On-board tuning of image signal processor for cameras of autonomous vehicles

In one embodiment, a system configures an image signal processor (ISP) of an autonomous driving vehicle (ADV) with a first set of ISP configuration parameters, where the ISP is used to process raw image data of an image sensor of the ADV based on the first set of ISP configuration parameters. The system determines whether one or more criteria is satisfied, where the one or more criteria corresponds to an expected change in a characteristic of ambient light being perceived by the image sensor of the ADV. In response to determining that the one or more criteria is satisfied, the system configures the ISP of the ADV with a second set of ISP configuration parameters, where the ISP is used to apply an image processing algorithm to raw image data based on the second set of ISP configuration parameters to generate an image.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operating autonomous driving vehicles. More particularly, embodiments of the disclosure relate to on-board tuning of image signal processor (ISP) for cameras of autonomous vehicles.

BACKGROUND

Image signal processors (ISP) are used to process image data for camera systems of autonomous driving vehicles (ADVs). ISP can be used to improve image quality of the images by applying ISP algorithms such as demosaic, white balancing, denoising, color correction, and so forth. However, the improvement to the image quality differs when the configuration settings of the ISP is suboptimal for the ambient light environment.

DETAILED DESCRIPTION

According to some embodiments, a camera system of an autonomous driving vehicle (ADV) uses information from a satellite navigation system (such as a global positioning system (GPS)) and/or map information to determine the expected ambient lighting conditions. The system configures an image signal processor (ISP) of the ADV with a set of selected configuration parameters for the expected ambient lighting conditions to improve the perception of the on-board cameras of the ADV. This way, the image quality of the on-board cameras is not degraded from a change in the ambient light conditions.

Ambient lighting is critical for camera systems of an autonomous driving vehicle (ADV) because lighting can affect the quality of the images being captured by the on-board cameras. When an autonomous driving vehicle (ADV) experiences significant changes to the ambient lighting conditions, such as before and after entering a road tunnel, the image quality of on-board cameras may degrade due to the changes in the ambient lighting conditions.

A previous implementation uses a same set of configuration parameters for the ISP of the ADV irrespective of the ambient light conditions of the ADV, whether or not the camera sensors of the ADV is operating at night or during the day, or experience different ambient light conditions.

According to an embodiment, a system configures an image signal processor (ISP) of an autonomous driving vehicle (ADV) with a first set of ISP configuration parameters, where the ISP is used to process raw image data of an image sensor of the ADV based on the first set of ISP configuration parameters. The system determines whether one or more criteria is satisfied, where the one or more criteria corresponds to an expected change in a characteristic of ambient light being perceived by the image sensor of the ADV. In response to determining that the one or more criteria is satisfied, the system configures the ISP of the ADV with a second set of ISP configuration parameters, where the ISP is used to apply an image processing algorithm to raw image data based on the second set of ISP configuration parameters to generate an image.

Referring now toFIG.2, in one embodiment, sensor system115includes, but it is not limited to, one or more cameras211, global positioning system (GPS) unit212, inertial measurement unit (IMU)213, radar unit214, and a light detection and range (LIDAR) unit215. GPS system212may include a transceiver operable to provide information regarding the position of the ADV. IMU unit213may sense position and orientation changes of the ADV based on inertial acceleration. Radar unit214may represent a system that utilizes radio signals to sense objects within the local environment of the ADV. In some embodiments, in addition to sensing objects, radar unit214may additionally sense the speed and/or heading of the objects. LIDAR unit215may sense objects in the environment in which the ADV is located using lasers. LIDAR unit215could include one or more laser sources, a laser scanner, and one or more detectors, among other system components. Cameras211may include one or more devices to capture images of the environment surrounding the ADV. Cameras211may be still cameras and/or video cameras. A camera may be mechanically movable, for example, by mounting the camera on a rotating and/or tilting a platform.

Some or all of the functions of ADV101may be controlled or managed by ADS110, especially when operating in an autonomous driving mode. ADS110includes the necessary hardware (e.g., processor(s), memory, storage) and software (e.g., operating system, planning and routing programs) to receive information from sensor system115, control system111, wireless communication system112, and/or user interface system113, process the received information, plan a route or path from a starting point to a destination point, and then drive vehicle101based on the planning and control information. Alternatively, ADS110may be integrated with vehicle control system111.

While ADV101is moving along the route, ADS110may also obtain real-time traffic information from a traffic information system or server (TIS). Note that servers103-104may be operated by a third party entity. Alternatively, the functionalities of servers103-104may be integrated with ADS110. Based on the real-time traffic information, MPOI information, and location information, as well as real-time local environment data detected or sensed by sensor system115(e.g., obstacles, objects, nearby vehicles), ADS110can plan an optimal route and drive vehicle101, for example, via control system111, according to the planned route to reach the specified destination safely and efficiently.

Server103may be a data analytics system to perform data analytics services for a variety of clients. In one embodiment, data analytics system103includes data collector121and machine learning engine122. Data collector121collects driving statistics123from a variety of vehicles, either ADVs or regular vehicles driven by human drivers. Driving statistics123include information indicating the driving commands (e.g., throttle, brake, steering commands) issued and responses of the vehicles (e.g., speeds, accelerations, decelerations, directions) captured by sensors of the vehicles at different points in time. Driving statistics123may further include information describing the driving environments at different points in time, such as, for example, routes (including starting and destination locations), MPOIs, road conditions, weather conditions, etc.

Based on driving statistics123, machine learning engine122generates or trains a set of rules, algorithms, and/or predictive models124for a variety of purposes. In one embodiment, algorithms124may include criteria when to reconfigure an ISP of the ADV to a different set of ISP configuration parameters. Different sets of ISP configuration parameters can be predetermined according to characteristics of the ambient light expected to be perceived by the ADV. Some characteristics include the color temperature or intensity of the light.

Algorithms124can then be uploaded on ADVs to be utilized during autonomous driving in real-time.

FIGS.3A and3Bare block diagrams illustrating an example of an autonomous driving system used with an ADV according to one embodiment. System300may be implemented as a part of ADV101ofFIG.1including, but is not limited to, ADS110, control system111, and sensor system115. Referring toFIGS.3A-3B, ADS110includes, but is not limited to, localization module301, perception module302, prediction module303, decision module304, planning module305, control module306, routing module307, and ISP parameters selector module308.

Based on the sensor data provided by sensor system115and localization information obtained by localization module301, a perception of the surrounding environment is determined by perception module302. The perception information may represent what an ordinary driver would perceive surrounding a vehicle in which the driver is driving. The perception can include the lane configuration, traffic light signals, a relative position of another vehicle, a pedestrian, a building, crosswalk, or other traffic related signs (e.g., stop signs, yield signs), etc., for example, in a form of an object. The lane configuration includes information describing a lane or lanes, such as, for example, a shape of the lane (e.g., straight or curvature), a width of the lane, how many lanes in a road, one-way or two-way lane, merging or splitting lanes, exiting lane, etc.

For each of the objects, prediction module303predicts what the object will behave under the circumstances. The prediction is performed based on the perception data perceiving the driving environment at the point in time in view of a set of map/route information311and traffic rules312. For example, if the object is a vehicle at an opposing direction and the current driving environment includes an intersection, prediction module303will predict whether the vehicle will likely move straight forward or make a turn. If the perception data indicates that the intersection has no traffic light, prediction module303may predict that the vehicle may have to fully stop prior to enter the intersection. If the perception data indicates that the vehicle is currently at a left-turn only lane or a right-turn only lane, prediction module303may predict that the vehicle will more likely make a left turn or right turn respectively.

For each of the objects, decision module304makes a decision regarding how to handle the object. For example, for a particular object (e.g., another vehicle in a crossing route) as well as its metadata describing the object (e.g., a speed, direction, turning angle), decision module304decides how to encounter the object (e.g., overtake, yield, stop, pass). Decision module304may make such decisions according to a set of rules such as traffic rules or driving rules312, which may be stored in persistent storage device352.

Routing module307is configured to provide one or more routes or paths from a starting point to a destination point. For a given trip from a start location to a destination location, for example, received from a user, routing module307obtains route and map information311and determines all possible routes or paths from the starting location to reach the destination location. Routing module307may generate a reference line in a form of a topographic map for each of the routes it determines from the starting location to reach the destination location. A reference line refers to an ideal route or path without any interference from others such as other vehicles, obstacles, or traffic condition. That is, if there is no other vehicle, pedestrians, or obstacles on the road, an ADV should exactly or closely follows the reference line. The topographic maps are then provided to decision module304and/or planning module305. Decision module304and/or planning module305examine all of the possible routes to select and modify one of the most optimal routes in view of other data provided by other modules such as traffic conditions from localization module301, driving environment perceived by perception module302, and traffic condition predicted by prediction module303. The actual path or route for controlling the ADV may be close to or different from the reference line provided by routing module307dependent upon the specific driving environment at the point in time.

Based on the planning and control data, control module306controls and drives the ADV, by sending proper commands or signals to vehicle control system111, according to a route or path defined by the planning and control data. The planning and control data include sufficient information to drive the vehicle from a first point to a second point of a route or path using appropriate vehicle settings or driving parameters (e.g., throttle, braking, steering commands) at different points in time along the path or route.

Note that decision module304and planning module305may be integrated as an integrated module. Decision module304/planning module305may include a navigation system or functionalities of a navigation system to determine a driving path for the ADV. For example, the navigation system may determine a series of speeds and directional headings to affect movement of the ADV along a path that substantially avoids perceived obstacles while generally advancing the ADV along a roadway-based path leading to an ultimate destination. The destination may be set according to user inputs via user interface system113. The navigation system may update the driving path dynamically while the ADV is in operation. The navigation system can incorporate data from a GPS system and one or more maps so as to determine the driving path for the ADV.

FIG.4is a block diagram illustrating an example of an ISP configuration parameters selection system400according to an embodiment. ISP configuration parameters selection system400can configure the ISP parameters of an ISP in real-time while an ADV is in operation.

In one embodiment, ISP configuration parameters selection system400includes localization module301, ISP parameters selector module308, cameras211, ISP401, and perception module302. ISP parameters selector module308can receive a reference time and/or location information from localization module301. The reference time and location information may be received from a satellite navigation system, such as GPS, GLONASS, Beidou, or Galileo system. The reference time can be received from a satellite, where the satellite maintains the reference time as an atomic clock of the satellite, and/or the reference time is maintained by an on-board clock/timer at the ADV. In one embodiment, module308receives map data from map and route data311.

In one embodiment, using the reference time, location, and/or map data, ISP parameter selector module308determines if one or more criteria are met and selects a set of ISP configuration parameters403for configuration corresponding to the met criteria. The ISP configuration parameters can correspond to adjustments (tuning) of the ISP to optimize the generation of the image for autofocus (AF), auto exposure (AE), and/or auto white balance (AWB). The ISP configuration parameters are used by the ISP algorithms that are applied to raw image data captured by cameras to generate images.

In one embodiment module308can configure ISP401in real-time using the ISP configuration parameters403. Once configured, when camera(s)211captures raw image data, the raw image data is applied an ISP algorithm using the selected set of ISP configuration parameters403to generate an image. The ISP algorithm can include any one or a combination of: image format conversion, image resize, exposure adjustment, black level adjustment, color adjustment, pixel correction, color interpolation, and/or de-noise algorithms. The ISP algorithm can be directed to generate the images for machine learning (ML) applications executed by perception module302. Here, applying the ISP algorithm processes and prepares the generated images to be used by perception module302.

Once an image is received by perception module302, the image is used by perception module302for object detection, object classification, or other machine learning algorithms to perceive an environment of the ADV. An example of a ML algorithm includes applying a deep convolutional neural network to the generated image for object detection.

Here, the quality of the generated images is affected by the ISP configuration parameters and the ISP configuration parameters are selected according to certain criteria. Examples of some criteria are shown inFIG.6Aaccording to one embodiment. Note that the criteria and corresponding parameter sets/priority, shown inFIG.6A, can be stored in persistent memory (such as ISP parameters sets/priority313) of ADV101.

The criteria can be evaluated by ADV101in response to a change in criteria (or time, location of ADV101). For example, when the criteria for a certain time of day (e.g., 7 AM-7 PM) is met, the ISP configuration parameters set A can be used to configure ISP401ofFIG.4. When the time of day is 7 PM-10 PM, the ISP configuration parameters set B can be used to configure ISP401. When the criteria when the ADV is about to enter a road tunnel of type1, the ISP configuration parameters set C can be used to configure ISP401. When the criteria when the ADV is about to enter a road tunnel of type2, the ISP configuration parameters set C can be used to configure ISP401. When the criteria that the heading direction of the ADV101is facing the direction of the sun, the ISP configuration parameters set D can be used to configure ISP401.

Different criteria can have different priorities according to some embodiments. For example, the criteria when ADV101heading direction is facing the direction of the sun has a priority of 1, while the time of day criteria has a lower priority of 3. Thus, when more than one conditions are met, module308applies the ISP configuration parameters corresponding to the criteria with the highest priority, e.g., set D is applied instead of set A since set D corresponds to a higher priority of 1 and set A corresponds to a lower priority of 3. Note that only some criteria is shown for purposes of illustration inFIG.6Aand the list of criteria can further include weather patterns, fire, or any other conditions/events that can affect the characteristics of the ambient lighting condition of the ADV.

FIG.5is a block diagram illustrating a scenario500to select and configure an ISP of an ADV according to an embodiment. In scenario500, the time of day can be 12:01 PM and an ISP of ADV101can be configured with parameter set A according to the criteria that the time of day of 12:01 PM between 7 AM-7 PM is met. Referring toFIG.5, scenario500further denotes that ADV101is entering an entrance of road tunnel501having a tunnel length of distance503and the road outside and inside of road tunnel501has four lanes.

The four lanes are separated by a double yellow lane line505for the two opposite driving directions of the road, where each direction is separated by a single white lane line507. Here, while the white and yellow lane lines can appear as white and yellow lane lines respectively from the perception of the human observer, the white and yellow lane lines may be transformed by an ISP algorithm of an ISP of ADV101to other colors so to further contrast the white from the yellow coloring of the lane lines. Such an ISP algorithm applied to the generated images improves an inference accuracy of the ML algorithms when the images are used for the ML algorithms.

Referring toFIG.5, in one embodiment, the entrance to road tunnel501can be represented by zone509, where zones509and511are stored as coordinates (and tunnel types) in map data311specifying the entrances to tunnel501. In another embodiment, the coordinates of zones509and511(and tunnel types, e.g., tunnel type1) can be stored in a table as shown inFIG.6B. Here, ADV101is considered to be in zone509when ADV101is within a threshold distance to the coordinate of zone509.

In one embodiment, when ADV101enters zone509, an ISP parameters selector module308of ADV101evaluates that the criteria of ADV101entering a road tunnel of type1 is met. When ADV101determines that such a criteria is met, ADV101determines the corresponding tunnel type to be type1 and selects the ISP configuration parameters set C for tunnel type1. ADV101then configures an ISP of ADV101using the selected parameters set C. Thus, after ADV101enters tunnel, ADV101can quickly adapt to the ambient lighting conditions inside tunnel501because the ambient light inside tunnel501is predetermined to correspond to the parameters set C.

In one embodiment, the different tunnel types can correspond to different color temperatures and/or intensities of the expected ambient light inside the corresponding tunnels. For example, tunnel type1can correspond to yellow incandescent lighting inside tunnel501, while tunnel type2can correspond to no lighting inside a corresponding tunnel. Here, color temperature refers to the temperature of an ideal black-body radiator that radiates light of a color comparable to that of a light source. It is a system of numerical values to measure the color characteristics of the light source ranging from warm (e.g., yellow/red) to cool colors (e.g., blue). Light intensity refers to a measure of the amount of light (lumens) falling on a surface. While two types of tunnels are illustrated inFIG.6A, various ambient lighting conditions can be configured for additional tunnel types.

In one embodiment, since ADV101has the ISP adapted to the ambient light inside tunnel501, the image generated by ADV101can apply an ISP algorithm to the raw image data to generate an image, where the ISP algorithm utilizes the configured ISP configuration parameters and causes a different image to be generated than if the ISP algorithm utilizes a different set of ISP configuration parameters.

Next, the generated image can then be used by a perception module and different machine learning algorithms can be applied to the images to identify and classify obstacles in the images accordingly. Had the ISP settings remained unchanged, the difference in ambient light condition may cause an inference error and the perception module may fail to identify the obstacles, e.g., a white lane line might be identified as a yellow lane line due to characteristics of the ambient lighting (the temperature color and/or intensity) being different inside and outside of tunnel501.

In one embodiment, ADV101further uses map data311to determine the length of the tunnel to be distance503and determines when ADV101has traveled a distance that is equal to distance503. The distance traveled can be determined according to vehicle dynamics, e.g., the distance traveled is equal to the velocity of the vehicle multiplied by the time traveled. Once the ADV101has determined that ADV101has traveled a distance equal to the tunnel length, ADV101configures the ISP back to parameter set A, e.g., prior to entering tunnel501. This way, the ISP is once again adapted to the ambient light conditions outside of tunnel501.

In another embodiment, when ADV101enters zone509and ADV101determines a heading direction of ADV101is facing the sun511, ADV101determines two configuration criteria are met. In this case, ADV101determines a highest priority from the competing criteria. ADV101then applies the ISP configuration parameters set according to the highest priority (e.g., parameter set E has a higher priority of 1 in comparison with parameter set C of priority 2) from the competing criteria.

FIG.7is a flow diagram illustrating a process to select a set of ISP configuration parameters for an ISP according to an embodiment. Process700may be performed by processing logic which may include software, hardware, or a combination thereof. For example, process700may be performed by ISP parameters selector module308ofFIG.3C.

Referring toFIG.7, at block701, processing logic configures an image signal processor (ISP) of an autonomous driving vehicle (ADV)101with a first set of ISP configuration parameters (e.g., set A), where the ISP (e.g., ISP401ofFIG.4) is used to process raw image data of an image sensor (e.g., camera(s)211ofFIG.4) of the ADV101based on the first set of ISP configuration parameters (e.g., set A according to the time of day criteria).

At block703, processing logic determines whether one or more criteria is satisfied, wherein the one or more criteria corresponds to an expected change in a characteristic of ambient light being perceived by the image sensor of the ADV.

At block705, in response to determining that the one or more criteria (e.g., criteria C, D, E, etc.) is satisfied, processing logic configures the ISP of the ADV with a second set of ISP configuration parameters (e.g., set C, set D, set E, etc.), where the ISP is used to apply an image processing algorithm (ISP algorithm) to raw image data based on the second set of ISP configuration parameters to generate an image.

In one embodiment, determining whether the one or more criteria is satisfied includes receiving a signal from a satellite-based navigation system, determining a location of the ADV based on the received signal, and determining the location is within a tolerance distance (e.g., 5 meters) to one of a plurality of predetermined locations (e.g., zones509,511) corresponding to an entrance of a road tunnel based on map data (e.g., map data311).

In one embodiment, processing logic further determines the ADV has reached an end of the road tunnel. In response to determining the ADV has reached the end of the road tunnel, processing logic configures the ISP with the first set of ISP configuration parameters (e.g., set A), where the ISP is used to apply an image processing algorithm to raw image data based on the first set of ISP configuration parameters to generate an image.

In one embodiment, determining the ADV has reached the end of the road tunnel includes determining a distance (e.g., distance503) between the entrance of the road tunnel and the end of the road tunnel and a distance traveled by the ADV based on vehicle dynamics of the ADV.

In one embodiment, determining whether the one or more criteria is satisfied includes determining a time of day, determining a heading direction of the ADV, and determining whether the ADV is facing a direction of a sun based on the time of day and the heading direction of the ADV.

In one embodiment, the first set of ISP configuration parameters is determined based on a characteristic of an ambient light being below a first threshold.

In one embodiment, the second set of ISP configuration parameters is determined based on a characteristic of an ambient light being above a first threshold.

In one embodiment, processing logic further captures, by the image sensor, the raw image data and applies an image processing algorithm to the raw image data based on a first or second set of ISP configuration parameters to generate the image.

In one embodiment, the characteristic of an ambient light includes color temperature or light intensity of an ambient light.

In one embodiment, the image processing algorithm comprises one of: image format conversion, image resize, exposure adjustment, black level adjustment, color adjustment, pixel correction, color interpolation, or de-noise algorithms.

In one embodiment, processing logic applies a machine learning algorithm to a generated image for object detection and/or objects classification, wherein an object detected includes a road lane and the first or second set of ISP configuration parameters contrast a white road lane from a yellow road lane when a color temperature of the ambient light is a warm color.

In one embodiment, processing logic applies an image processing algorithm to the raw image data based on a third set of ISP configuration parameters if the ADV is facing a direction of a sun.

In one embodiment, processing logic further determines a characteristic of an ambient light of the ADV. In response to determining the characteristic of an ambient light of the ADV is above a second threshold (e.g., ADV101is facing the sun), processing logic configures the ISP with a third set of ISP configuration parameters (e.g., set E).

In one embodiment, the first set of ISP configuration parameters is determined based on a time of day.

In one embodiment, the first or second set of ISP configuration parameters are used to automatically tune an image for autofocus (AF), auto exposure (AE), or auto white balance (AWB).

In one embodiment, an image processing algorithm is applied to the raw image data to improve an inference accuracy of a machine learning algorithm for perception.

In one embodiment, processing logic further configures the ISP of the ADV according to priorities corresponding to two or more criteria when two or more criteria are satisfied.