U.S. patent application number 16/863124 was filed with the patent office on 2021-11-04 for dynamic vibration sensor optics distortion prediction.
This patent application is currently assigned to Ford Global Technologies, LLC. The applicant listed for this patent is Ford Global Technologies, LLC. Invention is credited to Ronald Beras, David Michael Herman, Aaron Lesky.
Application Number | 20210344887 16/863124 |
Document ID | / |
Family ID | 1000005910287 |
Filed Date | 2021-11-04 |
United States Patent
Application |
20210344887 |
Kind Code |
A1 |
Herman; David Michael ; et
al. |
November 4, 2021 |
DYNAMIC VIBRATION SENSOR OPTICS DISTORTION PREDICTION
Abstract
The present disclosure discloses a system and a method for
mitigating image distortion. In an example implementation, the
system and the method can receive vehicle state data and vehicle
inertial measurement data; generate an image distortion prediction
indicative of image distortion within an image captured by the
image capture assembly based on the vehicle state data and the
vehicle inertial measurement data; and at least one of correct or
mitigate the image distortion based on the image distortion
prediction.
Inventors: |
Herman; David Michael; (Oak
Park, MI) ; Lesky; Aaron; (Ypsilanti, MI) ;
Beras; Ronald; (Warren, MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ford Global Technologies, LLC |
Dearborn |
MI |
US |
|
|
Assignee: |
Ford Global Technologies,
LLC
Dearborn
MI
|
Family ID: |
1000005910287 |
Appl. No.: |
16/863124 |
Filed: |
April 30, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 5/50 20130101; H04N
13/128 20180501; G06T 5/006 20130101; B60R 11/04 20130101; H04N
5/2252 20130101; H04N 2013/0096 20130101 |
International
Class: |
H04N 13/128 20060101
H04N013/128; H04N 5/225 20060101 H04N005/225; G06T 5/00 20060101
G06T005/00; G06T 5/50 20060101 G06T005/50; B60R 11/04 20060101
B60R011/04 |
Claims
1. A system comprising a computer including a processor and a
memory, the memory including instructions such that the processor
is programmed to: receive vehicle state data, vehicle inertial
measurement data, and strain data indicative of displacement on an
image capture assembly; generate an image distortion prediction
indicative of image distortion within an image captured by the
image capture assembly based on the vehicle state data, the vehicle
inertial measurement data, and the strain data; and at least one of
correct or mitigate image distortion within the image based on the
image distortion prediction.
2. The system of claim 1, wherein the processor is further
programmed to actuate a vehicle based on the image distortion
prediction.
3. The system of claim 1, wherein the image distortion prediction
includes at least one of a distortion type or a distortion
magnitude.
4. The system of claim 3, wherein the at least one of the
distortion type or the distortion magnitude comprises at least one
of an image translation, an image rotation, or an image distortion
error.
5. The system of claim 3, wherein the processor is further
programmed to mitigate the image distortion based on the image
distortion prediction by accessing a lookup table based on the at
least one of the distortion type or the distortion magnitude and
applying an image correction technique corresponding to the at
least one of the distortion type or the distortion magnitude.
6. The system of claim 1, wherein the processor is further
programmed to update a vehicle routing algorithm based on the image
distortion prediction.
7. (canceled)
8. The system of claim 1, further comprising the image capture
assembly disposed over a roof of a vehicle.
9. The system of claim 8, wherein the image capture assembly
comprises a housing including a camera.
10. The system of claim 9, further comprising a sensor disposed
within the housing.
11. The system of claim 10, wherein the sensor measures at least
one of strain data indicative of strain on the image capture
assembly or inertial measurement data of the image capture
assembly.
12. The system of claim 10, wherein the camera comprises a
stereoscopic camera, wherein the sensor is attached to a lens
assembly of at least one of a first camera or a second camera of
the stereoscopic camera.
13. The system of claim 1, wherein the processor is further
programmed to modify an image filter parameter of an image
perception algorithm based on the image distortion prediction.
14. The system of claim 1, wherein the processor is further
programmed to modify a vehicle speed and a vehicle course based on
the image distortion prediction.
15. A method comprising: receiving vehicle state data, vehicle
inertial measurement data, and strain data indicative of
displacement on an image capture assembly; generating an image
distortion prediction indicative of image distortion within an
image captured by the image capture assembly based on the vehicle
state data, the vehicle inertial measurement data, and the strain
data; and at least one of correcting or mitigating image distortion
within the image based on the image distortion prediction.
16. The method of claim 15, further comprising actuating a vehicle
based on the image distortion prediction.
17. The method of claim 16, wherein the image distortion prediction
includes at least one of a distortion type or a distortion
magnitude.
18. The method of claim 17, wherein the at least one of the
distortion type or the distortion magnitude comprises at least one
of an image translation, an image rotation, or an image distortion
error.
19. The method of claim 17, wherein the mitigating the image
distortion based on the image distortion prediction includes
accessing a lookup table based on the at least one of the
distortion type or the distortion magnitude and applying an image
correction technique corresponding to the at least one of the
distortion type or the distortion magnitude.
20. (canceled)
Description
BACKGROUND
[0001] Autonomous vehicles typically include various sensors that
provide information regarding the surrounding environment. In some
examples, these autonomous vehicles can include camera sensors,
radar sensors, and lidar sensors.
[0002] In some instances, the camera sensors may incorporate
stereoscopic vision, or multi-camera imaging, involves two or more
cameras having overlapping fields of view. By viewing the same
object or objects from different viewing angles, the observed
disparity between the positions of objects in respective ones of
the multiple views provides a basis for computing distances to
those objects. Some vehicle systems may use stereoscopic vision
imaging for the purposes of monitoring the surrounding
environment.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1 is a diagram of an example vehicle system in
accordance with an example implementation of the present
disclosure.
[0004] FIG. 2 is a diagram of an example vehicle including an image
capture assembly in accordance with an example implementation of
the present disclosure.
[0005] FIG. 3A is a block diagram of an example image capture
assembly in accordance with an example implementation of the
present disclosure.
[0006] FIG. 3B is an example diagram of an object detected by the
image capture assembly in which various forces on a lens assembly
of the image capture assembly cause distortion of the object within
the captured image.
[0007] FIG. 4 is a flow diagram illustrating an example process for
mitigating image distortion in accordance with an example
implementation of the present disclosure.
DETAILED DESCRIPTION
[0008] A system includes a computer including a processor and a
memory. The memory includes instructions such that the processor is
programmed to receive vehicle state data and vehicle inertial
measurement data; generate an image distortion prediction
indicative of image distortion within an image captured by the
image capture assembly based on the vehicle state data and the
vehicle inertial measurement data; and at least one of correct or
mitigate image distortion within the image based on the image
distortion prediction.
[0009] In other features, the processor is further programmed to
actuate a vehicle based on the image distortion prediction.
[0010] In other features, the image distortion prediction includes
at least one of a distortion type or a distortion magnitude.
[0011] In other features, the at least one of the distortion type
or the distortion magnitude comprises at least one of an image
translation, an image rotation, or an image distortion error.
[0012] In other features, the processor is further programmed to
mitigate the image distortion based on the image distortion
prediction by accessing a lookup table based on the at least one of
the distortion type or the distortion magnitude and applying an
image correction technique corresponding to the at least one of the
distortion type or the distortion magnitude.
[0013] In other features, the processor is further programmed to
update a vehicle routing algorithm based on the image distortion
prediction.
[0014] In other features, the processor is further programmed to
receive strain data associated with an image capture assembly,
wherein the strain data is indicative of strain on the image
capture assembly; and generate an image distortion prediction
indicative of image distortion within an image captured by the
image capture assembly based on the vehicle state data, the vehicle
inertial measurement data, and the strain data.
[0015] In other features, the system includes the image capture
assembly disposed over a roof of a vehicle.
[0016] In other features, the image capture assembly comprises a
housing including a camera.
[0017] In other features, the system includes a sensor disposed
within the housing.
[0018] In other features, the sensor measures at least one of the
strain data indicative of strain on the image capture assembly or
inertial measurement data of the image capture assembly.
[0019] In other features, the camera comprises a stereoscopic
camera, and the sensor is attached to a lens assembly of at least
one of a first camera or a second camera of the stereoscopic
camera.
[0020] In other features, the processor is further programmed to
modify an image filter parameter of an image perception algorithm
based on the image distortion prediction.
[0021] In other features, the processor is further programmed to
modify a vehicle speed and a vehicle course based on the image
distortion prediction.
[0022] A method includes receiving vehicle state data and vehicle
inertial measurement data; generating an image distortion
prediction indicative of image distortion within an image captured
by the image capture assembly based on the vehicle state data and
the vehicle inertial measurement data; and at least one of
correcting or mitigating image distortion within the image based on
the image distortion prediction.
[0023] In other features, the method further includes actuating a
vehicle based on the image distortion prediction.
[0024] In other features, the image distortion prediction includes
at least one of a distortion type or a distortion magnitude.
[0025] In other features, the at least one of the distortion type
or the distortion magnitude comprises at least one of an image
translation, an image rotation, or an image distortion error.
[0026] In other features, the mitigating the image distortion based
on the image distortion prediction includes accessing a lookup
table based on the at least one of the distortion type or the
distortion magnitude and applying an image correction technique
corresponding to the at least one of the distortion type or the
distortion magnitude.
[0027] In other features, the method further includes receiving
strain data associated with an image capture assembly, wherein the
strain data is indicative of force on the image capture assembly;
and generating an image distortion prediction indicative of image
distortion within an image captured by the image capture assembly
based on the vehicle state data, the vehicle inertial measurement
data, and the strain data.
[0028] Sensors, e.g. cameras, lidars, etc., often incorporate
optical elements, e.g. lenses, which act to improve the path of
light to or from a sensor or sub-component of a sensor, e.g.
photodiode, emitter, sensor array, etc. Such a sensor may often be
mounted onto a vehicle and operate while the vehicle undergoes
vibrational loading. Stable sensor data even under varying
vibrational loading is essential for use in automated and
semi-automated driving systems. Furthermore, multiple sensors
output may be compared in a sensor fusion process, stereoscopic
vision algorithm, or some other process.
[0029] Autonomous vehicles can employ perception algorithms, or
agents, to perceive the environment around the vehicle. These
vehicles can employ multiple sensors for perceiving aspects of the
surrounding environment. The perception algorithms use the sensor
data to determine whether one or more vehicle actions should be
modified based on the sensor data. For example, the perception
algorithms may update a routing algorithm such that the vehicle
alters course based on a sensed object within the environment. The
present disclosure discloses a system and a method for mitigating
image distortion associated with an image capture assembly of
vehicle.
[0030] FIG. 1 is a block diagram of an example vehicle system 100.
The system 100 includes a vehicle 105, which is a land vehicle such
as a car, truck, etc. The vehicle 105 includes a computer 110,
vehicle sensors 115, actuators 120 to actuate various vehicle
components 125, and a vehicle communications module 130. Via a
network 135, the communications module 130 allows the computer 110
to communicate with a server 145.
[0031] The computer 110 includes a processor and a memory. The
memory includes one or more forms of computer-readable media, and
stores instructions executable by the computer 110 for performing
various operations, including as disclosed herein.
[0032] The computer 110 may operate a vehicle 105 in an autonomous,
a semi-autonomous mode, or a non-autonomous (manual) mode. For
purposes of this disclosure, an autonomous mode is defined as one
in which each of vehicle 105 propulsion, braking, and steering are
controlled by the computer 110; in a semi-autonomous mode the
computer 110 controls one or two of vehicles 105 propulsion,
braking, and steering; in a non-autonomous mode a human operator
controls each of vehicle 105 propulsion, braking, and steering.
[0033] The computer 110 may include programming to operate one or
more of vehicle 105 brakes, propulsion (e.g., control of
acceleration in the vehicle by controlling one or more of an
internal combustion engine, electric motor, hybrid engine, etc.),
steering, climate control, interior and/or exterior lights, etc.,
as well as to determine whether and when the computer 110, as
opposed to a human operator, is to control such operations.
Additionally, the computer 110 may be programmed to determine
whether and when a human operator is to control such
operations.
[0034] The computer 110 may include or be communicatively coupled
to, e.g., via the vehicle 105 communications module 130 as
described further below, more than one processor, e.g., included in
electronic controller units (ECUs) or the like included in the
vehicle 105 for monitoring and/or controlling various vehicle
components 125, e.g., a powertrain controller, a brake controller,
a steering controller, etc. Further, the computer 110 may
communicate, via the vehicle 105 communications module 130, with a
navigation system that uses the Global Position System (GPS). As an
example, the computer 110 may request and receive location data of
the vehicle 105. The location data may be in a known form, e.g.,
geo-coordinates (latitudinal and longitudinal coordinates).
[0035] The computer 110 is generally arranged for communications on
the vehicle 105 communications module 130 and also with a vehicle
105 internal wired and/or wireless network, e.g., a bus or the like
in the vehicle 105 such as a controller area network (CAN) or the
like, and/or other wired and/or wireless mechanisms.
[0036] Via the vehicle 105 communications network, the computer 110
may transmit messages to various devices in the vehicle 105 and/or
receive messages from the various devices, e.g., vehicle sensors
115, actuators 120, vehicle components 125, a human machine
interface (HMI), etc. Alternatively or additionally, in cases where
the computer 110 actually comprises a plurality of devices, the
vehicle 105 communications network may be used for communications
between devices represented as the computer 110 in this disclosure.
Further, as mentioned below, various controllers and/or vehicle
sensors 115 may provide data to the computer 110.
[0037] Vehicle sensors 115 may include a variety of devices such as
are known to provide data to the computer 110. For example, the
vehicle sensors 115 may include Light Detection and Ranging (lidar)
sensor(s) 115, etc., disposed on a top of the vehicle 105, behind a
vehicle 105 front windshield, around the vehicle 105, etc., that
provide relative locations, sizes, and shapes of objects and/or
conditions surrounding the vehicle 105. As another example, one or
more radar sensors 115 fixed to vehicle 105 bumpers may provide
data to provide and range velocity of objects (possibly including
second vehicles 106), etc., relative to the location of the vehicle
105. The vehicle sensors 115 may further include camera sensor(s)
115, e.g. front view, side view, rear view, etc., providing images
from a field of view inside and/or outside the vehicle 105. The
vehicle sensors 115 may also include inertial measurement units
(IMUs) that measure force, angular rate, and/or an orientation
associated with the vehicle 105.
[0038] Within the present disclosure, the vehicle sensors 115 may
comprise active sensors and/or passive sensors. Active sensors,
such as lidar and radar sensors, project energy into a surrounding
environment and use measured energy reflections to interpret and/or
classify objects within the environment. Passive sensors, such as
cameras, do not project energy for the purposes of interpretation
and/or classification. Each type of sensor may employ optical
elements for the purposes of steering electromagnetic radiation,
e.g., light, for transmission and/or receiving purposes. In some
instances, errors or changes in optics may affect the perceived
image and/or point cloud received.
[0039] The vehicle 105 actuators 120 are implemented via circuits,
chips, motors, or other electronic and or mechanical components
that can actuate various vehicle subsystems in accordance with
appropriate control signals as is known. The actuators 120 may be
used to control components 125, including braking, acceleration,
and steering of a vehicle 105.
[0040] In the context of the present disclosure, a vehicle
component 125 is one or more hardware components adapted to perform
a mechanical or electro-mechanical function or operation--such as
moving the vehicle 105, slowing or stopping the vehicle 105,
steering the vehicle 105, etc. Non-limiting examples of components
125 include a propulsion component (that includes, e.g., an
internal combustion engine and/or an electric motor, etc.), a
transmission component, a steering component (e.g., that may
include one or more of a steering wheel, a steering rack, etc.), a
brake component (as described below), a park assist component, an
adaptive cruise control component, an adaptive steering component,
a movable seat, etc.
[0041] In addition, the computer 110 may be configured for
communicating via a vehicle-to-vehicle communication module or
interface 130 with devices outside of the vehicle 105, e.g.,
through a vehicle-to-vehicle (V2V) or vehicle-to-infrastructure
(V2X) wireless communications to another vehicle, to (typically via
the network 135) a remote server 145. The module 130 could include
one or more mechanisms by which the computer 110 may communicate,
including any desired combination of wireless (e.g., cellular,
wireless, satellite, microwave and radio frequency) communication
mechanisms and any desired network topology (or topologies when a
plurality of communication mechanisms are utilized). Exemplary
communications provided via the module 130 include cellular,
Bluetooth.RTM., IEEE 802.11, dedicated short range communications
(DSRC), and/or wide area networks (WAN), including the Internet,
providing data communication services.
[0042] The network 135 can be one or more of various wired or
wireless communication mechanisms, including any desired
combination of wired (e.g., cable and fiber) and/or wireless (e.g.,
cellular, wireless, satellite, microwave, and radio frequency)
communication mechanisms and any desired network topology (or
topologies when multiple communication mechanisms are utilized).
Exemplary communication networks include wireless communication
networks (e.g., using Bluetooth, Bluetooth Low Energy (BLE), IEEE
802.11, vehicle-to-vehicle (V2V) such as Dedicated Short-Range
Communications (DSRC), etc.), local area networks (LAN) and/or wide
area networks (WAN), including the Internet, providing data
communication services.
[0043] A computer 110 can receive and analyze data from sensors 115
substantially continuously, periodically, and/or when instructed by
a server 145, etc. Further, object classification or identification
techniques can be used, e.g., in a computer 110 based on lidar
sensor 115, camera sensor 115, etc., data, to identify a type of
object, e.g., vehicle, person, rock, pothole, bicycle, motorcycle,
etc., as well as physical features of objects.
[0044] FIG. 2 illustrates an example image capture assembly 202
attached to the vehicle 105. As shown, the image capture assembly
202 may be positioned over a roof of the vehicle 105. However, the
image capture assembly 202 may be located about the vehicle 105 in
other implementations. As explained in greater detail below, the
image capture assembly 202 captures images within a field of view
(FOV) 204 about an environment of the vehicle 105. The image
capture assembly 202 can include a housing 204 that houses, e.g.,
contains, the various components of the sensor apparatus. In one or
more implementations, the housing 204 may comprise a fiber
composite structure, a space frame structure, or the like.
[0045] As the vehicle 105 traverses a path, e.g., roadway, the
image capture assembly 202 captures images of an environment. For
instance, the image capture assembly 202 may capture images
including depictions of possible objects of interest within the
path of the vehicle, such as a pothole 208. The images are provided
to the computer 105 such that the computer 105 can classify objects
within the image and actuate the vehicle 105 in response to the
classification.
[0046] FIG. 3A is a block diagram illustrating an example image
capture assembly 202 according to an example implementation. The
sensor apparatus 202 is communicatively connected to the computer
110 and includes one or more cameras 302-1, 302-2. As a matter of
convenience, only one camera is illustrated. However, it is
understood that the image capture assembly 202 may include
additional cameras in other implementations. In one or more
implementations, the image capture assembly 202 may include
additional sensors, such as lidar sensors, that may utilize optics
in both light transmission and light receival. Each sensor's output
may be compared and/or fused with one another before or after
object detection. An example of low level sensor fusion before
object detection is multi-view imaging. For instance, the vehicle
system 100 can use various sensor fusion techniques to compare
and/or fuse the sensor output with one another. For instance, the
sensor fusion techniques may include, but are not limited to,
competitive sensor fusion techniques, complementary sensor fusion
techniques, and/or cooperative sensor fusion techniques.
[0047] As an example, each camera 302-1, 302-2 provides multi-view
imaging capability, e.g., stereoscopic imaging capability. For
instance, the cameras 302-1, 302-2 are operated as a stereo camera
pair. Each camera 302-1, 302-2 includes a lens assembly 304
including one or more lenses, an image sensor 306 that is placed in
optical alignment with the lens assembly 304, and an image
processor 308, which may be a pre-processor or other processing
circuit configured to operate the image sensor 306, provide
read-out of image sensor data, control exposure times, etc.
[0048] In another example, a lidar sensor projects electromagnetic
radiation into a FOV of the lidar sensor and measures the reflected
electromagnetic radiation. Processors associated with the lidar
sensor use the measured return times and wavelengths to generate a
three-dimensional representation of one or more objects within the
FOV. Similarly, lidar sensors use optics for the purposes of
focusing and/or receiving electromagnetic radiation.
[0049] The image capture assembly 202 also includes an image
processor 310, which may comprise one or more microprocessor-based,
DSP-based, ASIC-based, and/or FPGA-based circuits. In an
implementation, the image processor 310 comprises digital
processing circuitry that performs stereo image correlation
processing for stereo images as captured by the camera 302-1,
302-2. The image processor 310 can perform multi-view image
processing, such as generating depth maps and determining ranges to
objects within the imaged scene.
[0050] In an example implementation, the image processor receives
successive images, also referred to as "frames," from each of the
camera 302-1, 302-2. Here, a "frame" or "image" comprises the image
data, e.g., pixel data, from the image sensor for a given image
capture. For example, the image processor 310 receives a pair of
images, one from the first camera 302-1 and one from the camera
302-1, during each one in a succession of capture intervals. The
frame rate or capture rate determines the rate at which new images
are captured by the camera 302-1, 302-2.
[0051] The image processor 310 performs three-dimensional (3D)
ranging for the captured images, based on performing correlation
processing across corresponding image pairs from the cameras 302-1,
302-2. The cameras 302-1, 302-2 may be disposed along a horizontal
line, e.g. epipolar geometry, at some separation distance, for
operation as left-image and right-image cameras. The "disparity" or
displacement seen between the pixel position(s) in the left image
and the right image, for the same imaged pixel of an object or
feature, provides the basis for determining 3D ranging information,
as is understood by those of ordinary skill in the art. For
instance, in some implementations, grid and/or global search
algorithms may be improved with better camera image frame
alignment. The horizontal distance between the cameras 302-1, 302-2
may be referred to as a "baseline."
[0052] In one or more embodiments, the image processor 310 includes
or is associated with a storage device. The storage device will be
understood as comprising a type of computer-readable medium--e.g.,
FLASH memory or EEPROM--that provides non-transitory storage for a
computer program. The image processor 310 is adapted to carry out
the corresponding processing taught herein based on its execution
of computer program instructions.
[0053] The image capture assembly 202 further includes a
communication module 312 that communicatively connects the computer
110 to the image capture assembly 202, thereby allowing the image
capture assembly 202 to provide image data and/or derived object
detection data to the computer 110, and allowing the computer 110
to provide the image capture assembly 202 with computer-readable
instructions. The communication module 312 could include one or
more mechanisms by which the image capture assembly 202 may
communicate, including any desired combination of wireless (e.g.,
cellular, wireless, satellite, microwave and radio frequency)
communication mechanisms and any desired network topology (or
topologies when a plurality of communication mechanisms are
utilized). Exemplary communications provided via the communication
module 312 include cellular, Bluetooth.RTM., IEEE 802.11, dedicated
short range communications (DSRC), and/or wide area networks (WAN),
including the Internet, providing data communication services.
[0054] During operation, the image processor 310 and/or the
computer 110 processor estimates misalignments, e.g., mechanical
misalignments, in and/or between the cameras 302-1, 302-2, which is
described in greater detail below. The misalignments may be caused
by vehicle acceleration, the vehicle 105 experiencing a force
inducing event, and the like. As shown, one or more sensors 115 are
communicatively connected to the computer 110 via a Controller Area
Network (CAN) bus 320 such that the sensors 115 can provide vehicle
state data to the computer 110. For instance, the vehicle state
data can include, but is not limited to, vehicle acceleration,
vehicle speed, pedal position, engine revolutions-per-minute (RPM),
vehicle inertial measurement data, and the like.
[0055] As illustrated in FIG. 3A, the image capture assembly 202
also includes a sensor 316. In an example implementation, the
sensor 316 comprises a strain gauge that measures strain on an
object. The strain gauge can be a suitable strain sensor or related
sensor types that measure strain on a known geometry. For instance,
the strain gauge may include, but is not limited, optical fiber
strain gauges, mechanical strain gauges, or electrical strain
gauges. The sensor 316 may be mounted to the housing 204 to measure
the strain on the housing 204 and/or a body structure of the
vehicle 105. In an implementation, the image capture assembly 202
may also include a sensor 318 that is attached to one or both of
the cameras 302-1, 302-2 and/or camera optics, e.g., lens assembly.
For instance, the sensor 318 may be attached to the lens assembly
304 to measure forces on the lens assembly 304. In an example
implementation, the sensor 318 may comprise an inertial measurement
unit (IMU) that measures an acceleration, angular rate, and/or an
orientation associated with the cameras 302-1, 302-2. In another
example implementation, the sensor 318 may comprise a suitable
strain gauge that measures strain on the cameras 302-1, 302-2.
While only a single sensor 316 and a single sensor 318 are
illustrated, it is understood that the image capture assembly 202
can employ any number of sensors 318.
[0056] The image processor 310 and/or the processor of the computer
110 receive measurement data from the sensors 115, 316, 318 and
estimate lens assembly 304 accelerations and/or forces to generate
a displacement and stress prediction indicative of the displacement
and stress on the lens assembly 304. The image processor 310 and/or
the computer 110 processor can generate the displacement and stress
prediction based on suitable finite element analysis. For instance,
finite element analysis may use lens assembly geometry, boundary
conditions, material properties, inertial measurement data, vehicle
state data, and/or strain data associated with the vehicle 105
and/or the image capture assembly 202 to provide a displacement and
stress prediction through empirical testing and/or analysis.
Furthermore, discrete time steps of the finite element analysis may
further be interpolated or extrapolated to the corresponding time
frame of the camera's image capture, inclusive of the rolling
shutter frame by frame exposure time. Lastly, the finite element
model's prediction may be incorporated into a trained neural
network or other algorithm to improve and enable real time
prediction of the state of the lens assembly.
[0057] Based on the prediction, the image processor 310 and/or the
computer 110 processor generate a distortion prediction indicative
of a distortion of an image received by the cameras 302-1,
302-2.
[0058] In an implementation, the image processor 310 and/or the
computer 110 processor can use a lookup table relating predicted
lens displacement and stress to predicted image distortion. In
another implementation, the image processor 310 and/or the computer
110 processor can use machine learning techniques to predict image
distortion based on the predicted lens displacement and stress. The
machine learning techniques may be trained and/or the lookup table
may be programmed based on ray tracing optics simulation. The
output of the ray tracing optics simulation are image distortion
prediction(s). These image distortion predictions may include a
distortion type and/or distortion magnitude. For instance, the
distortion type and/or distortion magnitude include, but are not
limited to, an image translation, an image rotation, or an image
distortion error inclusive of defocus, tilt, spherical aberration,
Astigmatism, comatic aberration, shift of the image plane,
distortion (barrel, pincushion, mustache), Petzval field curvature,
chromatic aberration, point spread function, or the like. Within
the present disclosure, distortion may be defined as an optical
aberration, such as a deviation from rectilinear projection, which
a property of the optical systems causes light to be spread out
over some region of space rather than focused to a point.
[0059] FIG. 3B is a diagram illustrating an example object detected
by a sensor assembly, such as the image capture assembly 202. As
illustrated in steps (a)-(d) different impact loads cause the lens
assemblies 304 to change differently with respect to one another.
For instance, FIG. 3B-a illustrates barrel distortion of varying
magnitudes based on the respective lens assembly 304. As described
herein, the image processor 310 and/or the computer 110 correct or
mitigate image distortion within the image based on an image
distortion prediction.
[0060] In other examples, the image distortion associated with the
lens assembly 304 may be computed based on empirical testing in
conjunction with imaging of a calibration pattern under varying
time histories of amplitude, acceleration, frequency, and the like.
In some implementations, the machine learning techniques and/or the
lookup table may be initialized at the server 145 and provided to
the computer 110 via the network 135. However, it is understood
that the machine learning techniques and/or the lookup table may be
initialized at any suitable server and provided to the computer 110
via any suitable communication network.
[0061] The image processor 310 and/or the computer 110 processor
uses suitable computer vision techniques for the purposes of
identifying objects and/or object types within the FOV 204 of the
image capture assembly 202. Suitable computer vision techniques can
include, but are not limited to, computer vision algorithms or
machine learning techniques used for image processing for object
detection and/or object classification to allow an autonomous
vehicle to navigate its environment.
[0062] In some implementations, the image processor 310 and/or the
computer 110 processor correct and/or mitigate image distortion of
the received image according to the distortion type and/or
distortion magnitude, which results in an updated image. In some
implementations, the image processor 310 and/or the computer 110
processor apply image correction for certain distortion types
and/or distortion magnitudes. For instance, the image processor 310
and/or the computer 110 processor may use a lookup table relating
distortion types and/or distortion magnitudes to image correction
techniques and/or lidar point cloud correction techniques.
[0063] The image processor 310 and/or the computer 110 may use the
following equations to correct radial distortion associated with
the image:
x.sub.corrected=x(1+k.sub.1*r.sup.2+k.sub.2*r.sup.4+k.sub.3*r.sup.6)
Equation 1,
y.sub.corrected=y(1+k.sub.1*r.sup.2+k.sub.2*r.sup.4+k.sub.3*r.sup.6)
Equation 2,
[0064] where x.sub.corrected and y.sub.corrected represent
corrected pixel locations, x and y represent undistorted pixel
locations, k.sub.1, k.sub.2, and k.sub.3 represent radii distortion
coefficients of the lens assembly 304, and r.sup.2 represents
x.sup.2+y.sup.2.
[0065] The image processor 310 and/or the computer 110 may use the
following equations to correct tangential distortion associated
with the image:
x.sub.corrected=x+[2*p.sub.1*x*y+p.sub.2*(r.sup.2+2*x.sup.2)]
Equation 3,
y.sub.corrected=y+[p.sub.1*(r.sup.2+2*y.sup.2)+2*p.sub.2*x*y]
Equation 4,
[0066] where x.sub.corrected and y.sub.corrected represent
corrected pixel locations, x and y represent undistorted pixel
locations, k.sub.1, k.sub.2, and k.sub.3 represent tangential
distortion coefficients of the lens assembly 304, and r.sup.2
represents x.sup.2+y.sup.2.
[0067] In some implementation in which the received image cannot be
corrected based on the distortion type, distortion magnitude, a
characterization of a point spread function form, and/or
interactions amongst multiple distortion modes
(C1*contrast+C2*resolution+C3*contrast*resolution>threshold?),
the image processor 310 and/or the computer 110 processor update
image perception algorithms used to navigate the vehicle 105 based
on the received image(s). The variables C1, C2, and C3 can comprise
coefficients that weight and/or normalize distortion metrics with
respect to a predefined distortion threshold. The predefined
distortion threshold may be based on statistical evaluation of
camera distortion parameters relative to object detection accuracy,
false positive rate, R.sup.2, etc. For instance, the image
processor 310 and/or the computer 110 processor can bin the
received image to reduce the image size, modifying image filter
parameters, e.g., Gaussian, median, or bilateral image filters,
etc., or other computer vision workflow modification. The computer
110 may also initiate one or more vehicle 105 actions based on the
updated image, distortion type, and/or distortion magnitude. A
vehicle 105 action may include, but is not limited to, modifying
vehicle 105 speed, generating an alert, modifying a vehicle 105
course, and the like.
[0068] FIG. 4 is a flowchart of an exemplary process 400 for
mitigating image distortion. Blocks of the process 400 can be
executed by the computer 110 or the image processor 310. The
process 400 begins at block 405 in which a determination is made of
whether has been received from the image capture assembly 202. If
no image has been received, the process 400 returns to block 405.
In an example implementation, the computer 110 may apply static
distortion correction, e.g. barrel distortion correction, to the
received image from the static calibration process to the dynamic
loading correction prediction. Additionally or alternatively, the
computer 110 may apply quasi-static distortion parameter correction
to the received image. One example is temperature effects on the
lens distortion that may be characterized while static and possibly
incorporated into the finite element analysis (FEA) model where
some plastics change mechanical response (e.g. viscoelastic mode)
with temperature/loading rates which may be model as a Prony series
Otherwise, at block 410 in which vehicle state data is received.
Vehicle state data can include, but is not limited to, vehicle
acceleration, vehicle speed, pedal position, engine
revolutions-per-minute (RPM), inertial measurement data, and the
like.
[0069] At block 415, inertial measurement data associated with the
vehicle 105 is received. At block 420, strain data associated with
the vehicle 105 and/or the image capture assembly 202 is received.
At block 425, stress prediction for the image capture assembly 202,
e.g., cameras 302-1, 302-2 is generated. In an example
implementation, the camera assembly displacement and stress
prediction is generated using finite element analysis that uses the
vehicle state data, the inertial measurement data, and/or the
strain data as input.
[0070] At block 430, an image distortion prediction is generated
based on the stress prediction. For instance, a lookup table and/or
machine learning techniques can be used to relate the stress
prediction to the image distortion prediction. At block 435, image
distortion associated with received image is mitigated. In an
example implementation, the image processor 310 and/or the computer
110 processor can access a lookup table for image correction
techniques corresponding to the distortion types and/or distortion
magnitudes. In another example implementation, the image processor
310 and/or the computer 110 processor modify vehicle perception
algorithms to account for the distortion types and/or distortion
magnitudes.
[0071] At block 440, one or more vehicle actions are modified based
on the image distortion. In an example implementation, one or more
vehicle routing algorithms may be modified based on the image
distortion. For instance, a vehicle routing algorithm may be
updated to slow a speed of the vehicle 105 relative to its current
speed. In another instance, an alert may be generated to alert an
operator and/or passengers to the image distortion. At block 445,
the vehicle is actuated based on the modified vehicle actions. For
example, the computer 110 may cause the vehicle 105 to alter the
path of the vehicle 105 according to the updated vehicle routing
algorithm. In some instances, post processing techniques can be
executed to validate that the process 400 is operating. For
example, the post processing techniques may include comparing a
street sign before and after the vehicle 105 experiences a
force-inducing event, such as the vehicle 105 driving over an
object or driving through a pothole. The post processing techniques
may compare an image of the street sign before and after the
force-inducing event to ensure the comparison of the images is
within a predefined threshold, e.g., a sufficient amount of pixels
representing the street sign match.
[0072] In general, the computing systems and/or devices described
may employ any of a number of computer operating systems,
including, but by no means limited to, versions and/or varieties of
the Ford Sync.RTM. application, AppLink/Smart Device Link
middleware, the Microsoft Automotive.RTM. operating system, the
Microsoft Windows.RTM. operating system, the Unix operating system
(e.g., the Solaris.RTM. operating system distributed by Oracle
Corporation of Redwood Shores, Calif.), the AIX UNIX operating
system distributed by International Business Machines of Armonk,
N.Y., the Linux operating system, the Mac OSX and iOS operating
systems distributed by Apple Inc. of Cupertino, Calif., the
BlackBerry OS distributed by Blackberry, Ltd. of Waterloo, Canada,
and the Android operating system developed by Google, Inc. and the
Open Handset Alliance, or the QNX.RTM. CAR Platform for
Infotainment offered by QNX Software Systems. Examples of computing
devices include, without limitation, an on-board vehicle computer,
a computer workstation, a server, a desktop, notebook, laptop, or
handheld computer, or some other computing system and/or
device.
[0073] Computers and computing devices generally include
computer-executable instructions, where the instructions may be
executable by one or more computing devices such as those listed
above. Computer executable instructions may be compiled or
interpreted from computer programs created using a variety of
programming languages and/or technologies, including, without
limitation, and either alone or in combination, Java.TM., C, C++,
Matlab, Simulink, Stateflow, Intercal, Visual Basic, Java Script,
Perl, Python, HTML, etc. Some of these applications may be compiled
and executed on a virtual machine, such as the Java Virtual
Machine, the Dalvik virtual machine, or the like. In general, a
processor (e.g., a microprocessor) receives instructions, e.g.,
from a memory, a computer readable medium, etc., and executes these
instructions, thereby performing one or more processes, including
one or more of the processes described herein. Such instructions
and other data may be stored and transmitted using a variety of
computer readable media. A file in a computing device is generally
a collection of data stored on a computer readable medium, such as
a storage medium, a random-access memory, etc.
[0074] Memory may include a computer-readable medium (also referred
to as a processor-readable medium) that includes any non-transitory
(e.g., tangible) medium that participates in providing data (e.g.,
instructions) that may be read by a computer (e.g., by a processor
of a computer). Such a medium may take many forms, including, but
not limited to, non-volatile media and volatile media. Non-volatile
media may include, for example, optical or magnetic disks and other
persistent memory. Volatile media may include, for example, dynamic
random-access memory (DRAM), which typically constitutes a main
memory. Such instructions may be transmitted by one or more
transmission media, including coaxial cables, copper wire and fiber
optics, including the wires that comprise a system bus coupled to a
processor of an ECU. Common forms of computer-readable media
include, for example, a floppy disk, a flexible disk, hard disk,
magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other
optical medium, punch cards, paper tape, any other physical medium
with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM,
any other memory chip or cartridge, or any other medium from which
a computer can read.
[0075] Databases, data repositories or other data stores described
herein may include various kinds of mechanisms for storing,
accessing, and retrieving various kinds of data, including a
hierarchical database, a set of files in a file system, an
application database in a proprietary format, a relational database
management system (RDBMS), etc. Each such data store is generally
included within a computing device employing a computer operating
system such as one of those mentioned above, and are accessed via a
network in any one or more of a variety of manners. A file system
may be accessible from a computer operating system, and may include
files stored in various formats. An RDBMS generally employs the
Structured Query Language (SQL) in addition to a language for
creating, storing, editing, and executing stored procedures, such
as the PL/SQL language mentioned above. Some additional examples of
databases may include NoSQL and/or graph databases (GDB).
[0076] In some examples, system elements may be implemented as
computer-readable instructions (e.g., software) on one or more
computing devices (e.g., servers, personal computers, etc.), stored
on computer readable media associated therewith (e.g., disks,
memories, etc.). A computer program product may comprise such
instructions stored on computer readable media for carrying out the
functions described herein.
[0077] With regard to the media, processes, systems, methods,
heuristics, etc. described herein, it should be understood that,
although the steps of such processes, etc. have been described as
occurring according to a certain ordered sequence, such processes
may be practiced with the described steps performed in an order
other than the order described herein. It further should be
understood that certain steps may be performed simultaneously, that
other steps may be added, or that certain steps described herein
may be omitted. In other words, the descriptions of processes
herein are provided for the purpose of illustrating certain
embodiments, and should in no way be construed so as to limit the
claims.
[0078] Accordingly, it is to be understood that the above
description is intended to be illustrative and not restrictive.
Many embodiments and applications other than the examples provided
would be apparent to those of skill in the art upon reading the
above description. The scope of the invention should be determined,
not with reference to the above description, but should instead be
determined with reference to the appended claims, along with the
full scope of equivalents to which such claims are entitled. It is
anticipated and intended that future developments will occur in the
arts discussed herein, and that the disclosed systems and methods
will be incorporated into such future embodiments. In sum, it
should be understood that the invention is capable of modification
and variation and is limited only by the following claims.
[0079] All terms used in the claims are intended to be given their
plain and ordinary meanings as understood by those skilled in the
art unless an explicit indication to the contrary in made herein.
In particular, use of the singular articles such as "a," "the,"
"said," etc. should be read to recite one or more of the indicated
elements unless a claim recites an explicit limitation to the
contrary.
* * * * *