0115] The SoC(s) 804 may include an arithmetic logic unit(s)
(ALU(s)) which may be leveraged in performing processing with
respect to any of the variety of tasks or operations of the vehicle
800--such as processing DNNs. In addition, the SoC(s) 804 may
include a floating point unit(s) (FPU(s))--or other math
coprocessor or numeric coprocessor types--for performing
mathematical operations within the system. For example, the SoC(s)
104 may include one or more FPUs integrated as execution units
within a CPU(s) 806 and/or GPU(s) 808.
[0116] The SoC(s) 804 may include one or more accelerators 814
(e.g., hardware accelerators, software accelerators, or a
combination thereof). For example, the SoC(s) 804 may include a
hardware acceleration cluster that may include optimized hardware
accelerators and/or large on-chip memory. The large on-chip memory
(e.g., 4 MB of SRAM), may enable the hardware acceleration cluster
to accelerate neural networks and other calculations. The hardware
acceleration cluster may be used to complement the GPU(s) 808 and
to off-load some of the tasks of the GPU(s) 808 (e.g., to free up
more cycles of the GPU(s) 808 for performing other tasks). As an
example, the accelerator(s) 814 may be used for targeted workloads
(e.g., perception, convolutional neural networks (CNNs), etc.) that
are stable enough to be amenable to acceleration. The term "CNN,"
as used herein, may include all types of CNNs, including
region-based or regional convolutional neural networks (RCNNs) and
Fast RCNNs (e.g., as used for object detection). A multiclass
classifier, such as classifier 108, may include a CNN. The output
from the classifier 108 may be tuned using a temporal filter, such
as temporal filter 130.
[0117] The accelerator(s) 814 (e.g., the hardware acceleration
cluster) may include a deep learning accelerator(s) (DLA). The
DLA(s) may include one or more Tensor processing units (TPUs) that
may be configured to provide an additional ten trillion operations
per second for deep learning applications and inferencing. The TPUs
may be accelerators configured to, and optimized for, performing
image processing functions (e.g., for CNNs, RCNNs, etc.). The
DLA(s) may further be optimized for a specific set of neural
network types and floating point operations, as well as
inferencing. The design of the DLA(s) may provide more performance
per millimeter than a general-purpose GPU, and vastly exceeds the
performance of a CPU. The TPU(s) may perform several functions,
including a single-instance convolution function, supporting, for
example, INT8, INT16, and FP16 data types for both features and
weights, as well as post-processor functions.
[0118] The DLA(s) may quickly and efficiently execute neural
networks, especially CNNs, on processed or unprocessed data for any
of a variety of functions, including, for example and without
limitation: a CNN for object identification and detection using
data from camera sensors; a CNN for distance estimation using data
from camera sensors; a CNN for emergency vehicle detection and
identification and detection using data from microphones; a CNN for
facial recognition and vehicle owner identification using data from
camera sensors; and/or a CNN for security and/or safety related
events.
[0119] The DLA(s) may perform any function of the GPU(s) 808, and
by using an inference accelerator, for example, a designer may
target either the DLA(s) or the GPU(s) 808 for any function. For
example, the designer may focus processing of CNNs and floating
point operations on the DLA(s) and leave other functions to the
GPU(s) 808 and/or other accelerator(s) 814.
[0120] The accelerator(s) 814 (e.g., the hardware acceleration
cluster) may include a programmable vision accelerator(s) (PVA),
which may alternatively be referred to herein as a computer vision
accelerator. The PVA(s) may be designed and configured to
accelerate computer vision algorithms for the advanced driver
assistance systems (ADAS), autonomous driving, and/or augmented
reality (AR) and/or virtual reality (VR) applications. The PVA(s)
may provide a balance between performance and flexibility. For
example, each PVA(s) may include, for example and without
limitation, any number of reduced instruction set computer (RISC)
cores, direct memory access (DMA), and/or any number of vector
processors.
[0121] The RISC cores may interact with image sensors (e.g., the
image sensors of any of the cameras described herein), image signal
processor(s), and/or the like. Each of the RISC cores may include
any amount of memory. The RISC cores may use any of a number of
protocols, depending on the embodiment. In some examples, the RISC
cores may execute a real-time operating system (RTOS). The RISC
cores may be implemented using one or more integrated circuit
devices, application specific integrated circuits (ASICs), and/or
memory devices. For example, the RISC cores may include an
instruction cache and/or a tightly coupled RAM.
[0122] The DMA may enable components of the PVA(s) to access the
system memory independently of the CPU(s) 806. The DMA may support
any number of features used to provide optimization to the PVA
including, but not limited to, supporting multi-dimensional
addressing and/or circular addressing. In some examples, the DMA
may support up to six or more dimensions of addressing, which may
include block width, block height, block depth, horizontal block
stepping, vertical block stepping, and/or depth stepping.
[0123] The vector processors may be programmable processors that
may be designed to efficiently and flexibly execute programming for
computer vision algorithms and provide signal processing
capabilities. In some examples, the PVA may include a PVA core and
two vector processing subsystem partitions. The PVA core may
include a processor subsystem, DMA engine(s) (e.g., two DMA
engines), and/or other peripherals. The vector processing subsystem
may operate as the primary processing engine of the PVA, and may
include a vector processing unit (VPU), an instruction cache,
and/or vector memory (e.g., VMEM). A VPU core may include a digital
signal processor such as, for example, a single instruction,
multiple data (SIMD), very long instruction word (VLIW) digital
signal processor. The combination of the SIMD and VLIW may enhance
throughput and speed.
[0124] Each of the vector processors may include an instruction
cache and may be coupled to dedicated memory. As a result, in some
examples, each of the vector processors may be configured to
execute independently of the other vector processors. In other
examples, the vector processors that are included in a particular
PVA may be configured to employ data parallelism. For example, in
some embodiments, the plurality of vector processors included in a
single PVA may execute the same computer vision algorithm, but on
different regions of an image. In other examples, the vector
processors included in a particular PVA may simultaneously execute
different computer vision algorithms, on the same image, or even
execute different algorithms on sequential images or portions of an
image. Among other things, any number of PVAs may be included in
the hardware acceleration cluster and any number of vector
processors may be included in each of the PVAs. In addition, the
PVA(s) may include additional error correcting code (ECC) memory,
to enhance overall system safety.
[0125] The accelerator(s) 814 (e.g., the hardware acceleration
cluster) may include a computer vision network on-chip and SRAM,
for providing a high-bandwidth, low latency SRAM for the
accelerator(s) 814. In some examples, the on-chip memory may
include at least 4 MB SRAM, consisting of, for example and without
limitation, eight field-configurable memory blocks, that may be
accessible by both the PVA and the DLA. Each pair of memory blocks
may include an advanced peripheral bus (APB) interface,
configuration circuitry, a controller, and a multiplexer. Any type
of memory may be used. The PVA and DLA may access the memory via a
backbone that provides the PVA and DLA with high-speed access to
memory. The backbone may include a computer vision network on-chip
that interconnects the PVA and the DLA to the memory (e.g., using
the APB).
[0126] The computer vision network on-chip may include an interface
that determines, before transmission of any control
signal/address/data, that both the PVA and the DLA provide ready
and valid signals. Such an interface may provide for separate
phases and separate channels for transmitting control
signals/addresses/data, as well as burst-type communications for
continuous data transfer. This type of interface may comply with
ISO 26262 or IEC 61508 standards, although other standards and
protocols may be used.
[0127] In some examples, the SoC(s) 804 may include a real-time
ray-tracing hardware accelerator, such as described in U.S. patent
application Ser. No. 16/101,232, filed on Aug. 10, 2018. The
real-time ray-tracing hardware accelerator may be used to quickly
and efficiently determine the positions and extents of objects
(e.g., within a world model), to generate real-time visualization
simulations, for RADAR signal interpretation, for sound propagation
synthesis and/or analysis, for simulation of SONAR systems, for
general wave propagation simulation, for comparison to LIDAR data
for purposes of localization and/or other functions, and/or for
other uses. In some embodiments, one or more tree traversal units
(TTUs) may be used for executing one or more ray-tracing related
operations.
[0128] The accelerator(s) 814 (e.g., the hardware accelerator
cluster) have a wide array of uses for autonomous driving. The PVA
may be a programmable vision accelerator that may be used for key
processing stages in ADAS and autonomous vehicles. The PVA's
capabilities are a good match for algorithmic domains needing
predictable processing, at low power and low latency. In other
words, the PVA performs well on semi-dense or dense regular
computation, even on small data sets, which need predictable
run-times with low latency and low power. Thus, in the context of
platforms for autonomous vehicles, the PVAs are designed to run
classic computer vision algorithms, as they are efficient at object
detection and operating on integer math.
[0129] For example, according to one embodiment of the technology,
the PVA is used to perform computer stereo vision. A semi-global
matching-based algorithm may be used in some examples, although
this is not intended to be limiting. Many applications for Level
3-5 autonomous driving require motion estimation/stereo matching
on-the-fly (e.g., structure from motion, pedestrian recognition,
lane detection, etc.). The PVA may perform computer stereo vision
function on inputs from two monocular cameras.
[0130] In some examples, the PVA may be used to perform dense
optical flow. According to process raw RADAR data (e.g., using a 4D
Fast Fourier Transform) to provide Processed RADAR. In other
examples, the PVA is used for time of flight depth processing, by
processing raw time of flight data to provide processed time of
flight data, for example.
[0131] The DLA may be used to run any type of network to enhance
control and driving safety, including for example, a neural network
that outputs a measure of confidence for each object detection.
Object detection may use a multiclass classifier, such as
classifier 108. The output from the classifier 108 may be tuned
using a temporal filter, such as temporal filter 130. Such a
confidence value may be interpreted as a probability, or as
providing a relative "weight" of each detection compared to other
detections. This confidence value enables the system to make
further decisions regarding which detections should be considered
as true positive detections rather than false positive detections.
For example, the system may set a threshold value for the
confidence and consider only the detections exceeding the threshold
value as true positive detections. In an automatic emergency
braking (AEB) system, false positive detections would cause the
vehicle to automatically perform emergency braking, which is
obviously undesirable. Therefore, only the most confident
detections should be considered as triggers for AEB. The DLA may
run a neural network for regressing the confidence value. The
neural network may take as its input at least some subset of
parameters, such as bounding box dimensions, ground plane estimate
obtained (e.g. from another subsystem), inertial measurement unit
(IMU) sensor 866 output that correlates with the vehicle 800
orientation, distance, 3D location estimates of the object obtained
from the neural network and/or other sensors (e.g., LIDAR sensor(s)
864 or RADAR sensor(s) 860), among others.
[0132] The SoC(s) 804 may include data store(s) 816 (e.g., memory).
The data store(s) 816 may be on-chip memory of the SoC(s) 804,
which may store neural networks to be executed on the GPU and/or
the DLA. In some examples, the data store(s) 816 may be large
enough in capacity to store multiple instances of neural networks
for redundancy and safety. The data store(s) 812 may comprise L2 or
L3 cache(s) 812. Reference to the data store(s) 816 may include
reference to the memory associated with the PVA, DLA, and/or other
accelerator(s) 814, as described herein.
[0133] The SoC(s) 804 may include one or more processor(s) 810
(e.g., embedded processors). The processor(s) 810 may include a
boot and power management processor that may be a dedicated
processor and subsystem to handle boot power and management
functions and related security enforcement. The boot and power
management processor may be a part of the SoC(s) 804 boot sequence
and may provide runtime power management services. The boot power
and management processor may provide clock and voltage programming,
assistance in system low power state transitions, management of
SoC(s) 804 thermals and temperature sensors, and/or management of
the SoC(s) 804 power states. Each temperature sensor may be
implemented as a ring-oscillator whose output frequency is
proportional to temperature, and the SoC(s) 804 may use the
ring-oscillators to detect temperatures of the CPU(s) 806, GPU(s)
808, and/or accelerator(s) 814. If temperatures are determined to
exceed a threshold, the boot and power management processor may
enter a temperature fault routine and put the SoC(s) 804 into a
lower power state and/or put the vehicle 800 into a chauffeur to
safe stop mode (e.g., bring the vehicle 800 to a safe stop).
[0134] The processor(s) 810 may further include a set of embedded
processors that may serve as an audio processing engine. The audio
processing engine may be an audio subsystem that enables full
hardware support for multi-channel audio over multiple interfaces,
and a broad and flexible range of audio I/O interfaces. In some
examples, the audio processing engine is a dedicated processor core
with a digital signal processor with dedicated RAM.
[0135] The processor(s) 810 may further include an always on
processor engine that may provide necessary hardware features to
support low power sensor management and wake use cases. The always
on processor engine may include a processor core, a tightly coupled
RAM, supporting peripherals (e.g., timers and interrupt
controllers), various I/O controller peripherals, and routing
logic.
[0136] The processor(s) 810 may further include a safety cluster
engine that includes a dedicated processor subsystem to handle
safety management for automotive applications. The safety cluster
engine may include two or more processor cores, a tightly coupled
RAM, support peripherals (e.g., timers, an interrupt controller,
etc.), and/or routing logic. In a safety mode, the two or more
cores may operate in a lockstep mode and function as a single core
with comparison logic to detect any differences between their
operations.
[0137] The processor(s) 810 may further include a real-time camera
engine that may include a dedicated processor subsystem for
handling real-time camera management.
[0138] The processor(s) 810 may further include a high-dynamic
range signal processor that may include an image signal processor
that is a hardware engine that is part of the camera processing
pipeline.
[0139] The processor(s) 810 may include a video image compositor
that may be a processing block (e.g., implemented on a
microprocessor) that implements video post-processing functions
needed by a video playback application to produce the final image
for the player window. The video image compositor may perform lens
distortion correction on wide-view camera(s) 870, surround
camera(s) 874, and/or on in-cabin monitoring camera sensors.
In-cabin monitoring camera sensor is preferably monitored by a
neural network running on another instance of the Advanced SoC,
configured to identify in cabin events and respond accordingly. An
in-cabin system may perform lip reading to activate cellular
service and place a phone call, dictate emails, change the
vehicle's destination, activate or change the vehicle's
infotainment system and settings, or provide voice-activated web
surfing. Certain functions are available to the driver only when
the vehicle is operating in an autonomous mode, and are disabled
otherwise.
[0140] The video image compositor may include enhanced temporal
noise reduction for both spatial and temporal noise reduction. For
example, where motion occurs in a video, the noise reduction
weights spatial information appropriately, decreasing the weight of
information provided by adjacent frames. Where an image or portion
of an image does not include motion, the temporal noise reduction
performed by the video image compositor may use information from
the previous image to reduce noise in the current image.
[0141] The video image compositor may also be configured to perform
stereo rectification on input stereo lens frames. The video image
compositor may further be used for user interface composition when
the operating system desktop is in use, and the GPU(s) 808 is not
required to continuously render new surfaces. Even when the GPU(s)
808 is powered on and active doing 3D rendering, the video image
compositor may be used to offload the GPU(s) 808 to improve
performance and responsiveness.
[0142] The SoC(s) 804 may further include a mobile industry
processor interface (MIPI) camera serial interface for receiving
video and input from cameras, a high-speed interface, and/or a
video input block that may be used for camera and related pixel
input functions. The SoC(s) 804 may further include an input/output
controller(s) that may be controlled by software and may be used
for receiving I/O signals that are uncommitted to a specific
role.
[0143] The SoC(s) 804 may further include a broad range of
peripheral interfaces to enable communication with peripherals,
audio codecs, power management, and/or other devices. The SoC(s)
804 may be used to process data from cameras (e.g., connected over
Gigabit Multimedia Serial Link and Ethernet), sensors (e.g., LIDAR
sensor(s) 864, RADAR sensor(s) 860, etc. that may be connected over
Ethernet), data from bus 802 (e.g., speed of vehicle 800, steering
wheel position, etc.), data from GNSS sensor(s) 858 (e.g.,
connected over Ethernet or CAN bus). The SoC(s) 804 may further
include dedicated high-performance mass storage controllers that
may include their own DMA engines, and that may be used to free the
CPU(s) 806 from routine data management tasks.
[0144] The SoC(s) 804 may be an end-to-end platform with a flexible
architecture that spans automation levels 3-5, thereby providing a
comprehensive functional safety architecture that leverages and
makes efficient use of computer vision and ADAS techniques for
diversity and redundancy, provides a platform for a flexible,
reliable driving software stack, along with deep learning tools.
The SoC(s) 804 may be faster, more reliable, and even more
energy-efficient and space-efficient than conventional systems. For
example, the accelerator(s) 814, when combined with the CPU(s) 806,
the GPU(s) 808, and the data store(s) 816, may provide for a fast,
efficient platform for level 3-5 autonomous vehicles.
[0145] The technology thus provides capabilities and functionality
that cannot be achieved by conventional systems. For example,
computer vision algorithms may be executed on CPUs, which may be
configured using high-level programming language, such as the C
programming language, to execute a wide variety of processing
algorithms across a wide variety of visual data. However, CPUs are
oftentimes unable to meet the performance requirements of many
computer vision applications, such as those related to execution
time and power consumption, for example. In particular, many CPUs
are unable to execute complex object detection algorithms in
real-time, which is a requirement of in-vehicle ADAS applications,
and a requirement for practical Level 3-5 autonomous vehicles.
[0146] In contrast to conventional systems, by providing a CPU
complex, GPU complex, and a hardware acceleration cluster, the
technology described herein allows for multiple neural networks to
be performed simultaneously and/or sequentially, and for the
results to be combined together to enable Level 3-5 autonomous
driving functionality. For example, a CNN executing on the DLA or
dGPU (e.g., the GPU(s) 820) may include a text and word
recognition, allowing the supercomputer to read and understand
traffic signs, including signs for which the neural network has not
been specifically trained. The DLA may further include a neural
network that is able to identify, interpret, and provides semantic
understanding of the sign, and to pass that semantic understanding
to the path planning modules running on the CPU Complex.
[0147] As another example, multiple neural networks may be run
simultaneously, as is required for Level 3, 4, or 5 driving. For
example, a warning sign consisting of "Caution: flashing lights
indicate icy conditions," along with an electric light, may be
independently or collectively interpreted by several neural
networks. The sign itself may be identified as a traffic sign by a
first deployed neural network (e.g., a neural network that has been
trained), the text "Flashing lights indicate icy conditions" may be
interpreted by a second deployed neural network, which informs the
vehicle's path planning software (preferably executing on the CPU
Complex) that when flashing lights are detected, icy conditions
exist. The flashing light may be identified by operating a third
deployed neural network over multiple frames, informing the
vehicle's path-planning software of the presence (or absence) of
flashing lights. All three neural networks may run simultaneously,
such as within the DLA and/or on the GPU(s) 808.
[0148] In some examples, a CNN for facial recognition and vehicle
owner identification may use data from camera sensors to identify
the presence of an authorized driver and/or owner of the vehicle
800. The always on sensor processing engine may be used to unlock
the vehicle when the owner approaches the driver door and turn on
the lights, and, in security mode, to disable the vehicle when the
owner leaves the vehicle. In this way, the SoC(s) 804 provide for
security against theft and/or carjacking.
[0149] In another example, a CNN for emergency vehicle detection
and identification may use data from microphones 896 to detect and
identify emergency vehicle sirens. In contrast to conventional
systems, that use general classifiers to detect sirens and manually
extract features, the SoC(s) 804 use the CNN for classifying
environmental and urban sounds, as well as classifying visual data.
In a preferred embodiment, the CNN running on the DLA is trained to
identify the relative closing speed of the emergency vehicle (e.g.,
by using the Doppler Effect). The CNN may also be trained to
identify emergency vehicles specific to the local area in which the
vehicle is operating, as identified by GNSS sensor(s) 858. Thus,
for example, when operating in Europe the CNN will seek to detect
European sirens, and when in the United States the CNN will seek to
identify only North American sirens. Once an emergency vehicle is
detected, a control program may be used to execute an emergency
vehicle safety routine, slowing the vehicle, pulling over to the
side of the road, parking the vehicle, and/or idling the vehicle,
with the assistance of ultrasonic sensors 862, until the emergency
vehicle(s) passes.
[0150] The vehicle may include a CPU(s) 818 (e.g., discrete CPU(s),
or dCPU(s)), that may be coupled to the SoC(s) 804 via a high-speed
interconnect (e.g., PCIe). The CPU(s) 818 may include an X86
processor, for example. The CPU(s) 818 may be used to perform any
of a variety of functions, including arbitrating potentially
inconsistent results between ADAS sensors and the SoC(s) 804,
and/or monitoring the status and health of the controller(s) 836
and/or infotainment SoC 830, for example.
[0151] The vehicle 800 may include a GPU(s) 820 (e.g., discrete
GPU(s), or dGPU(s)), that may be coupled to the SoC(s) 804 via a
high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU(s) 820 may
provide additional artificial intelligence functionality, such as
by executing redundant and/or different neural networks, and may be
used to train and/or update neural networks based on input (e.g.,
sensor data) from sensors of the vehicle 800.
[0152] The vehicle 800 may further include the network interface
824 which may include one or more wireless antennas 826 (e.g., one
or more wireless antennas for different communication protocols,
such as a cellular antenna, a Bluetooth antenna, etc.). The network
interface 824 may be used to enable wireless connectivity over the
Internet with the cloud (e.g., with the server(s) 878 and/or other
network devices), with other vehicles, and/or with computing
devices (e.g., client devices of passengers). To communicate with
other vehicles, a direct link may be established between the two
vehicles and/or an indirect link may be established (e.g., across
networks and over the Internet). Direct links may be provided using
a vehicle-to-vehicle communication link. The vehicle-to-vehicle
communication link may provide the vehicle 800 information about
vehicles in proximity to the vehicle 800 (e.g., vehicles in front
of, on the side of, and/or behind the vehicle 800). This
functionality may be part of a cooperative adaptive cruise control
functionality of the vehicle 800.
[0153] The network interface 824 may include a SoC that provides
modulation and demodulation functionality and enables the
controller(s) 836 to communicate over wireless networks. The
network interface 824 may include a radio frequency front-end for
up-conversion from baseband to radio frequency, and down conversion
from radio frequency to baseband. The frequency conversions may be
performed through well-known processes, and/or may be performed
using super-heterodyne processes. In some examples, the radio
frequency front end functionality may be provided by a separate
chip. The network interface may include wireless functionality for
communicating over LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth,
Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or other wireless
protocols.
[0154] The vehicle 800 may further include data store(s) 828 which
may include off-chip (e.g., off the SoC(s) 804) storage. The data
store(s) 828 may include one or more storage elements including
RAM, SRAM, DRAM, VRAM, Flash, hard disks, and/or other components
and/or devices that may store at least one bit of data.
[0155] The vehicle 800 may further include GNSS sensor(s) 858. The
GNSS sensor(s) 858 (e.g., GPS and/or assisted GPS sensors), to
assist in mapping, perception, occupancy grid generation, and/or
path planning functions. Any number of GNSS sensor(s) 858 may be
used, including, for example and without limitation, a GPS using a
USB connector with an Ethernet to Serial (RS-232) bridge.
[0156] The vehicle 800 may further include RADAR sensor(s) 860. The
RADAR sensor(s) 860 may be used by the vehicle 800 for long-range
vehicle detection, even in darkness and/or severe weather
conditions. RADAR functional safety levels may be ASIL B. The RADAR
sensor(s) 860 may use the CAN and/or the bus 802 (e.g., to transmit
data generated by the RADAR sensor(s) 860) for control and to
access object tracking data, with access to Ethernet to access raw
data in some examples. A wide variety of RADAR sensor types may be
used. For example, and without limitation, the RADAR sensor(s) 860
may be suitable for front, rear, and side RADAR use. In some
example, Pulse Doppler RADAR sensor(s) are used.
[0157] The RADAR sensor(s) 860 may include different
configurations, such as long range with narrow field of view, short
range with wide field of view, short range side coverage, etc. In
some examples, long-range RADAR may be used for adaptive cruise
control functionality. The long-range RADAR systems may provide a
broad field of view realized by two or more independent scans, such
as within a 250 m range. The RADAR sensor(s) 860 may help in
distinguishing between static and moving objects, and may be used
by ADAS systems for emergency brake assist and forward collision
warning. Long-range RADAR sensors may include monostatic multimodal
RADAR with multiple (e.g., six or more) fixed RADAR antennae and a
high-speed CAN and FlexRay interface. In an example with six
antennae, the central four antennae may create a focused beam
pattern, designed to record the vehicle's 800 surroundings at
higher speeds with minimal interference from traffic in adjacent
lanes. The other two antennae may expand the field of view, making
it possible to quickly detect vehicles entering or leaving the
vehicle's 800 lane.
[0158] Mid-range RADAR systems may include, as an example, a range
of up to 860 m (front) or 80 m (rear), and a field of view of up to
42 degrees (front) or 850 degrees (rear). Short-range RADAR systems
may include, without limitation, RADAR sensors designed to be
installed at both ends of the rear bumper. When installed at both
ends of the rear bumper, such a RADAR sensor systems may create two
beams that constantly monitor the blind spot in the rear and next
to the vehicle.
[0159] Short-range RADAR systems may be used in an ADAS system for
blind spot detection and/or lane change assist.
[0160] The vehicle 800 may further include ultrasonic sensor(s)
862. The ultrasonic sensor(s) 862, which may be positioned at the
front, back, and/or the sides of the vehicle 800, may be used for
park assist and/or to create and update an occupancy grid. A wide
variety of ultrasonic sensor(s) 862 may be used, and different
ultrasonic sensor(s) 862 may be used for different ranges of
detection (e.g., 2.5 m, 4 m). The ultrasonic sensor(s) 862 may
operate at functional safety levels of ASIL B.
[0161] The vehicle 800 may include LIDAR sensor(s) 864. The LIDAR
sensor(s) 864 may be used for object and pedestrian detection,
emergency braking, collision avoidance, and/or other functions. The
LIDAR sensor(s) 864 may be functional safety level ASIL B. In some
examples, the vehicle 800 may include multiple LIDAR sensors 864
(e.g., two, four, six, etc.) that may use Ethernet (e.g., to
provide data to a Gigabit Ethernet switch).
[0162] In some examples, the LIDAR sensor(s) 864 may be capable of
providing a list of objects and their distances for a 360-degree
field of view. Commercially available LIDAR sensor(s) 864 may have
an advertised range of approximately 800 m, with an accuracy of 2
cm-3 cm, and with support for a 800 Mbps Ethernet connection, for
example. In some examples, one or more non-protruding LIDAR sensors
864 may be used. In such examples, the LIDAR sensor(s) 864 may be
implemented as a small device that may be embedded into the front,
rear, sides, and/or corners of the vehicle 800. The LIDAR sensor(s)
864, in such examples, may provide up to a 820-degree horizontal
and 35-degree vertical field-of-view, with a 200 m range even for
low-reflectivity objects. Front-mounted LIDAR sensor(s) 864 may be
configured for a horizontal field of view between 45 degrees and
135 degrees.
[0163] In some examples, LIDAR technologies, such as 3D flash
LIDAR, may also be used. 3D Flash LIDAR uses a flash of a laser as
a transmission source, to illuminate vehicle surroundings up to
approximately 200 m. A flash LIDAR unit includes a receptor, which
records the laser pulse transit time and the reflected light on
each pixel, which in turn corresponds to the range from the vehicle
to the objects. Flash LIDAR may allow for highly accurate and
distortion-free images of the surroundings to be generated with
every laser flash. In some examples, four flash LIDAR sensors may
be deployed, one at each side of the vehicle 800. Available 3D
flash LIDAR systems include a solid-state 3D staring array LIDAR
camera with no moving parts other than a fan (e.g., a non-scanning
LIDAR device). The flash LIDAR device may use a 5 nanosecond class
I (eye-safe) laser pulse per frame and may capture the reflected
laser light in the form of 3D range point clouds and co-registered
intensity data. By using flash LIDAR, and because flash LIDAR is a
solid-state device with no moving parts, the LIDAR sensor(s) 864
may be less susceptible to motion blur, vibration, and/or
shock.
[0164] The vehicle may further include IMU sensor(s) 866. The IMU
sensor(s) 866 may be located at a center of the rear axle of the
vehicle 800, in some examples. The IMU sensor(s) 866 may include,
for example and without limitation, an accelerometer(s), a
magnetometer(s), a gyroscope(s), a magnetic compass(es), and/or
other sensor types. In some examples, such as in six-axis
applications, the IMU sensor(s) 866 may include accelerometers and
gyroscopes, while in nine-axis applications, the IMU sensor(s) 866
may include accelerometers, gyroscopes, and magnetometers.
[0165] In some embodiments, the IMU sensor(s) 866 may be
implemented as a miniature, high performance GPS-Aided Inertial
Navigation System (GPS/INS) that combines micro-electro-mechanical
systems (MEMS) inertial sensors, a high-sensitivity GPS receiver,
and advanced Kalman filtering algorithms to provide estimates of
position, velocity, and attitude. As such, in some examples, the
IMU sensor(s) 866 may enable the vehicle 800 to estimate heading
without requiring input from a magnetic sensor by directly
observing and correlating the changes in velocity from GPS to the
IMU sensor(s) 866. In some examples, the IMU sensor(s) 866 and the
GNSS sensor(s) 858 may be combined in a single integrated unit.
[0166] The vehicle may include microphone(s) 896 placed in and/or
around the vehicle 800. The microphone(s) 896 may be used for
emergency vehicle detection and identification, among other
things.
[0167] The vehicle may further include any number of camera types,
including stereo camera(s) 868, wide-view camera(s) 870, infrared
camera(s) 872, surround camera(s) 874, long-range and/or mid-range
camera(s) 898, and/or other camera types. The cameras may be used
to capture image data around an entire periphery of the vehicle
800. The types of cameras used depends on the embodiments and
requirements for the vehicle 800, and any combination of camera
types may be used to provide the necessary coverage around the
vehicle 800. In addition, the number of cameras may differ
depending on the embodiment. For example, the vehicle may include
six cameras, seven cameras, ten cameras, twelve cameras, and/or
another number of cameras. The cameras may support, as an example
and without limitation, Gigabit Multimedia Serial Link (GMSL)
and/or Gigabit Ethernet. Each of the camera(s) is described with
more detail herein with respect to FIG. 8A and FIG. 8B.
[0168] The vehicle 800 may further include vibration sensor(s) 842.
The vibration sensor(s) 842 may measure vibrations of components of
the vehicle, such as the axle(s). For example, changes in
vibrations may indicate a change in road surfaces. In another
example, when two or more vibration sensors 842 are used, the
differences between the vibrations may be used to determine
friction or slippage of the road surface (e.g., when the difference
in vibration is between a power-driven axle and a freely rotating
axle).
[0169] The vehicle 800 may include an ADAS system 838. The ADAS
system 838 may include a SoC, in some examples. The ADAS system 838
may include autonomous/adaptive/automatic cruise control (ACC),
cooperative adaptive cruise control (CACC), forward crash warning
(FCW), automatic emergency braking (AEB), lane departure warnings
(LDW), lane keep assist (LKA), blind spot warning (BSW), rear
cross-traffic warning (RCTW), collision warning systems (CWS), lane
centering (LC), and/or other features and functionality.
[0170] The ACC systems may use RADAR sensor(s) 860, LIDAR sensor(s)
864, and/or a camera(s). The ACC systems may include longitudinal
ACC and/or lateral ACC. Longitudinal ACC monitors and controls the
distance to the vehicle immediately ahead of the vehicle 800 and
automatically adjust the vehicle speed to maintain a safe distance
from vehicles ahead. Lateral ACC performs distance keeping, and
advises the vehicle 800 to change lanes when necessary. Lateral ACC
is related to other ADAS applications such as LCA and CWS.
[0171] CACC uses information from other vehicles that may be
received via the network interface 824 and/or the wireless
antenna(s) 826 from other vehicles via a wireless link, or
indirectly, over a network connection (e.g., over the Internet).
Direct links may be provided by a vehicle-to-vehicle (V2V)
communication link, while indirect links may be
infrastructure-to-vehicle (I2V) communication link. In general, the
V2V communication concept provides information about the
immediately preceding vehicles (e.g., vehicles immediately ahead of
and in the same lane as the vehicle 800), while the I2V
communication concept provides information about traffic further
ahead. CACC systems may include either or both I2V and V2V
information sources. Given the information of the vehicles ahead of
the vehicle 800, CACC may be more reliable and it has potential to
improve traffic flow smoothness and reduce congestion on the
road.
[0172] FCW systems are designed to alert the driver to a hazard, so
that the driver may take corrective action. FCW systems use a
front-facing camera and/or RADAR sensor(s) 860, coupled to a
dedicated processor, DSP, FPGA, and/or ASIC, that is electrically
coupled to driver feedback, such as a display, speaker, and/or
vibrating component. FCW systems may provide a warning, such as in
the form of a sound, visual warning, vibration and/or a quick brake
pulse.
[0173] AEB systems detect an impending forward collision with
another vehicle or other object, and may automatically apply the
brakes if the driver does not take corrective action within a
specified time or distance parameter. AEB systems may use
front-facing camera(s) and/or RADAR sensor(s) 860, coupled to a
dedicated processor, DSP, FPGA, and/or ASIC. When the AEB system
detects a hazard, it typically first alerts the driver to take
corrective action to avoid the collision and, if the driver does
not take corrective action, the AEB system may automatically apply
the brakes in an effort to prevent, or at least mitigate, the
impact of the predicted collision. AEB systems, may include
techniques such as dynamic brake support and/or crash imminent
braking.
[0174] LDW systems provide visual, audible, and/or tactile
warnings, such as steering wheel or seat vibrations, to alert the
driver when the vehicle 800 crosses lane markings. A LDW system
does not activate when the driver indicates an intentional lane
departure, by activating a turn signal. LDW systems may use
front-side facing cameras, coupled to a dedicated processor, DSP,
FPGA, and/or ASIC, that is electrically coupled to driver feedback,
such as a display, speaker, and/or vibrating component.
[0175] LKA systems are a variation of LDW systems. LKA systems
provide steering input or braking to correct the vehicle 800 if the
vehicle 800 starts to exit the lane.
[0176] BSW systems detects and warn the driver of vehicles in an
automobile's blind spot. BSW systems may provide a visual, audible,
and/or tactile alert to indicate that merging or changing lanes is
unsafe. The system may provide an additional warning when the
driver uses a turn signal. BSW systems may use rear-side facing
camera(s) and/or RADAR sensor(s) 860, coupled to a dedicated
processor, DSP, FPGA, and/or ASIC, that is electrically coupled to
driver feedback, such as a display, speaker, and/or vibrating
component.
[0177] RCTW systems may provide visual, audible, and/or tactile
notification when an object is detected outside the rear-camera
range when the vehicle 800 is backing up. Some RCTW systems include
AEB to ensure that the vehicle brakes are applied to avoid a crash.
RCTW systems may use one or more rear-facing RADAR sensor(s) 860,
coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that is
electrically coupled to driver feedback, such as a display,
speaker, and/or vibrating component.
[0178] Conventional ADAS systems may be prone to false positive
results which may be annoying and distracting to a driver, but
typically are not catastrophic, because the ADAS systems alert the
driver and allow the driver to decide whether a safety condition
truly exists and act accordingly. However, in an autonomous vehicle
800, the vehicle 800 itself must, in the case of conflicting
results, decide whether to heed the result from a primary computer
or a secondary computer (e.g., a first controller 836 or a second
controller 836). For example, in some embodiments, the ADAS system
838 may be a backup and/or secondary computer for providing
perception information to a backup computer rationality module. The
backup computer rationality monitor may run a redundant diverse
software on hardware components to detect faults in perception and
dynamic driving tasks. Outputs from the ADAS system 838 may be
provided to a supervisory MCU. If outputs from the primary computer
and the secondary computer conflict, the supervisory MCU must
determine how to reconcile the conflict to ensure safe
operation.
[0179] In some examples, the primary computer may be configured to
provide the supervisory MCU with a confidence score, indicating the
primary computer's confidence in the chosen result. If the
confidence score exceeds a threshold, the supervisory MCU may
follow the primary computer's direction, regardless of whether the
secondary computer provides a conflicting or inconsistent result.
Where the confidence score does not meet the threshold, and where
the primary and secondary computer indicate different results
(e.g., the conflict), the supervisory MCU may arbitrate between the
computers to determine the appropriate outcome.
[0180] The supervisory MCU may be configured to run a neural
network(s) that is trained and configured to determine, based on
outputs from the primary computer and the secondary computer,
conditions under which the secondary computer provides false
alarms. Thus, the neural network(s) in the supervisory MCU may
learn when the secondary computer's output may be trusted, and when
it cannot. For example, when the secondary computer is a
RADAR-based FCW system, a neural network(s) in the supervisory MCU
may learn when the FCW system is identifying metallic objects that
are not, in fact, hazards, such as a drainage grate or manhole
cover that triggers an alarm. Similarly, when the secondary
computer is a camera-based LDW system, a neural network in the
supervisory MCU may learn to override the LDW when bicyclists or
pedestrians are present and a lane departure is, in fact, the
safest maneuver. In embodiments that include a neural network(s)
running on the supervisory MCU, the supervisory MCU may include at
least one of a DLA or GPU suitable for running the neural
network(s) with associated memory. In preferred embodiments, the
supervisory MCU may comprise and/or be included as a component of
the SoC(s) 804.
[0181] In other examples, ADAS system 838 may include a secondary
computer that performs ADAS functionality using traditional rules
of computer vision. As such, the secondary computer may use classic
computer vision rules (if-then), and the presence of a neural
network(s) in the supervisory MCU may improve reliability, safety
and performance. For example, the diverse implementation and
intentional non-identity makes the overall system more
fault-tolerant, especially to faults caused by software (or
software-hardware interface) functionality. For example, if there
is a software bug or error in the software running on the primary
computer, and the non-identical software code running on the
secondary computer provides the same overall result, the
supervisory MCU may have greater confidence that the overall result
is correct, and the bug in software or hardware on primary computer
is not causing material error.
[0182] In some examples, the output of the ADAS system 838 may be
fed into the primary computer's perception block and/or the primary
computer's dynamic driving task block. For example, if the ADAS
system 838 indicates a forward crash warning due to an object
immediately ahead, the perception block may use this information
when identifying objects. In other examples, the secondary computer
may have its own neural network which is trained and thus reduces
the risk of false positives, as described herein.
[0183] The vehicle 800 may further include the infotainment SoC 830
(e.g., an in-vehicle infotainment system (IVI)). Although
illustrated and described as a SoC, the infotainment system may not
be a SoC, and may include two or more discrete components. The
infotainment SoC 830 may include a combination of hardware and
software that may be used to provide audio (e.g., music, a personal
digital assistant, navigational instructions, news, radio, etc.),
video (e.g., TV, movies, streaming, etc.), phone (e.g., hands-free
calling), network connectivity (e.g., LTE, Wi-Fi, etc.), and/or
information services (e.g., navigation systems, rear-parking
assistance, a radio data system, vehicle related information such
as fuel level, total distance covered, brake fuel level, oil level,
door open/close, air filter information, etc.) to the vehicle 800.
For example, the infotainment SoC 830 may radios, disk players,
navigation systems, video players, USB and Bluetooth connectivity,
carputers, in-car entertainment, Wi-Fi, steering wheel audio
controls, hands free voice control, a heads-up display (HUD), an
HMI display 834, a telematics device, a control panel (e.g., for
controlling and/or interacting with various components, features,
and/or systems), and/or other components. The infotainment SoC 830
may further be used to provide information (e.g., visual and/or
audible) to a user(s) of the vehicle, such as information from the
ADAS system 838, autonomous driving information such as planned
vehicle maneuvers, trajectories, surrounding environment
information (e.g., intersection information, vehicle information,
road information, etc.), and/or other information.
[0184] The infotainment SoC 830 may include GPU functionality. The
infotainment SoC 830 may communicate over the bus 802 (e.g., CAN
bus, Ethernet, etc.) with other devices, systems, and/or components
of the vehicle 800. In some examples, the infotainment SoC 830 may
be coupled to a supervisory MCU such that the GPU of the
infotainment system may perform some self-driving functions in the
event that the primary controller(s) 836 (e.g., the primary and/or
backup computers of the vehicle 800) fail. In such an example, the
infotainment SoC 830 may put the vehicle 800 into a chauffeur to
safe stop mode, as described herein.
[0185] The vehicle 800 may further include an instrument cluster
832 (e.g., a digital dash, an electronic instrument cluster, a
digital instrument panel, etc.). The instrument cluster 832 may
include a controller and/or supercomputer (e.g., a discrete
controller or supercomputer). The instrument cluster 832 may
include a set of instrumentation such as a speedometer, fuel level,
oil pressure, tachometer, odometer, turn indicators, gearshift
position indicator, seat belt warning light(s), parking-brake
warning light(s), engine-malfunction light(s), airbag (SRS) system
information, lighting controls, safety system controls, navigation
information, etc. In some examples, information may be displayed
and/or shared among the infotainment SoC 830 and the instrument
cluster 832. In other words, the instrument cluster 832 may be
included as part of the infotainment SoC 830, or vice versa.
[0186] FIG. 8D is a system diagram for communication between
cloud-based server(s) and the example autonomous vehicle 800 of
FIG. 8A, in accordance with some embodiments of the present
disclosure. The system 876 may include server(s) 878, network(s)
890, and vehicles, including the vehicle 800. The server(s) 878 may
include a plurality of GPUs 884(A)-884(H) (collectively referred to
herein as GPUs 884), PCIe switches 882(A)-882(H) (collectively
referred to herein as PCIe switches 882), and/or CPUs 880(A)-880(B)
(collectively referred to herein as CPUs 880). The GPUs 884, the
CPUs 880, and the PCIe switches may be interconnected with
high-speed interconnects such as, for example and without
limitation, NVLink interfaces 888 developed by NVIDIA and/or PCIe
connections 886. In some examples, the GPUs 884 are connected via
NVLink and/or NVSwitch SoC and the GPUs 884 and the PCIe switches
882 are connected via PCIe interconnects. Although eight GPUs 884,
two CPUs 880, and two PCIe switches are illustrated, this is not
intended to be limiting. Depending on the embodiment, each of the
server(s) 878 may include any number of GPUs 884, CPUs 880, and/or
PCIe switches. For example, the server(s) 878 may each include
eight, sixteen, thirty-two, and/or more GPUs 884.
[0187] The server(s) 878 may receive, over the network(s) 890 and
from the vehicles, image data representative of images showing
unexpected or changed road conditions, such as recently commenced
road-work. The server(s) 878 may transmit, over the network(s) 890
and to the vehicles, neural networks 892, updated neural networks
892, and/or map information 894, including information regarding
traffic and road conditions. The updates to the map information 894
may include updates for the HD map 822, such as information
regarding construction sites, potholes, detours, flooding, and/or
other obstructions. In some examples, the neural networks 892, the
updated neural networks 892, and/or the map information 894 may
have resulted from new training and/or experiences represented in
data received from any number of vehicles in the environment,
and/or based on training performed at a datacenter (e.g., using the
server(s) 878 and/or other servers).
[0188] The server(s) 878 may be used to train machine learning
models (e.g., neural networks) based on training data. The training
data may be generated by the vehicles, and/or may be generated in a
simulation (e.g., using a game engine). In some examples, the
training data is tagged (e.g., where the neural network benefits
from supervised learning) and/or undergoes other pre-processing,
while in other examples the training data is not tagged and/or
pre-processed (e.g., where the neural network does not require
supervised learning). Training may be executed according to any one
or more classes of machine learning techniques, including, without
limitation, classes such as: supervised training, semi-supervised
training, unsupervised training, self learning, reinforcement
learning, federated learning, transfer learning, feature learning
(including principal component and cluster analyses), multi-linear
subspace learning, manifold learning, representation learning
(including spare dictionary learning), rule-based machine learning,
anomaly detection, and any variants or combinations therefor. Once
the machine learning models are trained, the machine learning
models may be used by the vehicles (e.g., transmitted to the
vehicles over the network(s) 890, and/or the machine learning
models may be used by the server(s) 878 to remotely monitor the
vehicles.
[0189] In some examples, the server(s) 878 may receive data from
the vehicles and apply the data to up-to-date real-time neural
networks for real-time intelligent inferencing. The server(s) 878
may include deep-learning supercomputers and/or dedicated AI
computers powered by GPU(s) 884, such as a DGX and DGX Station
machines developed by NVIDIA. However, in some examples, the
server(s) 878 may include deep learning infrastructure that use
only CPU-powered datacenters.
[0190] The deep-learning infrastructure of the server(s) 878 may be
capable of fast, real-time inferencing, and may use that capability
to evaluate and verify the health of the processors, software,
and/or associated hardware in the vehicle 800. For example, the
deep-learning infrastructure may receive periodic updates from the
vehicle 800, such as a sequence of images and/or objects that the
vehicle 800 has located in that sequence of images (e.g., via
computer vision and/or other machine learning object classification
techniques). The deep-learning infrastructure may run its own
neural network to identify the objects and compare them with the
objects identified by the vehicle 800 and, if the results do not
match and the infrastructure concludes that the AI in the vehicle
800 is malfunctioning, the server(s) 878 may transmit a signal to
the vehicle 800 instructing a fail-safe computer of the vehicle 800
to assume control, notify the passengers, and complete a safe
parking maneuver.
[0191] For inferencing, the server(s) 878 may include the GPU(s)
884 and one or more programmable inference accelerators (e.g.,
NVIDIA's TensorRT). The combination of GPU-powered servers and
inference acceleration may make real-time responsiveness possible.
In other examples, such as where performance is less critical,
servers powered by CPUs, FPGAs, and other processors may be used
for inferencing.
[0192] Example Computing Device
[0193] FIG. 9 is a block diagram of an example computing device(s)
900 suitable for use in implementing some embodiments of the
present disclosure. Computing device 900 may include an
interconnect system 902 that directly or indirectly couples the
following devices: memory 904, one or more central processing units
(CPUs) 906, one or more graphics processing units (GPUs) 908, a
communication interface 910, input/output (I/O) ports 912,
input/output components 914, a power supply 916, one or more
presentation components 918 (e.g., display(s)), and one or more
logic units 920.
[0194] Although the various blocks of FIG. 9 are shown as connected
via the interconnect system 902 with lines, this is not intended to
be limiting and is for clarity only. For example, in some
embodiments, a presentation component 918, such as a display
device, may be considered an I/O component 914 (e.g., if the
display is a touch screen). As another example, the CPUs 906 and/or
GPUs 908 may include memory (e.g., the memory 904 may be
representative of a storage device in addition to the memory of the
GPUs 908, the CPUs 906, and/or other components). In other words,
the computing device of FIG. 9 is merely illustrative. Distinction
is not made between such categories as "workstation," "server,"
"laptop," "desktop," "tablet," "client device," "mobile device,"
"hand-held device," "game console," "electronic control unit
(ECU)," "virtual reality system," and/or other device or system
types, as all are contemplated within the scope of the computing
device of FIG. 9.
[0195] The interconnect system 902 may represent one or more links
or busses, such as an address bus, a data bus, a control bus, or a
combination thereof. The interconnect system 902 may include one or
more bus or link types, such as an industry standard architecture
(ISA) bus, an extended industry standard architecture (EISA) bus, a
video electronics standards association (VESA) bus, a peripheral
component interconnect (PCI) bus, a peripheral component
interconnect express (PCIe) bus, and/or another type of bus or
link. In some embodiments, there are direct connections between
components. As an example, the CPU 906 may be directly connected to
the memory 904. Further, the CPU 906 may be directly connected to
the GPU 908. Where there is direct, or point-to-point connection
between components, the interconnect system 902 may include a PCIe
link to carry out the connection. In these examples, a PCI bus need
not be included in the computing device 900.
[0196] The memory 904 may include any of a variety of
computer-readable media. The computer-readable media may be any
available media that may be accessed by the computing device 900.
The computer-readable media may include both volatile and
nonvolatile media, and removable and non-removable media. By way of
example, and not limitation, the computer-readable media may
comprise computer-storage media and communication media.
[0197] The computer-storage media may include both volatile and
nonvolatile media and/or removable and non-removable media
implemented in any method or technology for storage of information
such as computer-readable instructions, data structures, program
modules, and/or other data types. For example, the memory 904 may
store computer-readable instructions (e.g., that represent a
program(s) and/or a program element(s), such as an operating
system. Computer-storage media may include, but is not limited to,
RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM,
digital versatile disks (DVD) or other optical disk storage,
magnetic cassettes, magnetic tape, magnetic disk storage or other
magnetic storage devices, or any other medium which may be used to
store the desired information and which may be accessed by
computing device 900. As used herein, computer storage media does
not comprise signals per se.
[0198] The computer storage media may embody computer-readable
instructions, data structures, program modules, and/or other data
types in a modulated data signal such as a carrier wave or other
transport mechanism and includes any information delivery media.
The term "modulated data signal" may refer to a signal that has one
or more of its characteristics set or changed in such a manner as
to encode information in the signal. By way of example, and not
limitation, the computer storage media may include wired media such
as a wired network or direct-wired connection, and wireless media
such as acoustic, RF, infrared and other wireless media.
Combinations of any of the above should also be included within the
scope of computer-readable media.
[0199] The CPU(s) 906 may be configured to execute at least some of
the computer-readable instructions to control one or more
components of the computing device 900 to perform one or more of
the methods and/or processes described herein. The CPU(s) 906 may
each include one or more cores (e.g., one, two, four, eight,
twenty-eight, seventy-two, etc.) that are capable of handling a
multitude of software threads simultaneously. The CPU(s) 906 may
include any type of processor, and may include different types of
processors depending on the type of computing device 900
implemented (e.g., processors with fewer cores for mobile devices
and processors with more cores for servers). For example, depending
on the type of computing device 900, the processor may be an
Advanced RISC Machines (ARM) processor implemented using Reduced
Instruction Set Computing (RISC) or an x86 processor implemented
using Complex Instruction Set Computing (CISC). The computing
device 900 may include one or more CPUs 906 in addition to one or
more microprocessors or supplementary co-processors, such as math
co-processors.
[0200] In addition to or alternatively from the CPU(s) 906, the
GPU(s) 908 may be configured to execute at least some of the
computer-readable instructions to control one or more components of
the computing device 900 to perform one or more of the methods
and/or processes described herein. One or more of the GPU(s) 908
may be an integrated GPU (e.g., with one or more of the CPU(s) 906
and/or one or more of the GPU(s) 908 may be a discrete GPU. In
embodiments, one or more of the GPU(s) 908 may be a coprocessor of
one or more of the CPU(s) 906. The GPU(s) 908 may be used by the
computing device 900 to render graphics (e.g., 3D graphics) or
perform general purpose computations. For example, the GPU(s) 908
may be used for General-Purpose computing on GPUs (GPGPU). The
GPU(s) 908 may include hundreds or thousands of cores that are
capable of handling hundreds or thousands of software threads
simultaneously. The GPU(s) 908 may generate pixel data for output
images in response to rendering commands (e.g., rendering commands
from the CPU(s) 906 received via a host interface). The GPU(s) 908
may include graphics memory, such as display memory, for storing
pixel data or any other suitable data, such as GPGPU data. The
display memory may be included as part of the memory 904. The
GPU(s) 908 may include two or more GPUs operating in parallel
(e.g., via a link). The link may directly connect the GPUs (e.g.,
using NVLINK) or may connect the GPUs through a switch (e.g., using
NVSwitch). When combined together, each GPU 908 may generate pixel
data or GPGPU data for different portions of an output or for
different outputs (e.g., a first GPU for a first image and a second
GPU for a second image). Each GPU may include its own memory, or
may share memory with other GPUs.
[0201] In addition to or alternatively from the CPU(s) 906 and/or
the GPU(s) 908, the logic unit(s) 920 may be configured to execute
at least some of the computer-readable instructions to control one
or more components of the computing device 900 to perform one or
more of the methods and/or processes described herein. In
embodiments, the CPU(s) 906, the GPU(s) 908, and/or the logic
unit(s) 920 may discretely or jointly perform any combination of
the methods, processes and/or portions thereof. One or more of the
logic units 920 may be part of and/or integrated in one or more of
the CPU(s) 906 and/or the GPU(s) 908 and/or one or more of the
logic units 920 may be discrete components or otherwise external to
the CPU(s) 906 and/or the GPU(s) 908. In embodiments, one or more
of the logic units 920 may be a coprocessor of one or more of the
CPU(s) 906 and/or one or more of the GPU(s) 908.
[0202] Examples of the logic unit(s) 920 include one or more
processing cores and/or components thereof, such as Tensor Cores
(TCs), Tensor Processing Units(TPUs), Pixel Visual Cores (PVCs),
Vision Processing Units (VPUs), Graphics Processing Clusters
(GPCs), Texture Processing Clusters (TPCs), Streaming
Multiprocessors (SMs), Tree Traversal Units (TTUs), Artificial
Intelligence Accelerators (AIAs), Deep Learning Accelerators
(DLAs), Arithmetic-Logic Units (ALUs), Application-Specific
Integrated Circuits (ASICs), Floating Point Units (FPUs),
input/output (I/O) elements, peripheral component interconnect
(PCI) or peripheral component interconnect express (PCIe) elements,
and/or the like.
[0203] The communication interface 910 may include one or more
receivers, transmitters, and/or transceivers that enable the
computing device 900 to communicate with other computing devices
via an electronic communication network, included wired and/or
wireless communications. The communication interface 910 may
include components and functionality to enable communication over
any of a number of different networks, such as wireless networks
(e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired
networks (e.g., communicating over Ethernet or InfiniBand),
low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or
the Internet.
[0204] The I/O ports 912 may enable the computing device 900 to be
logically coupled to other devices including the I/O components
914, the presentation component(s) 918, and/or other components,
some of which may be built in to (e.g., integrated in) the
computing device 900. Illustrative I/O components 914 include a
microphone, mouse, keyboard, joystick, game pad, game controller,
satellite dish, scanner, printer, wireless device, etc. The I/O
components 914 may provide a natural user interface (NUI) that
processes air gestures, voice, or other physiological inputs
generated by a user. In some instances, inputs may be transmitted
to an appropriate network element for further processing. An NUI
may implement any combination of speech recognition, stylus
recognition, facial recognition, biometric recognition, gesture
recognition both on screen and adjacent to the screen, air
gestures, head and eye tracking, and touch recognition (as
described in more detail below) associated with a display of the
computing device 900. The computing device 900 may be include depth
cameras, such as stereoscopic camera systems, infrared camera
systems, RGB camera systems, touchscreen technology, and
combinations of these, for gesture detection and recognition.
Additionally, the computing device 900 may include accelerometers
or gyroscopes (e.g., as part of an inertia measurement unit (IMU))
that enable detection of motion. In some examples, the output of
the accelerometers or gyroscopes may be used by the computing
device 900 to render immersive augmented reality or virtual
reality.
[0205] The power supply 916 may include a hard-wired power supply,
a battery power supply, or a combination thereof. The power supply
916 may provide power to the computing device 900 to enable the
components of the computing device 900 to operate.
[0206] The presentation component(s) 918 may include a display
(e.g., a monitor, a touch screen, a television screen, a
heads-up-display (HUD), other display types, or a combination
thereof), speakers, and/or other presentation components. The
presentation component(s) 918 may receive data from other
components (e.g., the GPU(s) 908, the CPU(s) 906, etc.), and output
the data (e.g., as an image, video, sound, etc.).
[0207] The disclosure may be described in the general context of
computer code or machine-useable instructions, including
computer-executable instructions such as program modules, being
executed by a computer or other machine, such as a personal data
assistant or other handheld device. Generally, program modules
including routines, programs, objects, components, data structures,
etc., refer to code that perform particular tasks or implement
particular abstract data types. The disclosure may be practiced in
a variety of system configurations, including hand-held devices,
consumer electronics, general-purpose computers, more specialty
computing devices, etc. The disclosure may also be practiced in
distributed computing environments where tasks are performed by
remote-processing devices that are linked through a communications
network.
[0208] As used herein, a recitation of "and/or" with respect to two
or more elements should be interpreted to mean only one element, or
a combination of elements. For example, "element A, element B,
and/or element C" may include only element A, only element B, only
element C, element A and element B, element A and element C,
element B and element C, or elements A, B, and C. In addition, "at
least one of element A or element B" may include at least one of
element A, at least one of element B, or at least one of element A
and at least one of element B. Further, "at least one of element A
and element B" may include at least one of element A, at least one
of element B, or at least one of element A and at least one of
element B.
[0209] The subject matter of the present disclosure is described
with specificity herein to meet statutory requirements. However,
the description itself is not intended to limit the scope of this
disclosure. Rather, the inventors have contemplated that the
claimed subject matter might also be embodied in other ways, to
include different steps or combinations of steps similar to the
ones described in this document, in conjunction with other present
or future technologies. Moreover, although the terms "step" and/or
"block" may be used herein to connote different elements of methods
employed, the terms should not be interpreted as implying any
particular order among or between various steps herein disclosed
unless and except when the order of individual steps is explicitly
described.
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