Patent Application
20180374341
The present disclosure generally relates to autonomous vehicles, and more particularly relates to systems and methods for predicting traffic patterns of vehicles and objects in the vicinity of autonomous vehicles.
An autonomous vehicle is a vehicle that is capable of sensing its environment and navigating with little or no user input. It does so by using 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 and perform traffic prediction.
While recent years have seen significant advancements in navigation systems and traffic prediction, such systems might still be improved in a number of respects. For example, an autonomous vehicle will typically encounter, during normal operation, a large number of vehicles and other objects, each of which might exhibit its own, hard-to-predict behavior. That is, even when an autonomous vehicle has an accurate semantic understanding of the roadway and has correctly detected and classified objects in its vicinity, the vehicle may yet be unable to accurately predict the trajectory and/or paths of certain objects in a variety of contexts.
Accordingly, it is desirable to provide systems and methods that are capable of predicting the behavior of objects encountered by an autonomous vehicle. 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.
Systems and method are provided for controlling a vehicle. In one embodiment, a traffic pattern prediction method includes providing, within an autonomous vehicle, a first set of prediction policies. The method further includes receiving traffic pattern data associated with an object observed by the autonomous vehicle, the traffic pattern data including a kinematic estimate for the object, a position sequence for the object, and road semantics associated with a region near the object. The traffic pattern data may also include data relating to the shape and/or size of the object. A predicted path for the object is determined based on the first set of prediction policies and the traffic pattern data, and an actual path for the object is determined. A new prediction policy for the object is determined if the difference between the predicted path and the actual path is above a predetermined threshold. A second set of prediction policies is produced based on the first set of prediction policies and the new policy.
In one embodiment, the kinematic estimate includes at least one of a velocity, an acceleration, and a turn rate of the observed object.
In one embodiment, the traffic pattern data further includes an estimate of the physical dimensions of the object.
In one embodiment, determining the new prediction policy is performed by a server remote from the autonomous vehicle.
In one embodiment, the first set of prediction policies includes a plurality of vehicle maneuvers.
In one embodiment, the difference between the predicted path and the actual path is a sum-of-squares difference.
In one embodiment, the road semantics include at least one of road labels, lane boundaries, lane connectivity, and drivable areas of the roadway.
In one embodiment, a system for controlling a vehicle includes a sensor system configured to observe an object in an environment associated with the vehicle, and a policy learning module, communicatively coupled to the sensor system, including a first set of prediction policies. The policy learning module is configured to: receive traffic pattern data associated with an object observed by the autonomous vehicle, the traffic pattern data including a kinematic estimate for the object, a position sequence for the object, and road semantics associated with a region near the object; determine a predicted path for the object based on the first set of prediction policies and the traffic pattern data; determine an actual path for the object; determine a new prediction policy for the object if the difference between the predicted path and the actual path is above a predetermined threshold; and modify the first set of prediction policies based on the new policy.
In one embodiment, the kinematic estimate includes at least one of a velocity, an acceleration, and a turn rate of the observed object.
In one embodiment, the traffic pattern data further includes an estimate of the physical dimensions of the object.
In one embodiment, the new prediction policy is determined by a server remote from the autonomous vehicle.
In one embodiment, the first set of prediction policies includes a plurality of vehicle maneuvers.
In one embodiment, the difference between the predicted path and the actual path is a sum-of-squares difference.
In one embodiment, the road semantics include at least one of road labels, lane boundaries, lane connectivity, and drivable areas of the roadway.
An autonomous vehicle in accordance with one embodiment includes a sensor system configured to observe an object in an environment associated with the vehicle; and a policy learning module, communicatively coupled to the sensor system, including a first set of prediction policies. The policy learning module configured to: receive traffic pattern data associated with an object observed by the autonomous vehicle, the traffic pattern data including a kinematic estimate for the object, a position sequence for the object, and road semantics associated with a region near the object; determine a predicted path for the object based on the first set of prediction policies and the traffic pattern data; determine an actual path for the object; determine a new prediction policy for the object if the difference between the predicted path and the actual path is above a predetermined threshold; and modify the first set of prediction policies based on the new policy.
In one embodiment, the kinematic estimate includes at least one of a velocity, an acceleration, and a turn rate of the observed object.
In one embodiment, the traffic pattern data further includes an estimate of the physical dimensions of the object.
In one embodiment, the new prediction policy is determined by a server remote from the autonomous vehicle.
In one embodiment, the first set of prediction policies includes a plurality of vehicle maneuvers.
In one embodiment, the difference between the predicted path and the actual path is a sum-of-squares difference.
The exemplary embodiments will hereinafter be described in conjunction with the following drawing figures, wherein like numerals denote like elements, and wherein:
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.
Embodiments of the present disclosure may be described herein in terms of functional and/or logical block components and various processing steps. It should be appreciated that such block components may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of the present disclosure may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. In addition, those skilled in the art will appreciate that embodiments of the present disclosure may be practiced in conjunction with any number of systems, and that the systems described herein is merely exemplary embodiments of the present disclosure.
For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, machine learning, image analysis, neural networks, vehicle kinematics, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent example functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an embodiment of the present disclosure.
With reference to
As depicted in
In various embodiments, the vehicle 10 is an autonomous vehicle and the traffic pattern prediction system 100 is incorporated into the autonomous vehicle 10 (hereinafter referred to as the autonomous vehicle 10). The autonomous vehicle 10 is, for example, a vehicle that is automatically controlled to carry passengers from one location to another. The vehicle 10 is 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 vehicle 10 corresponds 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.
As shown, the autonomous vehicle 10 generally includes a propulsion system 20, a transmission system 22, a steering system 24, a brake system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36. The propulsion system 20 may, 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 system 22 is configured to transmit power from the propulsion system 20 to the vehicle wheels 16 and 18 according to selectable speed ratios. According to various embodiments, the transmission system 22 may include a step-ratio automatic transmission, a continuously-variable transmission, or other appropriate transmission.
The brake system 26 is configured to provide braking torque to the vehicle wheels 16 and 18. Brake system 26 may, 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 system 24 influences a position of the vehicle wheels 16 and/or 18. While depicted as including a steering wheel 25 for illustrative purposes, in some embodiments contemplated within the scope of the present disclosure, the steering system 24 may not include a steering wheel.
The sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the exterior environment and/or the interior environment of the autonomous vehicle 10. The sensing devices 40a-40n might include, but are not limited to, radars, lidars, global positioning systems, optical cameras, thermal cameras, ultrasonic sensors, and/or other sensors. The actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features such as, but not limited to, the propulsion system 20, the transmission system 22, the steering system 24, and the brake system 26. In various embodiments, autonomous vehicle 10 may also include interior and/or exterior vehicle features not illustrated in
The data storage device 32 stores data for use in automatically controlling the autonomous vehicle 10. In various embodiments, the data storage device 32 stores 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 to
The controller 34 includes at least one processor 44 and a computer-readable storage device or media 46. The processor 44 may be any custom-made or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chip set), any combination thereof, or generally any device for executing instructions. The computer readable storage device or media 46 may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example. KAM is a persistent or non-volatile memory that may be used to store various operating variables while the processor 44 is powered down. The computer-readable storage device or media 46 may be implemented using any of a number of known memory devices such as PROMs (programmable read-only memory), EPROMs (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or any other electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the controller 34 in controlling the autonomous vehicle 10.
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 processor 44, receive and process signals from the sensor system 28, perform logic, calculations, methods and/or algorithms for automatically controlling the components of the autonomous vehicle 10, and generate control signals that are transmitted to the actuator system 30 to automatically control the components of the autonomous vehicle 10 based on the logic, calculations, methods, and/or algorithms. Although only one controller 34 is shown in
The communication system 36 is configured to wirelessly communicate information to and from other entities 48, such as but not limited to, other vehicles (“V2V” communication), infrastructure (“V2I” communication), remote transportation systems, and/or user devices (described in more detail with regard to
With reference now to
The communication network 56 supports communication as needed between devices, systems, and components supported by the operating environment 50 (e.g., via tangible communication links and/or wireless communication links). For example, the communication network 56 may include a wireless carrier system 60 such 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 system 60 with 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 system 60 can 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 system 60. 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 system 60, a second wireless carrier system in the form of a satellite communication system 64 can be included to provide uni-directional or bi-directional communication with the autonomous vehicles 10a-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 vehicle 10 and the station. The satellite telephony can be utilized either in addition to or in lieu of the wireless carrier system 60.
A land communication system 62 may further be included that is a conventional land-based telecommunications network connected to one or more landline telephones and connects the wireless carrier system 60 to the remote transportation system 52. For example, the land communication system 62 may 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 system 62 can 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 system 52 need not be connected via the land communication system 62, but can include wireless telephony equipment so that it can communicate directly with a wireless network, such as the wireless carrier system 60.
Although only one user device 54 is shown in
The remote transportation system 52 includes 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 system 52. The remote transportation system 52 can be manned by a live advisor, an automated advisor, an artificial intelligence system, or a combination thereof. The remote transportation system 52 can communicate with the user devices 54 and the autonomous vehicles 10a-10n to schedule rides, dispatch autonomous vehicles 10a-10n, and the like. In various embodiments, the remote transportation system 52 stores 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 system 52 includes a route database 53 that stores information relating to navigational system routes and also may be used to perform traffic pattern prediction.
In accordance with a typical use case workflow, a registered user of the remote transportation system 52 can create a ride request via the user device 54. 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 system 52 receives the ride request, processes the request, and dispatches a selected one of the autonomous vehicles 10a-10n (when and if one is available) to pick up the passenger at the designated pickup location and at the appropriate time. The transportation system 52 can also generate and send a suitably configured confirmation message or notification to the user device 54, 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 vehicle 10 and/or an autonomous vehicle based remote transportation system 52. 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, controller 34 implements an autonomous driving system (ADS) 70 as shown in
In various embodiments, the instructions of the autonomous driving system 70 may be organized by function or system. For example, as shown in
In various embodiments, the sensor fusion system 74 synthesizes and processes sensor data and predicts the presence, location, classification, and/or path of objects and features of the environment of the vehicle 10. In various embodiments, the sensor fusion system 74 can incorporate information from multiple sensors, including but not limited to cameras, lidars, radars, and/or any number of other types of sensors.
The positioning system 76 processes 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 vehicle 10 relative to the environment. The guidance system 78 processes sensor data along with other data to determine a path for the vehicle 10 to follow. The vehicle control system 80 generates control signals for controlling the vehicle 10 according to the determined path.
In various embodiments, the controller 34 implements machine learning techniques to assist the functionality of the controller 34, such as feature detection/classification, obstruction mitigation, route traversal, mapping, sensor integration, ground-truth determination, and the like.
As mentioned briefly above, the traffic pattern prediction system 100 is configured to predict the trajectory of vehicles and other objects in the vicinity of AV 10 and iteratively improve those predictions over time based on its observations of those objects. In some embodiments, this functionality is incorporated into sensor fusion system 74 of
In that regard,
In general, AV 10 is configured to utilize sensor data (e.g., from sensor system 28 of
AV 10 may estimate the spatial orientations 461 and 462 of objects 431 and 432 based on their respective paths and other available sensor data. For example, as shown in
The sequence of object positions (e.g., 441-446 and 451-455) may be represented and stored using any convenient data structure and metric known in the art. Furthermore, it will be appreciated that the distribution and number of positions used by the system is not limited by this example. Any number of such positions may be determined for objects 431 and 432, and the rate at which such positions are acquired may also vary depending upon design considerations.
In accordance with various embodiments, the size, geometry, dimensions, and other such aspects of objects 431 and 432 are estimated. In accordance with other embodiments, AV 10 is further configured to estimate the kinematic behavior of objects 431 and 432. As used herein, the terms “kinematic behavior” and “kinematic estimate” as applied to an object refers to a collection of parameters and values that may be used to characterize the motion of these objects, generally without reference to the forces that gave rise to such motion. Kinematic parameters might include, for example, the respective velocities of objects 431, 432 (i.e., their speeds and directions) and the instantaneous acceleration of objects 431, 432. Kinematic parameters may also include turn rates for objects 431, 432. These kinematic parameters may be determined in a variety of ways, as is known in the art.
In accordance with various embodiments, AV 10 has, generally speaking, a semantic understanding of roadway 400 (i.e., “road semantics”). Such road semantics might include, for example, road labels (e.g., for the lanes 411-414), lane boundaries, lane connectivity, drivable areas of the roadway 400, etc. Such information may be derived, for example, from map data of the type that would typically be available to AV 10 and described above in connection with
In various embodiments, AV 10 is configured to observe, detect, and classify objects 431 and 432 utilizing, for example, machine learning techniques applied to lidar, radar, and image data acquired via sensor system 28. That is, given the example shown in
Referring now to
Referring first to
Path prediction module 520 stores or otherwise has access to a set of policies 501-503 that, as described in further detail below, allow module 520 to produce an output 521 corresponding to a prediction of the future path(s) of the observed object or objects. In various embodiments, the accuracy of module 520 is continually improved by iteratively adapting policies 501-503 to accommodate “ill-described” classes of objects (via policy learning module 620, as described in further detail below).
The term “policy” or “prediction policy” as used herein refers to a procedure, model, set of criteria, or the like that takes as its input the characteristics of an object and its environment (e.g., the sum total of inputs 511-514) and produces a predicted path for that object. Thus, policies 501-503 are “prediction policies” in the sense that they are guidelines, rules, etc. for predicting the behavior of an object based on past knowledge regarding that type of object in similar circumstances and similar road semantics. Thus, policies 501-503 will generally correspond to different classes of objects and maneuvers, and module 520 will attempt to select which policy 501-503 best fits an object and/or maneuver, based on inputs 511-514 and past experience (e.g., via supervised, unsupervised, and/or reinforcement learning). In some embodiments, vehicles may interact with each other in such a way that the behavior and/or policies of one vehicle may be used to influence the policies of another vehicle. That is, policies 501-503 of AV 10 may be interactively modified based on the policies and behavior of other autonomous or non-autonomous vehicles in the vicinity.
For example, path prediction module 520, receiving inputs 511-514 corresponding to object 431 (i.e., the motorcycle) of
Path prediction module 520 (as well as policy learning module 620) may be implemented using any desired combination of hardware and software. In some embodiments, one or more of modules 520, 620 implement a machine learning (ML) model. A variety of ML techniques may be employed, including, for example, multivariate regression, artificial neural networks (ANNs), random forest classifiers, Bayes classifiers (e.g., naive Bayes), principal component analysis (PCA), support vector machines, linear discriminant analysis, clustering algorithms (e.g., KNN, K-means), and/or the like. In some embodiments, multiple ML models are used (e.g., via ensemble learning techniques).
It will be understood that the sub-modules shown in
Referring now to
In some embodiments, path prediction module 520 determines (periodically or in real-time) which outputs 521 should be considered “ill-described,” and that data is subsequently uploaded to an off-line system, such as system 52 of
Referring now to
First, at 701, a set (e.g., a “first set”) of policies (e.g., 501-503) are provided. The nature of policies 501-503 are described above, but in general correspond to expected behaviors for different position sequences, kinematics, and classes of those objects, as well as the applicable road semantics (e.g., inputs 511-514 to module 520). In some embodiments, a large number of policies are provided; in others, a minimal number of policies are used initially, assuming that subsequent learning (by module 620) based on experience will further populate and refine those policies.
Next, at 702, AV 10 collects traffic pattern data associated with objects observed in its vicinity. As described above, such traffic pattern data might include, for each detected object, a sequence of positions 511, a kinematic estimate 512, and a classification 514. Subsequently, or at the same time, the system determines (e.g., recalls, downloads, etc.) road semantics 513 applicable to the region in which AV 10 is operating (e.g., the expected layout of lanes 411-414 in roadway 400).
At 704, path prediction module 520 attempts to select a “best fit” policy (e.g., 501, 502, or 503) for each of the observed objects (e.g., 431 and 432) based on inputs 511-514. This may be accomplished using, for example, an artificial neural network (ANN) model or other such machine learning model typically used to solve classification problems, as described above.
Next, at 704, the module 520 tracks and determines the future of behavior of the observed objects (e.g., 431 and 432), and determines whether any of those objects are “ill-described” classes of objects. As used herein, the term “ill-described” refers to an object or class of objects in which the predicted behavior (as determined via policies 501-503) diverges from the actual (future) behavior by some predetermined “distance” or amount. The metric used for determining “ill-described” classes may vary. For example, this metric may be based on a difference (e.g., sum-of-squares difference) between the actual and predicted paths and/or kinematic values of an object. If the calculated difference is above some predetermined threshold, then that object is categorized as “ill-described.”
Given the set of “ill-described” objects and data relating thereto (610), policy learning module 620 then groups or clusters those objects into object classes. That is, module 620 examines the ill-described object data 610 and attempts to determine whether certain objects have some features in common. Consider, for example, object 431 in
One way to determine such object classes for ill-described objects is illustrated in
Next, at 707, module 620 determines a new set of policies for the classes determined for the ill-described objects in step 706. This may be accomplished, for example, by supervised training of module 520 using the previous determined inputs 511-514 and the actual behavior observed in those objects. Subsequently, at 708, a new set of policies are provided to module 520 based on the new set of policies and the previous, “first” set of policies provided at 701. Steps 701-708 may then be continually performed during operation of AV 10. In this way, the set of policies will tend to improve and be refined over time, allowing module 520 to iteratively learn to recognize and predict the behavior of a wide range of object classes.
While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration of the disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the exemplary embodiment or exemplary embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope of the disclosure as set forth in the appended claims and the legal equivalents thereof.
