U.S. patent application number 17/338977 was filed with the patent office on 2021-10-28 for dynamic communications paths for sensors in an autonomous vehicle.
The applicant listed for this patent is GHOST LOCOMOTION INC.. Invention is credited to JOHN HAYES, VOLKMAR UHLIG.
Application Number | 20210334151 17/338977 |
Document ID | / |
Family ID | 1000005627880 |
Filed Date | 2021-10-28 |
United States Patent
Application |
20210334151 |
Kind Code |
A1 |
HAYES; JOHN ; et
al. |
October 28, 2021 |
DYNAMIC COMMUNICATIONS PATHS FOR SENSORS IN AN AUTONOMOUS
VEHICLE
Abstract
Dynamic communications paths for sensors in an autonomous
vehicle, comprising: detecting a fault associated with a first
sensor of a plurality of sensors associated with a same sensing
space of the autonomous vehicle; severing, in response to detecting
the fault, a first communications path in a switched fabric between
a processing unit and the first sensor; and establishing, via the
switched fabric, in response to detecting the fault, a second
communications path between the processing unit and a second sensor
of the plurality of sensors.
Inventors: |
HAYES; JOHN; (MOUNTAIN VIEW,
CA) ; UHLIG; VOLKMAR; (CUPERTINO, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
GHOST LOCOMOTION INC. |
MOUNTAIN VIEW |
CA |
US |
|
|
Family ID: |
1000005627880 |
Appl. No.: |
17/338977 |
Filed: |
June 4, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16388957 |
Apr 19, 2019 |
11030031 |
|
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17338977 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B60W 50/029 20130101;
B60W 50/0205 20130101; G06F 11/0721 20130101; B60W 50/04 20130101;
G06F 11/0739 20130101; B60W 2050/0215 20130101; B60W 2050/0292
20130101 |
International
Class: |
G06F 11/07 20060101
G06F011/07; B60W 50/04 20060101 B60W050/04; B60W 50/02 20060101
B60W050/02; B60W 50/029 20060101 B60W050/029 |
Claims
1. A method for dynamic communications paths for sensors in an
autonomous vehicle, comprising: detecting a fault associated with a
first sensor of a plurality of sensors associated with a same
sensing space of the autonomous vehicle; severing, in response to
detecting the fault, a first communications path in a switched
fabric between a processing unit and the first sensor; and
establishing, via the switched fabric, in response to detecting the
fault, a second communications path between the processing unit and
a second sensor of the plurality of sensors.
2. The method of claim 1, wherein detecting the fault associated
with the first sensor comprises determining, based on sensor data
received from the first sensor, that the first sensor is
faulty.
3. The method of claim 1, wherein detecting the fault associated
with the first sensor comprises detecting a loss of signal from the
first sensor.
4. The method of claim 1, wherein detecting the fault associated
with the first sensor comprises determining that the first signal
fails to respond to a message.
5. The method of claim 1, further comprising: receiving, before
severing the first communications path, via the first
communications path, first sensor data from the first sensor; and
receiving, after establishing the second communications path, via
the second communications path, second sensor data from the second
sensor.
6. The method of claim 1, wherein the second sensor comprises a
redundant sensor of the plurality of sensors.
7. An apparatus for dynamic communications paths for sensors in an
autonomous vehicle, the apparatus configured to perform steps
comprising: detecting a fault associated with a first sensor of a
plurality of sensors associated with a same sensing space of the
autonomous vehicle; severing, in response to detecting the fault, a
first communications path in a switched fabric between a processing
unit and the first sensor; and establishing, via the switched
fabric, in response to detecting the fault, a second communications
path between the processing unit and a second sensor of the
plurality of sensors.
8. The apparatus of claim 7, wherein detecting the fault associated
with the first sensor comprises determining, based on sensor data
received from the first sensor, that the first sensor is
faulty.
9. The apparatus of claim 7, wherein detecting the fault associated
with the first sensor comprises detecting a loss of signal from the
first sensor.
10. The apparatus of claim 7, wherein detecting the fault
associated with the first sensor comprises determining that the
first signal fails to respond to a message.
11. The apparatus of claim 7, further comprising: receiving, before
severing the first communications path, via the first
communications path, first sensor data from the first sensor; and
receiving, after establishing the second communications path, via
the second communications path, second sensor data from the second
sensor.
12. The apparatus of claim 7, wherein the second sensor comprises a
redundant sensor of the plurality of sensors.
13. An autonomous vehicle for dynamic communications paths for
sensors in an autonomous vehicle, the autonomous vehicle comprising
an apparatus configured to perform steps comprising: detecting a
fault associated with a first sensor of a plurality of sensors
associated with a same sensing space of the autonomous vehicle;
severing, in response to detecting the fault, a first
communications path in a switched fabric between a processing unit
and the first sensor; and establishing, via the switched fabric, in
response to detecting the fault, a second communications path
between the processing unit and a second sensor of the plurality of
sensors.
14. The autonomous vehicle of claim 13, wherein detecting the fault
associated with the first sensor comprises determining, based on
sensor data received from the first sensor, that the first sensor
is faulty.
15. The autonomous vehicle of claim 13, wherein detecting the fault
associated with the first sensor comprises detecting a loss of
signal from the first sensor.
16. The autonomous vehicle of claim 13, wherein detecting the fault
associated with the first sensor comprises determining that the
first signal fails to respond to a message.
17. The autonomous vehicle of claim 13, further comprising:
receiving, before severing the first communications path, via the
first communications path, first sensor data from the first sensor;
and receiving, after establishing the second communications path,
via the second communications path, second sensor data from the
second sensor.
18. The autonomous vehicle of claim 13, wherein the second sensor
comprises a redundant sensor of the plurality of sensors.
19. A computer program product disposed upon a non-transitory
computer readable medium, the computer program product comprising
computer program instructions for dynamic communications paths for
sensors in an autonomous vehicle that, when executed, cause a
computer system to carry out the steps of: detecting a fault
associated with a first sensor of a plurality of sensors associated
with a same sensing space of the autonomous vehicle; severing, in
response to detecting the fault, a first communications path in a
switched fabric between a processing unit and the first sensor; and
establishing, via the switched fabric, in response to detecting the
fault, a second communications path between the processing unit and
a second sensor of the plurality of sensors.
20. The computer program product of claim 19, wherein detecting the
fault associated with the first sensor comprises determining, based
on sensor data received from the first sensor, that the first
sensor is faulty.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of and claims priority
from U.S. Pat. No. 11,030,031, issued Jun. 8, 2021, which is hereby
incorporated by reference in its entirety.
BACKGROUND
Field of the Invention
[0002] The field of the invention is autonomous vehicles, or, more
specifically, methods, apparatus, autonomous vehicles, and products
for a redundant sensor fabric in an autonomous vehicle.
Description Of Related Art
[0003] Autonomous vehicles operate by detecting their environment
via one or more sensors, and making operational decisions based on
the state of the autonomous vehicle and the environment. Failure in
one of these sensors can degrade performance of the autonomous
vehicle, and may inhibit autonomous operation.
SUMMARY
[0004] A redundant sensor fabric in an autonomous vehicle may
include receiving, by a processing unit, sensor data from a first
sensor of a plurality of sensors associated with a same sensing
space of the autonomous vehicle; detecting a fault associated with
the first sensor; establishing, via a switched fabric, a
communications path between the processing unit and a second sensor
of the plurality of sensors; and receiving, by the processing unit,
sensor data from the second sensor instead of the first sensor.
[0005] The foregoing and other objects, features and advantages of
the invention will be apparent from the following more particular
descriptions of exemplary embodiments of the invention as
illustrated in the accompanying drawings wherein like reference
numbers generally represent like parts of exemplary embodiments of
the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 shows example views of an autonomous vehicle having a
redundant sensor fabric.
[0007] FIG. 2 is block diagram of an autonomous computing system
for a redundant sensor fabric in an autonomous vehicle.
[0008] FIG. 3 is a block diagram of a redundant power fabric for an
autonomous vehicle having a redundant sensor fabric.
[0009] FIG. 4 is a block diagram of a redundant data fabric for an
autonomous vehicle having a redundant sensor fabric.
[0010] FIG. 5 is an example view of process allocation across CPU
packages for a redundant sensor fabric in an autonomous
vehicle.
[0011] FIG. 6 is an example view of a redundant sensor fabric in an
autonomous vehicle.
[0012] FIG. 7 is a flowchart of an example method for a redundant
sensor fabric in an autonomous vehicle.
[0013] FIG. 8 is a flowchart of an example method for a redundant
sensor fabric in an autonomous vehicle.
[0014] FIG. 9 is a flowchart of an example method for a redundant
sensor fabric in an autonomous vehicle.
DETAILED DESCRIPTION
[0015] Example methods, apparatus, autonomous vehicles, and
products for a redundant sensor fabric in an autonomous vehicle are
described with reference to the accompanying drawings, beginning
with FIG. 1. FIG. 1 shows multiple views of an autonomous vehicle
100 configured with a redundant sensor fabric in accordance with
some embodiments of the present disclosure. Right side view 101a
shows a right side of the autonomous vehicle 100, where sensors 102
and 103 are mounted on or otherwise affixed to the right side of
the autonomous vehicle 100. The sensors 102 and 103 that are
mounted on or otherwise affixed to the autonomous vehicle 100 may
be configured to capture image data, video data, audio data, or any
other data (including combinations thereof) that can be used to
determine the environmental state of the autonomous vehicle 100
from the perspective of the right side of the autonomous vehicle
100.
[0016] Front view 101b shows a front side of the autonomous vehicle
100, where sensors 104 and 106 are mounted on or otherwise affixed
to the front side of the autonomous vehicle 100. The sensors 104
and 106 that are mounted on or otherwise affixed to the front side
of the autonomous vehicle 100 may be configured to capture image
data, video data, audio data, or any other data (including
combinations thereof) that can be used to determine the
environmental state of the autonomous vehicle 100 from the
perspective of the front side of the autonomous vehicle 100.
[0017] Rear view 101c shows a rear side of the autonomous vehicle
100, where sensors 108 and 110 are mounted on or otherwise affixed
to the rear side of the autonomous vehicle 100. The sensors 108 and
110 that are mounted on or otherwise affixed to the rear side of
the autonomous vehicle 100 may be configured to capture image data,
video data, audio data, or any other data (including combinations
thereof) that can be used to determine the environmental state of
the autonomous vehicle 100 from the perspective of the rear side of
the autonomous vehicle 100.
[0018] Top view 101d shows an overhead view of the autonomous
vehicle 100. Shown in the top view 101d are sensors 102-110 as
illustrated in previous views, as well as sensors 112 and 114 that
are mounted on or otherwise affixed to the left side of the
autonomous vehicle 100. The sensors 112 and 114 that are mounted on
or otherwise affixed to the left side of the autonomous vehicle 100
may be configured to capture image data, video data, audio data, or
any other data (including combinations thereof) that can be used to
determine the environmental state of the autonomous vehicle 100
from the perspective of the left side of the autonomous vehicle
100. Readers will appreciate that the placement of the sensors
102-114 is merely for illustrative purposes and in no way
represents a limitation on the arrangement of sensors, the manner
in which the sensors are attached to the autonomous vehicle 100,
and so on.
[0019] Further shown in the top view 101d is an automation
computing system 116. The automation computing system 116 can
include one or more computing devices configured to evaluate the
environmental state of the autonomous vehicle 100, control one or
more autonomous operations (e.g., autonomous driving operations) of
the autonomous vehicle 100 in coordination with other components of
the autonomous vehicle 100, and perform other tasks as will be
explained in greater detail below. In such an example, the one or
more computing devices may be configured to control one or more
autonomous operations of the autonomous vehicle 100 through the use
of one or more modules of computer program instructions that are
executing on one or more of the computing devices. For example, the
automation computing system 116 may be configured to process sensor
data (e.g., data from the sensors 102-114 and potentially other
sensors), operational data (e.g., a speed, acceleration, gear,
orientation, turning direction), and other data to determine an
operational state and/or operational history of the autonomous
vehicle. The automation computing system 116 may then determine one
or more operational commands for the autonomous vehicle (e.g., a
change in speed or acceleration, a change in brake application, a
change in gear, a change in turning or orientation, etc.) that may
be effected via coordination between the automation computing
system 116 and other components within the autonomous vehicle 100.
For example, the automation computing system 116 may be coupled,
directly or indirectly, for data communications with a device that
can control the operation of a steering system within the
autonomous vehicle 100. In such an example, if an analysis of
sensor data causes the automation computing system 116 to determine
that the direction of the autonomous vehicle 100 should be altered,
the automation computing system 116 may issue one or more commands
to the device that can control the operation of the steering system
within the autonomous vehicle 100, thereby causing the device that
can control the operation of a steering system within the
autonomous vehicle 100 to change the direction of the autonomous
vehicle by manipulating the steering system within the autonomous
vehicle 100. The automation computing system 116 may also capture
and store sensor data. Operational data of the autonomous vehicle
may also be stored in association with corresponding sensor data,
thereby indicating the operational data of the autonomous vehicle
100 at the time the sensor data was captured.
[0020] Although the autonomous vehicle 100 of FIG. 1 is shown as
car, it is understood that autonomous vehicles 100 in accordance
with embodiments of the present disclosure may also include other
vehicles, including motorcycles, planes, helicopters, unmanned
aerial vehicles (UAVs, e.g., drones), or other vehicles as can be
appreciated. Moreover, it is understood that additional sensors or
other external sensors may also be included in the autonomous
vehicle 100.
[0021] For further explanation, FIG. 2 sets forth a block diagram
of automated computing machinery comprising an exemplary automation
computing system 116 according to some embodiments of the present
disclosure. The automation computing system 116 of FIG. 2 includes
at least one computer Central Processing Unit (`CPU`) package 204
as well as random access memory 206 (`RAM`) which is connected
through a data communications link (e.g., a bus) to the CPU
packages 204 and to other components of the automation computing
system 116.
[0022] A CPU package 204 may comprise a plurality of processing
units. For example, each CPU package 204 may comprise a logical or
physical grouping of a plurality of processing units. Each
processing unit may be allocated a particular process for
execution. Moreover, each CPU package 204 may comprise one or more
redundant processing units. A redundant processing unit is a
processing unit not allocated a particular process for execution
unless a failure occurs in another processing unit. For example,
when a given processing unit allocated a particular process fails,
a redundant processing unit may be selected and allocated the given
process. A process may be allocated to a plurality of processing
units within the same CPU package 204 or different CPU packages
204. For example, a given process may be allocated to a primary
processing unit in a CPU package 204. The results or output of the
given process may be output from the primary processing unit to a
receiving process or service. The given process may also be
executed in parallel on a secondary processing unit. The secondary
processing unit may be included within the same CPU package 204 or
a different CPU package 204. The secondary processing unit may not
provide its output or results of the process until the primary
processing unit fails. The receiving process or service may then
receive data from the secondary processing unit and a redundant
processing unit may then be selected and have allocated the given
process to ensure that two or more processing units are allocated
the given process for redundancy and increased reliability.
[0023] The CPU packages 204 are communicatively coupled to one or
more sensors 212. The sensors 212 may be configured to capture
sensor data describing the operational and environmental conditions
of an autonomous vehicle. For example, the sensors 212 may include
sensors (e.g., the sensors 102-114 of FIG. 1), accelerometers,
Global Positioning System (GPS) radios, Lidar sensors, or other
sensors as can be appreciated. Although the sensors 212 are shown
as being external to the automation computing system 116, it is
understood that one or more of the sensors 212 may reside as a
component of the automation computing system 212 (e.g., on the same
board, within the same housing or chassis). The sensors 212 may be
communicatively coupled with the CPU packages 204 via a switched
fabric 213.
[0024] The switched fabric 213 comprises a communications topology
through which the CPU packages 204 and sensors 212 are coupled via
a plurality of switching mechanisms (e.g., latches, switches,
crossbar switches, field programmable gate arrays (FPGAs), etc.).
For example, the switched fabric 213 may implement a mesh
connection connecting the CPU packages 204 and sensors 212 as
endpoints, with the switching mechanisms serving as intermediary
nodes of the mesh connection. The CPU packages 204 and sensors 212
may be in communication via a plurality of switched fabrics 213.
For example, each of the switched fabrics 213 may include the CPU
packages 204 and sensors 212, or a subset of the CPU packages 204
and sensors 212, as endpoints. Each switched fabric 213 may also
comprise a respective plurality of switching components. The
switching components of a given switched fabric 213 may be
independent (e.g., not connected) of the switching components of
other switched fabrics 213 such that only switched fabric 213
endpoints (e.g., the CPU packages 204 and sensors 212) are
overlapping across the switched fabrics 213. This provides
redundancy such that, should a connection between a CPU package 204
and sensor 212 fail in one switched fabric 213, the CPU package 204
and sensor 212 may remain connected via another switched fabric
213. Moreover, in the event of a failure in a CPU package 204, a
processor of a CPU package 204, or a sensor, a communications path
excluding the failed component and including a functional redundant
component may be established.
[0025] The CPU packages 204 and sensors 212 are configured to
receive power from one or more power supplies 215. The power
supplies 215 may comprise an extension of a power system of the
autonomous vehicle 100 or an independent power source (e.g., a
battery). The power supplies 215 may supply power to the CPU
packages 204 and sensors 212 by another switched fabric 214. The
switched fabric 214 provides redundant power pathways such that, in
the event of a failure in a power connection, a new power
connection pathway may be established to the CPU packages 204 and
sensors 214.
[0026] Stored in RAM 206 is an automation module 220. The
automation module 220 may be configured to process sensor data from
the sensors 212 to determine one or more operational commands for
an autonomous vehicle 100 to affect the movement, direction, or
other function of the autonomous vehicle 100, thereby facilitating
autonomous driving or operation of the vehicle. Such operational
commands may include a change in the speed of the autonomous
vehicle 100, a change in steering direction, a change in gear, or
other command as can be appreciated. For example, the automation
module 220 may provide sensor data and/or processed sensor data as
one or more inputs to a trained machine learning model (e.g., a
trained neural network) to determine the one or more operational
commands. The operational commands may then be communicated to
autonomous vehicle control systems 223 via a vehicle interface 222.
The autonomous vehicle control systems 223 are configured to affect
the movement and operation of the autonomous vehicle 100. For
example, the autonomous vehicle control systems 223 may turn or
otherwise change the direction of the autonomous vehicle 100,
accelerate or decelerate the autonomous vehicle 100, change a gear
of the autonomous vehicle 100, or otherwise affect the movement and
operation of the autonomous vehicle 100.
[0027] Further stored in RAM 206 is a data collection module 224
configured to process and/or store sensor data received from the
one or more sensors 212. For example, the data collection module
224 may store the sensor data as captured by the one or more
sensors 212, or processed sensor data 212 (e.g., sensor data 212
having object recognition, compression, depth filtering, or other
processes applied). Such processing may be performed by the data
collection module 224 in real-time or in substantially real-time as
the sensor data is captured by the one or more sensors 212. The
processed sensor data may then be used by other functions or
modules. For example, the automation module 220 may use processed
sensor data as input to determine one or more operational commands.
The data collection module 224 may store the sensor data in data
storage 218.
[0028] Also stored in RAM 206 is a data processing module 226. The
data processing module 226 is configured to perform one or more
processes on stored sensor data (e.g., stored in data storage 218
by the data collection module 218) prior to upload to a server 227.
Such operations can include filtering, compression, encoding,
decoding, or other operations as can be appreciated. The data
processing module 226 may then communicate the processed and stored
sensor data to the server 227. Readers will appreciate that
although the embodiment depicted in FIG. 2 relates to an embodiment
where the data processing module 226 communicates the processed and
stored sensor data to the server 227, in other embodiments, the
data processing module 226 may communicate with other types of
environments such a cloud computing environment, data warehouse, or
any other endpoint that may receive data from the autonomous
vehicle 100.
[0029] Further stored in RAM 206 is a hypervisor 228. The
hypervisor 228 is configured to manage the configuration and
execution of one or more virtual machines 229. For example, each
virtual machine 229 may emulate and/or simulate the operation of a
computer. Accordingly, each virtual machine 229 may comprise a
guest operating system 216 for the simulated computer. The
hypervisor 228 may manage the creation of a virtual machine 229
including installation of the guest operating system 216. The
hypervisor 228 may also manage when execution of a virtual machine
229 begins, is suspended, is resumed, or is terminated. The
hypervisor 228 may also control access to computational resources
(e.g., processing resources, memory resources, device resources) by
each of the virtual machines.
[0030] Each of the virtual machines 229 may be configured to
execute one or more of the automation module 220, the data
collection module 224, the data processing module 226, or
combinations thereof. Moreover, as is set forth above, each of the
virtual machines 229 may comprise its own guest operating system
216. Guest operating systems 216 useful in autonomous vehicles in
accordance with some embodiments of the present disclosure include
UNIX.TM., Linux.TM., Microsoft Windows.TM., AIX.TM., IBM's iOS.TM.,
and others as will occur to those of skill in the art. For example,
the autonomous vehicle 100 may be configured to execute a first
operating system when the autonomous vehicle is in an autonomous
(or even partially autonomous) driving mode and the autonomous
vehicle 100 may be configured to execute a second operating system
when the autonomous vehicle is not in an autonomous (or even
partially autonomous) driving mode. In such an example, the first
operating system may be formally verified, secure, and operate in
real-time such that data collected from the sensors 212 are
processed within a predetermined period of time, and autonomous
driving operations are performed within a predetermined period of
time, such that data is processed and acted upon essentially in
real-time. Continuing with this example, the second operating
system may not be formally verified, may be less secure, and may
not operate in real-time as the tasks that are carried out (which
are described in greater detail below) by the second operating
system are not as time-sensitive the tasks (e.g., carrying out
self-driving operations) performed by the first operating
system.
[0031] Readers will appreciate that although the example included
in the preceding paragraph relates to an embodiment where the
autonomous vehicle 100 may be configured to execute a first
operating system when the autonomous vehicle is in an autonomous
(or even partially autonomous) driving mode and the autonomous
vehicle 100 may be configured to execute a second operating system
when the autonomous vehicle is not in an autonomous (or even
partially autonomous) driving mode, other embodiments are within
the scope of the present disclosure. For example, in another
embodiment one CPU (or other appropriate entity such as a chip, CPU
core, and so on) may be executing the first operating system and a
second CPU (or other appropriate entity) may be executing the
second operating system, where switching between these two
modalities is accomplished through fabric switching, as described
in greater detail below. Likewise, in some embodiments, processing
resources such as a CPU may be partitioned where a first partition
supports the execution of the first operating system and a second
partition supports the execution of the second operating
system.
[0032] The guest operating systems 216 may correspond to a
particular operating system modality. An operating system modality
is a set of parameters or constraints which a given operating
system satisfies, and are not satisfied by operating systems of
another modality. For example, a given operating system may be
considered a "real-time operating system" in that one or more
processes executed by the operating system must be performed
according to one or more time constraints. For example, as the
automation module 220 must make determinations as to operational
commands to facilitate autonomous operation of a vehicle.
Accordingly, the automation module 220 must make such
determinations within one or more time constraints in order for
autonomous operation to be performed in real time. The automation
module 220 may then be executed in an operating system (e.g., a
guest operating system 216 of a virtual machine 229) corresponding
to a "real-time operating system" modality. Conversely, the data
processing module 226 may be able to perform its processing of
sensor data independent of any time constrains, and may then be
executed in an operating system (e.g., a guest operating system 216
of a virtual machine 229) corresponding to a "non-real-time
operating system" modality.
[0033] As another example, an operating system (e.g., a guest
operating system 216 of a virtual machine 229) may comprise a
formally verified operating system. A formally verified operating
system is an operating system for which the correctness of each
function and operation has been verified with respect to a formal
specification according to formal proofs. A formally verified
operating system and an unverified operating system (e.g., one that
has not been formally verified according to these proofs) can be
said to operate in different modalities.
[0034] The automation module 220, data collection module 224, data
collection module 224, data processing module 226, hypervisor 228,
and virtual machine 229 in the example of FIG. 2 are shown in RAM
206, but many components of such software typically are stored in
non-volatile memory also, such as, for example, on data storage
218, such as flash storage. Moreover, any of the automation module
220, data collection module 224, and data processing module 226 may
be executed in a virtual machine 229 and facilitated by a guest
operating system 216 of that virtual machine 229.
[0035] The exemplary automation computing system 116 of FIG. 2
includes a communications adapter 238 for data communications with
other computers and for data communications with a data
communications network. Such data communications may be carried out
through data communications networks such as IP data communications
networks, and in other ways as will occur to those of skill in the
art. Communications adapters implement the hardware level of data
communications through which one computer sends data communications
to another computer, directly or through a data communications
network. Examples of communications adapters useful in autonomous
vehicle according to some embodiments of the present disclosure
include 802.11 adapters for wireless data communications, mobile
adapters (e.g., 4G communications adapters, LTE communications
adapters, 5G communications adapters) for mobile data
communications, and others. For example, the automation computing
system 116 may communicate with one or more remotely disposed
servers 227, or other communications endpoint as described above,
via the communications adapter 238.
[0036] The exemplary automation computing system of FIG. 2 also
includes one or more Artificial Intelligence (AI) accelerators 240.
The AI accelerator 240 provides hardware-based assistance and
acceleration of AI-related functions, including machine learning,
computer vision, etc. Accordingly, performance of any of the
automation module 220, data collection module 224, data processing
module 226, or other operations of the automation computing system
116 may be performed at least in part by the AI accelerators
240.
[0037] The exemplary automation computing system of FIG. 2 also
includes one or more graphics processing units (GPUs) 242. The GPUs
242 are configured to provide additional processing and memory
resources for processing image and/or video data, including
encoding, decoding, etc. Accordingly, performance of any of the
automation module 220, data collection module 224, data processing
module 226, or other operations of the automation computing system
116 may be performed at least in part by the GPUs 242.
[0038] FIG. 3 shows an example redundant power fabric for an
autonomous vehicle having a redundant processor fabric. The
redundant power fabric provides redundant pathways for power
transfer between the power supplies 215, the sensors 212, the CPU
packages 204, and may also be used for power transfer to other
components not explicitly depicted in FIG. 3. In this example, the
power supplies 215 are coupled to the sensors 212 and CPU packages
via two switched fabrics 214a and 214b, although additional
redundant resources may be incorporated in accordance with other
embodiments of the present disclosure. The topology shown in FIG. 3
provides redundant pathways between the power supplies 215, the
sensors 212, and the CPU packages 204 such that power can be
rerouted through any of multiple pathways in the event of a failure
in an active connection pathway. The switched fabrics 214a and 214b
may provide power to the sensors 212 using various connections,
including Mobile Industry Processor Interface (MIPI),
Inter-Integrated Circuit (I2C), Universal Serial Bus (USB), or
another connection. The switched fabrics 214a and 214b may also
provide power to the CPU packages 204 using various connections,
including Peripheral Component Interconnect Express (PCIe), USB, or
other connections. Although only two switched fabrics 214a and 214b
are shown connecting the power supplies 215 to the sensors 212 and
CPU packages 204, it is understood that the approach shown by FIG.
3 can be modified to include additional switched fabrics 214.
[0039] FIG. 4 is an example redundant data fabric for an autonomous
vehicle having a redundant processor fabric. The redundant data
fabric provides redundant data connection pathways between sensors
212 and CPU packages 204, and may also provide redundant data
connection pathways between other components not explicitly
depicted in FIG. 4. In this example view, three CPU packages 204a,
204b, and 204c are connected to three sensors 212a, 212b, and 212c
via three switched fabrics 213a, 213b, and 213c. Each CPU package
204a, 204b, and 204c is connected to a subset of the switched
fabrics 213a, 213b, and 213c. For example, CPU package 204a is
connected to switched fabrics 213a and 213c, CPU package 204b is
connected to switched fabrics 213a and 213b, and CPU package 204c
is connected to switched fabrics 213b and 213c. Each switched
fabric 213a, 213b, and 213c is connected to a subset of the sensors
212a, 212b, and 212c. For example, switched fabric 213a is
connected to sensors 212a and 212b, switched fabric 213b is
connected to sensor 212b and 212c, and switched fabric 213c is
connected to sensors 212a and 212c. Under this topology, each CPU
package 204a, 204b, and 204c has an available connection path to
any sensor 212a, 212b, and 212c. It is understood that the topology
of FIG. 4 is exemplary, and that CPU packages, switched fabrics,
sensors, or connections between components may be added or removed
while maintaining redundancy as can be appreciated by one skilled
in the art.
[0040] FIG. 5 is an example view of process allocation across CPU
packages for a redundant processing fabric in an autonomous
vehicle. Shown are three CPU packages 204a, 204b, and 204c. Each
CPU package 204a includes a processing unit that has been allocated
(e.g., by a hypervisor 228 or other process or service) primary
execution of a process and another processing unit that has been
allocated secondary execution of a process. As set forth herein,
primary execution of a process describes an executing instance of a
process whose output will be provided to another process or
service. Secondary execution of the process describes executing an
instance of the process in parallel to the primary execution, but
the output may not be output to the other process or service. For
example, in CPU package 204a, processing unit 502a has been
allocated secondary execution of "process B," denoted as secondary
process B 504b, while processing unit 502b has been allocated
primary execution of "process C," denoted as primary process C
506a.
[0041] CPU package 204a also comprises two redundant processing
units that are not actively executing a process A, B, or C, but are
instead reserved in case of failure of an active processing unit.
Redundant processing unit 508a has been reserved as "AB redundant,"
indicating that reserved processing unit 508a may be allocated
primary or secondary execution of processes A or B in the event of
a failure of a processing unit allocated the primary or secondary
execution of these processes. Redundant processing unit 508b has
been reserved as "A/C redundant," indicating that reserved
processing unit 508b may be allocated primary or secondary
execution of processes A or C in the event of a failure of a
processing unit allocated the primary or secondary execution of
these processes.
[0042] CPU package 204b includes processing unit 502c, which has
been allocated primary execution of "process A," denoted as primary
process A 510a, and processing unit 502d, which has been allocated
secondary execution of "process C," denoted as secondary process C
506a. CPU package 204b also includes redundant processing unit
508c, reserved as "AB redundant," and redundant processing unit
508d, reserved as "B/C redundant." CPU package 204c includes
processing unit 502e, which has been allocated primary execution of
"process B," denoted as primary process B 504a, and processing unit
502f, which has been allocated secondary execution of "process A,"
denoted as secondary process A 510a. CPU package 204c also includes
redundant processing unit 508e, reserved as "B/C redundant," and
redundant processing unit 508f, reserved as "A/C redundant."
[0043] As set forth in the example view of FIG. 5, primary and
secondary instances of processes A, B, and C are each executed in
an allocated processing unit. Thus, if a processing unit performing
primary execution of a given process fails, the processing unit
performing secondary execution may instead provide output of the
given process to a receiving process or service. Moreover, the
primary and secondary execution of a given process are executed on
different CPU packages. Thus, if an entire processing unit fails,
execution of each of the processes can continue using one or more
processing units handling secondary execution. The redundant
processing units 508a-f allow for allocation of primary or
secondary execution of a process in the event of processing unit
failure. This further prevents errors caused by processing unit
failure as parallel primary and secondary execution of a process
may be restored. One skilled in the art would understand that the
number of CPU packages, processing units, redundant processing
units, and processes may be modified according to performance
requirements while maintaining redundancy.
[0044] FIG. 6 is an example view of a redundant communications
pathway for a redundant sensor fabric in an autonomous vehicle.
Shown in this example are CPU packages 204a and 204b, which include
processing units 602a and 602b, respectively. Processing unit 602a
has been allocated execution of process 604a (e.g., by a hypervisor
228, or another process or service), and processing unit 602a has
been allocated execution of process 604b. It is understood that the
inclusion of processing units 602a and 602b is merely exemplary,
and that CPU packages 204a and 204b may also include additional
processing units, each of which may be allocated execution of
additional processes. CPU packages 204a and 204b also each include
redundant processing units 606a and 606b, respectively, which are
processing units not yet allocated execution of a particular
process.
[0045] CPU packages 204a and 204b are coupled to a switched fabric
213. The switched fabric 213 is also coupled to sensors 212a, 212b,
and one or more redundant sensors 212c. A redundant sensor 212c is
a sensor 212 whose output is not actively being processed (e.g.,
stored, encoded, or otherwise processed) by a process executed on a
CPU package. For example, a redundant sensor 212c may be
uninitialized, initialized but inactive, or otherwise redundant.
The sensors 212a and 212b, and redundant sensors 212c are each
associated with a same sensing space 608. A sensing space 608 is a
shared targeted area or attribute for capture by a sensor 212. For
example, a sensing space 608 for sensors 212 may comprise a same
targeted area, a same or similar focal line, a same or similar
field of view, or other similar area (e.g., installed on the same
side of an autonomous vehicle 100). As another example, a plurality
of accelerometer sensors 212 would share the sensing space 608 of
the acceleration of a particular body (e.g., an autonomous vehicle
100).
[0046] Processes 604a and 604b may comprise separately executed
instances of a same process for processing sensor data associated
with the same sensing space 608. For example, process 604a may be
configured to process sensor data from sensor 212a, while process
604b may be configured to process sensor data from sensor 212b.
Accordingly, the switched fabric 213 may comprise a first
communications pathway between the sensor 212a and the processing
unit 602a and a second communications pathway between the CPU
sensor 212b and the processing unit 602b.
[0047] In this example, output from processing unit 602a (e.g.,
from process 604a) may be sent to a receiving process (e.g., a
process of an automation module 220, a process of a data collection
module 224, or another process or service). Output from processing
unit 602b (e.g., from process 604b) may be sent to the receiving
process, but not actively acted upon by the receiving process. For
example, the receiving process may ignore or drop output from
processing unit 602b. Thus, processing units 602a and 602b process
sensor data in parallel, but the receiving process only acts upon
(e.g., processes) the output from the process 604a. In other words,
process 604a may comprise a primary execution of a given process,
while process 604b comprises a secondary execution of the given
process.
[0048] In this example, a fault associated with the sensor 212a may
be detected. Detecting a fault associated with the sensor may
comprise determining (e.g., by the processing unit 602a, by the
primary process 604a, or by another process or service) that the
sensor data from the sensor 212a is non-qualifiable. Sensor data
may be determined to be non-qualifiable when one or more functions
to be applied to the sensor data are unable to be completed. For
example, an object recognition, depth filtering, text recognition,
or other function may be applied (e.g., by the processing unit
602a, by the primary process 604b) to the sensor data. Such a
function may be considered incomplete, and therefore the sensor
data non-qualifiable, when no data may be generated from the sensor
data (e.g., no identified objects, no identified text). The sensor
data may also be considered non-qualifiable when an exception or
fault occurs when attempting to apply the given function. The
sensor data may further be considered non-qualifiable when one or
more data integrity functions (e.g., hash functions, cyclical
redundancy checks) fail when applied to the sensor data.
[0049] Detecting the fault associated with the sensor 212a may also
comprise comparing sensor data from the sensor 212a with sensor
data from at least one other sensor (e.g., the sensor 212b and/or
one or more redundant sensors 212c). Where sensor data from the
sensor 212a is compared to sensor data from a redundant sensor
212c, the redundant sensor 212c may be awoken from an inactive
state, or initialized and accessed for a period of time to
determine that the fault associated with the sensor 212a. For
example, on startup of an autonomous vehicle 100, while the
autonomous vehicle 100 is stationary, or in response to a user
command, a function may be initialized to compare sensor data from
sensors in a same sensing space to determine if a fault is present
in one of the sensors.
[0050] Detecting the fault associated with the sensor 212a may
comprise determining that the sensor 212a is obstructed by
identifying an object or other visual artifact in sensor data
(e.g., image data or video data) from the sensor 212a that is not
present in sensor data from the other sensors. For example,
detecting the fault associated with the sensor 212a may comprise
identifying a color differential between sensor data (e.g., image
data or video data) from the sensor 212a and sensor data from the
other sensors. For example, sensor data from the sensor 212a may
experience a yellow tinting or other color variation due to
degradation of a lens or other camera component. As another
example, sensor data from the sensor 212a may experience a color
variation in sensor data due to a semi-transparent or opaque
substance or material present on the sensor 212.
[0051] Detecting the fault associated with the sensor 212a may also
include determining a loss of a signal from the sensor 212a. For
example, the processing unit 602a may determine the fault
associated with the sensor when sensor data from the sensor 212a is
no longer received. As another example, a dedicated circuit of the
switching fabrics 213 may monitor channels of active communications
pathways and determine that no signal, or a weak signal, is being
communicated via the communications pathway from the sensor 212a.
Detecting the fault associated with the sensor 212a may also
include determining that the sensor 212a fails to respond to a
query, request, or message.
[0052] In response to detecting the fault associated with the
sensor 212a, a communications path can be established, via the
switched fabric 213, between the processing unit 602a and another
sensor. Establishing the communications path between the processing
unit 602a and the other sensor may comprise sending one or more
signals (e.g., by the processing unit 602a, by a circuit or process
monitoring communications paths of the switched fabric 213, by a
hypervisor 228 or by another entity) to one or more switching
components of the switched fabric 213 to establish the
communications path. Establishing the communications path between
the processing unit 602a and the other sensor may comprise sending
one or more signals (e.g., by the processing unit 602a, by a
circuit or process monitoring communications paths of the switched
fabric 213, by a hypervisor 228 or by another entity) to one or
more switching components of the switched fabric 213 to break,
remove, or otherwise circumvent the communications path between the
failed sensor 212a and the processing unit 602a.
[0053] The other sensor with which the communications path to the
processing unit 602a is established may comprise a redundant sensor
212c, thereby "activating" the redundant sensor 212c. Accordingly,
establishing the communications path may also comprise waking or
activating the redundant sensor 212. In this example, the switched
fabric 213 provides, before the fault is detected, a first
communications path between the sensor 212a and the processing unit
602a, and a second communications path between the sensor 212b and
the processing unit 604b. After the fault is detected and the
communications path established, the switched fabric 213 provides
the second communications path and a third communications path
between a redundant sensor 212c and the processing unit 602a.
[0054] The other sensor with which the communications path to the
processing unit 602a is established may comprise the sensor 212b
(e.g., a sensor 212 already providing sensor data to another
processing unit). For example, there may be no redundant sensors
212c available, or the redundant sensors 212c may themselves be
faulty. Accordingly, establishing the communications path may also
comprise duplicating, via the switched fabric 213, the sensor data
from the second sensor 212b for communication to the processing
unit 602a. Thus, processing units 602a and 602b (facilitated by
processes 604a and 604b) may then process sensor data from the
sensor 212b. This ensures that parallel processing of sensor data
associated with the sensing space 608 is maintained, providing
redundancy in the event of processing unit failure.
[0055] Communications paths may be established with additional
redundant sensors 212c in the event that faults with other active
sensors (e.g., the sensor 212b or activated redundant sensors 212c)
so long as at least one redundant sensor 212c is available for
activation. Once no redundant sensors 212c are available, sensor
data from an active sensor may then be duplicated and communicated
to the appropriate processing unit.
[0056] For further explanation, FIG. 7 sets forth a flow chart
illustrating an exemplary method for a redundant sensor fabric in
an autonomous vehicle that includes receiving 702, by a processing
unit (e.g., a processing unit 602a), sensor data from a first
sensor 212 (e.g., a sensor 212a). The processing unit may comprise
one of a plurality of processing units. For example, the processing
unit may be included in a plurality of processing units of a CPU
package 204. Moreover, the CPU package 204 may be one of a
plurality of CPU packages 204. The first sensor 212 may comprise
one of a plurality of sensors 212. The plurality of sensors 212 may
comprise one or more redundant sensors 212c.
[0057] The plurality of sensors 212 may be associated with a same
sensing space 608. For example, the plurality of sensors 212 may be
installed on a same side of an autonomous vehicle 100 (e.g., front
side, left side, right side, back side) and configured to capture
sensor data (e.g., images or video) from the perspective of the
given side. As another example, the plurality of sensors 212 may be
configured to detect a same attribute associate with the operation
of the autonomous vehicle 100 (e.g., speed, acceleration, GPS
location). The plurality of sensors 212 and the plurality of
processing units may be coupled via one or more switched fabrics
213.
[0058] The method of FIG. 7 further comprises detecting 704 a fault
associated with the first sensor 212. Detecting a fault associated
with the first sensor 212 may comprise determining (e.g., by the
processing unit, by a processes allocated for execution by the
processing unit, or by another process or service) that the sensor
data from the first sensor 212 is non-qualifiable. Sensor data may
be determined to be non-qualifiable when one or more functions to
be applied to the sensor data are unable to be completed. For
example, an object recognition, depth filtering, text recognition,
or other function may be applied (e.g., by the processing unit) to
the sensor data. Such a function may be considered incomplete, and
therefore the sensor data non-qualifiable, when no data may be
generated from the sensor data (e.g., no identified objects, no
identified text). The sensor data may also be considered
non-qualifiable when an exception or fault occurs when attempting
to apply the given function. The sensor data may further be
considered non-qualifiable when one or more data integrity
functions (e.g., hash functions, cyclical redundancy checks) fail
when applied to the sensor data.
[0059] Detecting the fault associated with the first sensor 212 may
also include determining a loss of a signal from the first sensor.
For example, the processing unit 602a may determine the fault
associated with the first sensor 212 when sensor data from the
first sensor 212 is no longer received. As another example, a
dedicated circuit of the switching fabrics 213 may monitor channels
of active communications pathways and determine that no signal, or
a weak signal, is being communicated via the communications pathway
from the first sensor 212. Detecting the fault associated with the
first sensor 212 may also include determining that the first sensor
212 fails to respond to a query, request, or message.
[0060] The method of FIG. 7 further comprises establishing 706, via
a switched fabric 213, a communications path between the processing
unit and a second sensor 212. Establishing the communications path
between the processing unit and the second sensor 212 may comprise
sending one or more signals (e.g., by the processing unit, by a
circuit or process monitoring communications paths of the switched
fabric 213, by a hypervisor 228 or by another entity) to one or
more switching components of the switched fabric 213 to establish
the communications path. Establishing the communications path
between the processing unit and the second sensor 212 may comprise
sending one or more signals (e.g., by the processing unit, by a
circuit or process monitoring communications paths of the switched
fabric 213, by a hypervisor 228 or by another entity) to one or
more switching components of the switched fabric 213 to break,
remove, or otherwise circumvent the communications path between the
failed first sensor 212 and the processing unit.
[0061] The other sensor with which the communications path to the
processing unit is established may comprise a redundant sensor
212c, thereby "activating" the redundant sensor 212c. Accordingly,
establishing the communications path may also comprise waking or
activating the redundant sensor 212. The method of FIG. 7 further
comprises receiving 708, by the processing unit, sensor data from
the second sensor 212 instead of the first sensor 212.
[0062] For further explanation, FIG. 8 sets forth a flow chart
illustrating an exemplary method for a redundant sensor fabric in
an autonomous vehicle that includes receiving 702, by a processing
unit, sensor data from a first sensor 212; detecting 704 a fault
associated with the first sensor 212; establishing 706, via a
switched fabric 213, a communications path between the processing
unit and a second sensor 212, and receiving 708, by the processing
unit, sensor data from the second sensor 212 instead of the first
sensor 212.
[0063] FIG. 8 differs from FIG. 7 in that detecting 704 the fault
associated with the first sensor 212 comprises comparing 802 sensor
data from the first sensor 212 and sensor data from at least one
other sensor 212. For example, sensor data from the first sensor
212 may be compared to sensor data from an active second sensor 212
providing sensor data to another processing unit. Sensor data from
the first sensor 212 may also be compared to sensor data from a
redundant sensor 212c. Where sensor data from the first sensor 212
is compared to sensor data from a redundant sensor 212c, the
redundant sensor 212c may be awoken from an inactive state, or
initialized and accessed for a period of time to determine that the
fault associated with the sensor 212a. For example, on startup of
an autonomous vehicle 100, while the autonomous vehicle 100 is
stationary, or in response to a user command, a function may be
initialized to compare sensor data from sensors in a same sensing
space to determine if a fault is present in one of the sensors.
[0064] Comparing sensor data from the first sensor 212 and sensor
data from at least one other sensor 212 may comprise determining
that the first sensor 212 is obstructed by identifying an object or
other visual artifact in sensor data (e.g., image data or video
data) from the first sensor 212 that is not present in sensor data
from the other sensors. Comparing sensor data from the first sensor
212 and sensor data from at least one other sensor 212 may comprise
identifying a color differential between sensor data (e.g., image
data or video data) from the first sensor 212 and sensor data from
the other sensors. For example, sensor data from the first sensor
212 may experience a yellow tinting or other color variation due to
degradation of a lens or other camera component. As another
example, sensor data from the first sensor 212 may experience a
color variation in sensor data due to a semi-transparent or opaque
substance or material present on the sensor 212.
[0065] For further explanation, FIG. 9 sets forth a flow chart
illustrating an exemplary method for a redundant sensor fabric in
an autonomous vehicle that includes receiving 702, by a processing
unit, sensor data from a first sensor 212; detecting 704 a fault
associated with the first sensor 212; establishing 706, via a
switched fabric 213, a communications path between the processing
unit and a second sensor 212, and receiving 708, by the processing
unit, sensor data from the second sensor 212 instead of the first
sensor 212.
[0066] FIG. 9 differs from FIG. 8 in that establishing 706, via the
switched fabric 213, a communications path between the processing
unit and a second sensor 212 comprises providing 902 sensor data
from the second sensor 212 to the processing unit and another
processing unit. For example, the other processing unit may
comprise a processing unit configured to process, prior to
detecting the fault in the first sensor 212, sensor data from the
second sensor 212 (e.g., in parallel to the processing unit
processing the sensor data from the first sensor 212). For example,
there may be no redundant sensors 212c available for coupling to
the processing unit in place of the first sensor 212, or the
redundant sensors 212c may themselves be faulty. Accordingly,
establishing the communications path may also comprise duplicating,
via the switched fabric 213, the sensor data from the second sensor
212b for communication to the processing unit and the other
processing unit. This ensures that parallel processing of sensor
data associated with the sensing space 608 is maintained, providing
redundancy in the event of processing unit failure.
[0067] In view of the explanations set forth above, readers will
recognize that the benefits of a redundant sensor fabric in an
autonomous vehicle according to embodiments of the present
invention include:
[0068] The ability to recover from sensor failure in an automated
vehicle in substantially real- time, thereby maintaining
performance of the automated vehicle.
[0069] The ability to preserve parallel sensor data processing
within a given sensing space, allowing for both redundancy and
reliability in the event of sensor failure or processor
failure.
[0070] Exemplary embodiments of the present invention are described
largely in the context of a fully functional computer system for a
redundant sensor fabric in an autonomous vehicle. Readers of skill
in the art will recognize, however, that the present invention also
may be embodied in a computer program product disposed upon
computer readable storage media for use with any suitable data
processing system. Such computer readable storage media may be any
storage medium for machine-readable information, including magnetic
media, optical media, or other suitable media. Examples of such
media include magnetic disks in hard drives or diskettes, compact
disks for optical drives, magnetic tape, and others as will occur
to those of skill in the art. Persons skilled in the art will
immediately recognize that any computer system having suitable
programming means will be capable of executing the steps of the
method of the invention as embodied in a computer program product.
Persons skilled in the art will recognize also that, although some
of the exemplary embodiments described in this specification are
oriented to software installed and executing on computer hardware,
nevertheless, alternative embodiments implemented as firmware or as
hardware are well within the scope of the present invention.
[0071] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0072] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0073] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0074] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0075] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0076] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0077] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0078] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0079] It will be understood that any of the functionality or
approaches set forth herein may be facilitated at least in part by
artificial intelligence applications, including machine learning
applications, big data analytics applications, deep learning, and
other techniques. Applications of such techniques may include:
machine and vehicular object detection, identification and
avoidance; visual recognition, classification and tagging;
algorithmic financial trading strategy performance management;
simultaneous localization and mapping; predictive maintenance of
high-value machinery; prevention against cyber security threats,
expertise automation; image recognition and classification;
question answering; robotics; text analytics (extraction,
classification) and text generation and translation; and many
others.
[0080] It will be understood from the foregoing description that
modifications and changes may be made in various embodiments of the
present invention without departing from its true spirit. The
descriptions in this specification are for purposes of illustration
only and are not to be construed in a limiting sense. The scope of
the present invention is limited only by the language of the
following claims.
* * * * *