Systems and methods for determining the lighting state of a vehicle

Systems and method are provided for controlling a vehicle. In one embodiment, a vehicle lighting detection method includes receiving sensor data associated with operation of one or more vehicles, and extracting from the sensor data a plurality of images and a plurality of corresponding image labels, wherein the images each include at least a portion of an observed vehicle, and the image labels indicate the corresponding lighting state of the observed vehicle in each of the images. The method further includes training, with a processor, a machine learning model utilizing the plurality of images and the plurality of corresponding image labels.

TECHNICAL FIELD

The present disclosure generally relates to autonomous vehicles, and more particularly relates to systems and methods for determining the lighting state of a vehicle in the vicinity of the autonomous vehicle, for example, whether or not the brake lights and/or turn signals of the vehicle are illuminated.

BACKGROUND

An autonomous vehicle (AV) is a vehicle that is capable of sensing its environment and navigating with little or no user input. It does so by employing sensing devices such as radar, lidar, image sensors, and the like. Autonomous vehicles further use information from global positioning systems (GPS) technology, navigation systems, vehicle-to-vehicle communication, vehicle-to-infrastructure technology, and/or drive-by-wire systems to navigate the vehicle.

While recent years have seen significant advancements in AVs, such systems might still be improved in a number of respects. For example, it would be advantageous for an AV to be capable of determining whether the brake lights, turn signals, hazard lights and/or other exterior lamps of another vehicle in the environment are illuminated. This information would assist the AV in predicting the likely behavior of other vehicles. While machine learning models might be considered for this task, training such a model would be time-consuming—requiring significant human intervention in the form of acquiring a large number of training images (e.g., of other vehicles) and labeling those images with the appropriate “lighting state” (e.g., “brake lights on,” “left turn signal on,” etc.).

Accordingly, it is desirable to provide systems and methods that are capable of training, without the aforementioned human intervention, an AV to recognize the exterior lighting state of other vehicles in the environment. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.

SUMMARY

Systems and method are provided for controlling an autonomous vehicle. In one embodiment, a vehicle lighting detection method includes receiving sensor data associated with operation of one or more vehicles, and extracting from the sensor data a plurality of images and a plurality of corresponding image labels, wherein the images each include at least a portion of an observed vehicle, and the image labels indicate the corresponding lighting state of the observed vehicle in each of the images. The method further includes training, with a processor, a machine learning model utilizing the plurality of images and the plurality of corresponding image labels.

In one embodiment, a system for controlling a vehicle includes an image extraction module and a vehicle lighting detection module. The image extraction module is configured to: accept sensor data associated with operation of one or more vehicles; extract from the sensor data a plurality of images and a plurality of corresponding image labels, wherein the images each include at least a portion of an observed vehicle, and the image labels indicate the corresponding lighting state of the observed vehicle in each of the images; and train a machine learning model utilizing the plurality of images and the plurality of corresponding image labels. The vehicle lighting detection module, which includes the trained machine learning model, is configured to receive sensor data relating to an environment associated with the autonomous vehicle and determine the vehicle lighting state of a second vehicle in the environment.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and is not intended to limit the application and uses. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary, or the following detailed description. As used herein, the term “module” refers to any hardware, software, firmware, electronic control component, processing logic, and/or processor device, individually or in any combination, including without limitation: application specific integrated circuit (ASIC), a field-programmable gate-array (FPGA), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.

With reference toFIG. 1, a vehicle lighting detection system (or simply “system”)100is associated with an autonomous vehicle (AV)10in accordance with various embodiments. In general, vehicle lighting detection system100includes a machine learning (ML) model (e.g., a convolutional neural network) capable of determining the lighting state of other vehicles in the vicinity of vehicle10, wherein the ML model itself is trained (e.g., by a central server external to AV10) using sensor data previously acquired by one or more vehicles and subjected to an automatic extraction and labeling process to produce labeled training images (e.g., a set of individual vehicle images, produced by an optical camera, along with a lighting state label for each of those images). The resulting ML model can then be distributed to any number of vehicles, and may be automatically updated at regular or configurable intervals.

Stated another way, system and methods in accordance with the present subject matter are capable of automatically extracting and labeling training images from contemporaneous sensor data and thereafter training the ANN without human involvement. This extraction and labeling is accomplished using information acquired from sensor data (such as lidar and map data) contemporaneously, which is used by the system to reason about the behavior and state of the vehicle that was previously observed (i.e., the “observed vehicle”). This process might take into account evidence relating to, for example, the state of the world (e.g., illumination of a traffic light), the state of the observed vehicle relative to the world (e.g., vehicle decelerating rapidly, vehicle in a turn-only lane, vehicle approaching an intersection), and the future behavior of the observed vehicle (e.g., a determination that the vehicle actually made a turn or stopped at

Referring now toFIG. 1, an autonomous vehicle (“AV” or simply “vehicle”)10generally includes a chassis12, a body14, front wheels16, and rear wheels18. The body14is arranged on the chassis12and substantially encloses components of the vehicle10. The body14and the chassis12may jointly form a frame. The wheels16-18are each rotationally coupled to the chassis12near a respective corner of the body14.

In various embodiments, vehicle10is an autonomous vehicle and vehicle lighting detection system100is incorporated into the autonomous vehicle10. The autonomous vehicle10is, for example, a vehicle that is automatically controlled to carry passengers from one location to another. The vehicle10is depicted in the illustrated embodiment as a passenger car, but it should be appreciated that any other vehicle, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), marine vessels, aircraft, etc., can also be used.

In an exemplary embodiment, the autonomous vehicle10corresponds to a level four or level five automation system under the Society of Automotive Engineers (SAE) “J3016” standard taxonomy of automated driving levels. Using this terminology, a level four system indicates “high automation,” referring to a driving mode in which the automated driving system performs all aspects of the dynamic driving task, even if a human driver does not respond appropriately to a request to intervene. A level five system, on the other hand, indicates “full automation,” referring to a driving mode in which the automated driving system performs all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a human driver. It will be appreciated, however, the embodiments in accordance with the present subject matter are not limited to any particular taxonomy or rubric of automation categories. Furthermore, systems and methods in accordance with the present embodiment may be used in conjunction with any autonomous vehicle that utilizes a navigation system to provide route guidance.

As shown, the autonomous vehicle10generally includes a propulsion system20, a transmission system22, a steering system24, a brake system26, a sensor system28, an actuator system30, at least one data storage device32, at least one controller34, and a communication system36. The propulsion system20may, in various embodiments, include an internal combustion engine, an electric machine such as a traction motor, and/or a fuel cell propulsion system. The transmission system22is configured to transmit power from the propulsion system20to the vehicle wheels16and18according to selectable speed ratios. According to various embodiments, the transmission system22may include a step-ratio automatic transmission, a continuously-variable transmission, or other appropriate transmission.

The brake system26is configured to provide braking torque to the vehicle wheels16and18. Brake system26may, in various embodiments, include friction brakes, brake by wire, a regenerative braking system such as an electric machine, and/or other appropriate braking systems.

The steering system24influences a position of the vehicle wheels16and/or18. While depicted as including a steering wheel25for illustrative purposes, in some embodiments contemplated within the scope of the present disclosure, the steering system24may not include a steering wheel.

The sensor system28includes one or more sensing devices40a-40nthat sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle10. The sensing devices40a-40nmight include, but are not limited to, radars, lidars, global positioning systems, optical cameras, thermal cameras, ultrasonic sensors, and/or other sensors. The actuator system30includes one or more actuator devices42a-42nthat control one or more vehicle features such as, but not limited to, the propulsion system20, the transmission system22, the steering system24, and the brake system26. In various embodiments, autonomous vehicle10may also include interior and/or exterior vehicle features not illustrated inFIG. 1, such as various doors, a trunk, and cabin features such as air, music, lighting, touch-screen display components (such as those used in connection with navigation systems), and the like.

The data storage device32stores data for use in automatically controlling the autonomous vehicle10. In various embodiments, the data storage device32stores defined maps of the navigable environment. In various embodiments, the defined maps may be predefined by and obtained from a remote system (described in further detail with regard toFIG. 2). For example, the defined maps may be assembled by the remote system and communicated to the autonomous vehicle10(wirelessly and/or in a wired manner) and stored in the data storage device32. Route information may also be stored within data device32—i.e., a set of road segments (associated geographically with one or more of the defined maps) that together define a route that the user may take to travel from a start location (e.g., the user's current location) to a target location. As will be appreciated, the data storage device32may be part of the controller34, separate from the controller34, or part of the controller34and part of a separate system.

The instructions may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. The instructions, when executed by the processor44, receive and process signals from the sensor system28, perform logic, calculations, methods and/or algorithms for automatically controlling the components of the autonomous vehicle10, and generate control signals that are transmitted to the actuator system30to automatically control the components of the autonomous vehicle10based on the logic, calculations, methods, and/or algorithms. Although only one controller34is shown inFIG. 1, embodiments of the autonomous vehicle10may include any number of controllers34that communicate over any suitable communication medium or a combination of communication mediums and that cooperate to process the sensor signals, perform logic, calculations, methods, and/or algorithms, and generate control signals to automatically control features of the autonomous vehicle10. In one embodiment, as discussed in detail below, controller34is configured to detect the lighting state of other vehicles in the environment using a model that has been previously trained by extracting and labeling training images from contemporaneous sensor data.

With reference now toFIG. 2, in various embodiments, the autonomous vehicle10described with regard toFIG. 1may be suitable for use in the context of a taxi or shuttle system in a certain geographical area (e.g., a city, a school or business campus, a shopping center, an amusement park, an event center, or the like) or may simply be managed by a remote system. For example, the autonomous vehicle10may be associated with an autonomous vehicle based remote transportation system.FIG. 2illustrates an exemplary embodiment of an operating environment shown generally at50that includes an autonomous vehicle based remote transportation system (or simply “remote transportation system”)52that is associated with one or more autonomous vehicles10a-10nas described with regard toFIG. 1. In various embodiments, the operating environment50(all or a part of which may correspond to entities48shown inFIG. 1) further includes one or more user devices54that communicate with the autonomous vehicle10and/or the remote transportation system52via a communication network56.

The communication network56supports communication as needed between devices, systems, and components supported by the operating environment50(e.g., via tangible communication links and/or wireless communication links). For example, the communication network56may include a wireless carrier system60such as a cellular telephone system that includes a plurality of cell towers (not shown), one or more mobile switching centers (MSCs) (not shown), as well as any other networking components required to connect the wireless carrier system60with a land communications system. Each cell tower includes sending and receiving antennas and a base station, with the base stations from different cell towers being connected to the MSC either directly or via intermediary equipment such as a base station controller. The wireless carrier system60can implement any suitable communications technology, including for example, digital technologies such as CDMA (e.g., CDMA2000), LTE (e.g., 4G LTE or 5G LTE), GSM/GPRS, or other current or emerging wireless technologies. Other cell tower/base station/MSC arrangements are possible and could be used with the wireless carrier system60. For example, the base station and cell tower could be co-located at the same site or they could be remotely located from one another, each base station could be responsible for a single cell tower or a single base station could service various cell towers, or various base stations could be coupled to a single MSC, to name but a few of the possible arrangements.

Apart from including the wireless carrier system60, a second wireless carrier system in the form of a satellite communication system64can be included to provide uni-directional or bi-directional communication with the autonomous vehicles10a-10n. This can be done using one or more communication satellites (not shown) and an uplink transmitting station (not shown). Uni-directional communication can include, for example, satellite radio services, wherein programming content (news, music, etc.) is received by the transmitting station, packaged for upload, and then sent to the satellite, which broadcasts the programming to subscribers. Bi-directional communication can include, for example, satellite telephony services using the satellite to relay telephone communications between the vehicle10and the station. The satellite telephony can be utilized either in addition to or in lieu of the wireless carrier system60.

A land communication system62may further be included that is a conventional land-based telecommunications network connected to one or more landline telephones and connects the wireless carrier system60to the remote transportation system52. For example, the land communication system62may include a public switched telephone network (PSTN) such as that used to provide hardwired telephony, packet-switched data communications, and the Internet infrastructure. One or more segments of the land communication system62can be implemented through the use of a standard wired network, a fiber or other optical network, a cable network, power lines, other wireless networks such as wireless local area networks (WLANs), or networks providing broadband wireless access (BWA), or any combination thereof. Furthermore, the remote transportation system52need not be connected via the land communication system62, but can include wireless telephony equipment so that it can communicate directly with a wireless network, such as the wireless carrier system60.

Although only one user device54is shown inFIG. 2, embodiments of the operating environment50can support any number of user devices54, including multiple user devices54owned, operated, or otherwise used by one person. Each user device54supported by the operating environment50may be implemented using any suitable hardware platform. In this regard, the user device54can be realized in any common form factor including, but not limited to: a desktop computer; a mobile computer (e.g., a tablet computer, a laptop computer, or a netbook computer); a smartphone; a video game device; a digital media player; a component of a home entertainment equipment; a digital camera or video camera; a wearable computing device (e.g., smart watch, smart glasses, smart clothing); or the like. Each user device54supported by the operating environment50is realized as a computer-implemented or computer-based device having the hardware, software, firmware, and/or processing logic needed to carry out the various techniques and methodologies described herein. For example, the user device54includes a microprocessor in the form of a programmable device that includes one or more instructions stored in an internal memory structure and applied to receive binary input to create binary output. In some embodiments, the user device54includes a GPS module capable of receiving GPS satellite signals and generating GPS coordinates based on those signals. In other embodiments, the user device54includes cellular communications functionality such that the device carries out voice and/or data communications over the communication network56using one or more cellular communications protocols, as are discussed herein. In various embodiments, the user device54includes a visual display, such as a touch-screen graphical display, or other display.

The remote transportation system52includes one or more backend server systems, not shown), which may be cloud-based, network-based, or resident at the particular campus or geographical location serviced by the remote transportation system52. The remote transportation system52can be manned by a live advisor, an automated advisor, an artificial intelligence system, or a combination thereof. The remote transportation system52can communicate with the user devices54and the autonomous vehicles10a-10nto schedule rides, dispatch autonomous vehicles10a-10n, and the like. In various embodiments, the remote transportation system52stores store account information such as subscriber authentication information, vehicle identifiers, profile records, biometric data, behavioral patterns, and other pertinent subscriber information. In one embodiment, as described in further detail below, remote transportation system52includes a route database53that stores information relating to navigational system routes.

In accordance with a typical use case workflow, a registered user of the remote transportation system52can create a ride request via the user device54. The ride request will typically indicate the passenger's desired pickup location (or current GPS location), the desired destination location (which may identify a predefined vehicle stop and/or a user-specified passenger destination), and a pickup time. The remote transportation system52receives the ride request, processes the request, and dispatches a selected one of the autonomous vehicles10a-10n(when and if one is available) to pick up the passenger at the designated pickup location and at the appropriate time. The transportation system52can also generate and send a suitably configured confirmation message or notification to the user device54, to let the passenger know that a vehicle is on the way.

As can be appreciated, the subject matter disclosed herein provides certain enhanced features and functionality to what may be considered as a standard or baseline autonomous vehicle10and/or an autonomous vehicle based remote transportation system52. To this end, an autonomous vehicle and autonomous vehicle based remote transportation system can be modified, enhanced, or otherwise supplemented to provide the additional features described in more detail below.

In accordance with various embodiments, controller34implements an autonomous driving system (ADS)70as shown inFIG. 3. That is, suitable software and/or hardware components of controller34(e.g., processor44and computer-readable storage device46) are utilized to provide an autonomous driving system70that is used in conjunction with vehicle10.

In various embodiments, the instructions of the autonomous driving system70may be organized by function or system. For example, as shown inFIG. 3, the autonomous driving system70can include a sensor fusion system74, a positioning system76, a guidance system78, and a vehicle control system80. As can be appreciated, in various embodiments, the instructions may be organized into any number of systems (e.g., combined, further partitioned, etc.) as the disclosure is not limited to the present examples.

In various embodiments, the sensor fusion system74synthesizes and processes sensor data and predicts the presence, location, classification, and/or path of objects and features of the environment of the vehicle10. In various embodiments, the sensor fusion system74can incorporate information from multiple sensors, including but not limited to cameras, lidars, radars, and/or any number of other types of sensors.

The positioning system76processes sensor data along with other data to determine a position (e.g., a local position relative to a map, an exact position relative to lane of a road, vehicle heading, velocity, etc.) of the vehicle10relative to the environment. The guidance system78processes sensor data along with other data to determine a path for the vehicle10to follow. The vehicle control system80generates control signals for controlling the vehicle10according to the determined path.

In various embodiments, the controller34implements machine learning techniques to assist the functionality of the controller34, such as feature detection/classification, obstruction mitigation, route traversal, mapping, sensor integration, ground-truth determination, and the like.

As mentioned briefly above, the system100ofFIG. 1is configured to determine the lighting state of other vehicles in the environment utilizing a machine learning model that has been trained by automatically extracting and labeling training images from sensor data previously acquired from any number of vehicles.

In that regard,FIG. 4is a simplified, conceptual overview of an autonomous vehicle and roadway helpful in describing operation of various embodiments. InFIG. 4, AV10is shown traveling (to the right) along a roadway211toward an intersection212. Also shown in this figure is a traffic light204and a second vehicle202located in front of vehicle10along roadway211. The inset image shows a view of the rear portion250of vehicle202, including various external lighting components that might be observed by the various sensors of AV10, such as brake lamps/turn signal/hazard lamps251, turn signal lamps252, and upper brake lamp253. It will be appreciated that the example vehicle202and rear lighting configuration shown inFIG. 4are not intended to be limiting, and that systems and methods in accordance with the present subject matter are capable of detecting and categorizing the lighting state of any type of vehicle now known or later developed.

As used herein, the term “lighting state” refers to the state (e.g., illuminated, not illuminated, partially illuminated) of various lighting components provided on vehicles external to AV10and the meaning of their activation or lack of activation. As is known in the art, vehicle lighting may be categorized as either illumination lighting (e.g., headlamps, driving lamps, fog lamps, high beams, and the like) or “conspicuity” lighting. The latter category may include front conspicuity lighting (daytime running lights), lateral conspicuity lighting (side turn signals, etc.), and rear conspicuity lighting (reverse indicator, tail lights, brake lights, center high mount stop lamps, emergency stop signals, etc.). Depending upon the design of vehicle202, any given lamp may be used for multiple purposes. For example, the brake lamps (251) might also be used as hazard lamps, turn signals, or the like, depending upon the context. Accordingly, the “lighting state” is intended to encompass the meaning of the detected illumination of the vehicle. Such lighting states may include, for example, “hazard lights on/off”, “brake lights on/off”, “turn signals off/onLeft/onRight”, and the like. The lighting state may be represented in any convenient way using a variety of known data structures.

As mentioned briefly above, and as further described below, system and methods in accordance with the present subject matter are capable of automatically extracting and labeling training images from contemporaneous sensor data produced by any number of vehicles (such as AV10, vehicle202, etc.) and thereafter training a neural network without human involvement. This extraction and labeling is accomplished take into account evidence relating to, for example, the state of the world (i.e., the state of the environment in the vicinity of the vehicle), the state of the observed vehicle relative to the world (i.e., the state of the vehicle relative to the environment in which it is traveling), and the future behavior of the observed vehicle (i.e., the behavior of the vehicle later observed).FIG. 4thus shows the real-time acquisition of sensor data (by AV10) that will later be used for extraction and labeling of training data. In that regard, the phrase “observed vehicle” refers to a vehicle whose behavior (as determined by sensor data) is later used to train a machine learning model, as described in further detail below.

InFIG. 4, the state of the world might include, for example, the state of traffic light204(e.g., green, yellow, red), which both AV10and vehicle202are approaching. That is, if the sensor data indicates that traffic light204is red (i.e., a “stop” state), then the system might consider this extra “evidence” (when later performing the labeling procedure) that the optical view of rear250of vehicle202includes illumination of brake lamps251. Such extra evidence might also include, for example, the presence of crosswalks, pedestrians within a crosswalk, and any other visible light or other signal that might be relevant.

The state of the vehicle relative to the world might include, for example, an assessment that vehicle202is decelerating rapidly (suggesting that brake lights251are activated). The state of the vehicle relative to the world might also include the nature of the lane in which vehicle202is traveling (using map data). For example, if it is determined that vehicle202is in a right-turn only lane, then this might be considered extra evidence that the right turn signal252of vehicle202is illuminated. The state of the vehicle relative to the world might also include an indication that vehicle202is approaching intersection212(as determined from map data), and is thus likely more to be traveling with its brake lights251activated. The state of the vehicle might also include an indication that vehicle202is approaching a top sign, a yield sign, a particular type of intersection, or other relevant signage.

In some embodiments, the system confirms that the observed vehicle is traveling in the same direction as AV10(e.g., to prevent the system from considering automotive headlights). This might involve, for example, filtering out decelerating cars traveling the opposite direction in an opposite lane. That is, the system would consider not only decelerating cars, but decelerating cars that will have their brake lights visible to AV10. Similarly, the system might also filter observations based on the presence of other cars in between AV10and the observed vehicle—i.e., determining that the correct vehicle is being observed.

With respect to the future behavior of the vehicle, this might include, for example, a determination that vehicle202actually stopped at intersection, which provides extra evidence that the brake lights251were previously illuminated. Similarly, the fact that vehicle202actually takes a right turn at intersection212is further evidence that its right turn signal252was previously illuminated. The system might also take into account the fact that a car with its hazards activated is not likely to move in the near term.

In accordance with various embodiment, the term “future behavior” refers to an implementation in which the system examines the output of both a tracker and a map component available to AV10(as may be incorporated into the system ofFIG. 3). As mentioned above, a tracker is a component that tracks the position (and other data) of objects over time. More particularly, in one embodiment the system queries the tracker as to where a particular observed vehicle ended up after some predetermined time (e.g., about 10 seconds). The tracker then responds with positional information (e.g., longitude/latitude). The system queries the map component to determine the street, etc., corresponding to that location. The nature of the movement to that street, etc. can then be interpreted as a turn (e.g., right turn, left turn, straight, etc.).

In some embodiments, the fact that many drivers do not properly use turn signals, etc., might also be taken into account—i.e., there is “noise” in the observed data. If this noise is sufficiently low, the ML model may select to tolerate the error. If the noise is above some predetermined threshold, however, a human operator may be employed to assist in interpretation (i.e., labeling training images). In one embodiment, for example, when presented with an already cropped and likely correctly-labeled image, the operator merely indicates whether the label is correct.

FIG. 5is a dataflow diagram that illustrates various embodiments of the system100which may be embedded within the controller34. Referring toFIG. 5, an exemplary system generally includes a vehicle lighting detection module520that receives sensor data502relating to the vehicle's environment (e.g., camera images, lidar data, or any other sensor data received from sensor system28) and has, as its output503, a determination as to the lighting state of a vehicle within its field of view (such as vehicle202inFIG. 4). Thus, module520implements the ML model that has been previously trained using a variety of sensor data acquired through the normal everyday driving of one or more vehicles, as described in further detail below.

As a threshold matter, it will be understood that various embodiments of the system100according to the present disclosure can include any number of sub-modules embedded within the controller34. As can be appreciated, the sub-modules shown inFIG. 5can be combined and/or further partitioned to similarly perform the various methods described herein. Inputs to the system100may be received from the sensor system28, received from other control modules (not shown) associated with the autonomous vehicle10, received from the communication system36, and/or determined/modeled by other sub-modules (not shown) within the controller34ofFIG. 1.

As mentioned briefly above, the vehicle lighting detection module may implement a variety of machine learning methodologies, such as an image-centric artificial neural network that undergoes training using a set of images previously acquired and stored (e.g., in server53ofFIG. 2). In that regard,FIG. 6is a block diagram of an exemplary convolutional neural network (CNN) in accordance with various embodiments.

As shown inFIG. 6, an exemplary CNN600generally receives one or more input images600(e.g., labeled optical images of an observed vehicle, as described further below) and produces a series of outputs640associated with lighting state of observed vehicles recognized within the image. In that regard, input610may be referred to without loss of generality as an “image,” even though it might include other sensor data types.

In general, CNN600implements a convolutional phase622, followed by feature extraction620and classification630. Convolutional phase622uses an appropriately sized convolutional filter that produces a set of feature maps621corresponding to smaller tilings of input image610. As is known, convolution as a process is translationally invariant—i.e., features of interest (brake lamps, side mirror lights, etc.) can be identified regardless of their location within image610.

Subsampling624is then performed to produce a set of smaller feature maps623that are effectively “smoothed” to reduce sensitivity of the convolutional filters to noise and other variations. Subsampling might involve taking an average or a maximum value over a sample of the inputs621. Feature maps623then undergo another convolution628, as is known in the art, to produce a large set of smaller feature maps625. Feature maps625are then subsampled to produce feature maps627.

During the classification phase (630), the feature maps627are processed to produce a first layer631, followed by a fully-connected layer633, from which outputs640are produced. For example, during normal operation of AV10(i.e., after installation of the trained ML model) output641might correspond to “hazard lamps on”, output642might correspond to “left turn signal on”, etc. In some embodiments, outputs640are probabilistic—i.e., the output is a vector representing the probability that corresponding lighting states are “true”, for example:[probability_brake_lights_on,probability_left_turn_signal_on,probability_right_turn_signal_on,probability_hazard_lights_on])=[0.92, 0.14, 0.13, 0.30]

In general, the CNN600illustrated inFIG. 6has been trained by presenting it with a large number (i.e., a “corpus”) of input images and providing the known, predetermined labels to outputs840based on the determined lighting state(s). Backpropagation as is known in the art is then used to refine the training of CNN600. The resulting model is then implemented within module520ofFIG. 5. Subsequently, during normal operation of AV10, the trained CNN600is used to process images610received as AV10travels and observes other vehicles in its environment.

It will be appreciated that the present embodiments are not limited to the CNN model described above. A variety of machine learning techniques may be used, including, for example, other artificial neural networks, such as recurrent neural networks (RNN), as well as random forest classifiers, Bayes classifiers (e.g., naive Bayes), principal component analysis (PCA), support vector machines, linear discriminant analysis, long short-term memory (LSTM) models, and the like. In some embodiments, multiple ANNs can be employed—i.e., one ANN may be used to detect turn signal activity, and another might be used to detect brake light activity.

FIG. 7is a dataflow diagram illustrating operation of an image extraction and label generation module720in accordance with various embodiments. In general, module720takes as its input sensor data702(from one or more of the sensors within sensor system28), and produces an output74that comprises labeled images704. That is, output74includes a set of images of what it has determined are individual observed vehicles (extracted from any number of vehicles within a particular scene), along with a corresponding label (e.g., “brake_lights_on,” “right_turn_signal_on”, or the like), which can then be used to train CNN600. Referring toFIG. 8, for example, a large scale optical image802(representing, perhaps, the front view from AV10) may be processed by module720(using other available sensor data, such as lidar cloud information) to extract a smaller image804of an individual observed vehicle and to provide it with an appropriate label (in this case “brake_lights_on”). This image would later be supplied as the input

Referring now toFIG. 9, and with continued reference toFIGS. 1-8, a flowchart illustrates a control method900that can be performed by the system100ofFIG. 1in conjunction with the module720ofFIG. 7. As can be appreciated in light of the disclosure, the order of operation within the method is not limited to the sequential execution as illustrated inFIG. 9, but may be performed in one or more varying orders as applicable and in accordance with the present disclosure.

With continued reference toFIG. 9, the process begins at901with the acquisition of sensor data. As mentioned above, sensor data might include any available data acquired by sensor system28during operation of AV10and/or other vehicles that are used to populate the database (e.g., database53) to be used for training CNN600. That is, a corpus of sensor data may be populated by a fleet of vehicles configured to transmit their saved sensor data to an external server for later processing.

Next, at902, module720is used to extract and label images to create a training set based on the sensor data702acquired at901. The result will be a large number of labeled images (704) with corresponding labels (corresponding to discrete vehicle lighting states). Module720may be implemented, for example, within remote transportation system52ofFIG. 2. This step generally includes analyzing all available sensor data, cropping a large optical image of a scene so that it encompasses only one vehicle (or as close as possible to one vehicle), then determining the most likely vehicle lighting state of the vehicle that is the subject of the cropped image. The size of the crop may be determined, for example, by examining the lidar point cloud size corresponding to the observed vehicle. The vehicle lighting state may be determined, as noted above, based on the state of the world, the state of the vehicle relative to the world, and the future behavior of the vehicle.

In one embodiment, the extraction process is implemented as a series of progressive “filtering” steps. For example, the system might first scan over the entire dataset or some subset of the dataset chosen by some heuristic or manual decision. This scan might be performed using a batch or streaming processing framework such as MapReduce or Spark, which are known in the art. The system might not need to examine every single vehicle if the database is indexed. For instance, the database might store the maximum deceleration of a vehicle. In that case, the system might just ask the database for cars with a certain minimum deceleration, and avoid the cost of observing irrelevant cars. Next, the system performs a series of successive filtering steps. At each step, the system reduces the number of vehicles considered by applying some form of predetermined test.

For example, to locate cars with their brake lights on, the system might: (a) filter to keep only those vehicles that are rapidly decelerating; (b) filter to keep only those rapidly-decelerating cars that are traveling in the same direction as AV10and have no camera-blocking cars in between it and AV10; (c) filter to keep only those cars from (b) that are stopping at a red light, and so on with additional progressive filters, if appropriate.

During such progressive filtering, the system might also collect what are called “negative examples.” That is, the ANN is preferably trained with examples of both cars with their lights on and those with lights off, so it can learn to tell the difference between the two. The filtering steps provide an opportunity to collect these negative examples as well.

The labeled images704produced by module720in step802are then used to train the ANN (e.g. CNN600ofFIG. 6) as described above. That is, each of the labeled images are presented to CNN600as an input image (610), with the outputs (640) being set to the correct vehicle lighting state label associated with each image.

Once the ANN has been trained, the model is then provided (e.g., via communication network56) to one or more vehicles (e.g., AV10), as shown in step904. This model can then be used in the ordinary course to detect the lighting state of other vehicles in the vicinity of AV10, thereby providing AV10with another tool to predict the behavior of those vehicles.

Both the training of ML model520and the subsequent use of that model to detect lighting states may be accomplished through the use of a sequence of images or other sensor input acquired at known time intervals. In such cases, ML model520may advantageously implement a time-based recurrent neural network (RNN), such as a long short-term memory (LSTM) model. The observation of a sequence of images (rather than a single frame or snapshot) can be useful in a number of scenarios. For example, an image sequence may be used to detect flashing turn indicators, which experience a change in illumination over time. Similarly, an image sequence may be used to detect the activation of a brake signal, particularly at night, when the primary indication of braking is an increase in tail lamp intensity.