False discrimination of rule violations for autonomous vehicles for data processing

Abstract

The method includes determining whether the at least one agent is a vulnerable road user (VRU) by determining whether the situation is "rationally predictable", and determining whether the at least one agent violates a higher priority rule compared to a rule violated by the vehicle. Rule violations in real-time driving scenarios, simulations, re-simulations, or other applications that result in traffic conflicts involving the vehicle and at least one agent are determined as an error for the vehicle. In an embodiment, the on-vehicle decision is based on a rule manual having a priority structure that accounts for responsibility, and a "initiator" of a traffic conflict is determined based on whether the vehicle or at least one agent first violates the rule and first violates a higher priority rule.

Description

Background Art

[0001] The planning system used by an autonomous vehicle (AV) can use a strategy or rule book to determine multiple feasible trajectories through the environment. Many rule violations can be registered by the AV, both in real life and in simulation. Some of these rule violations reflect poor behavioral choices by the AV. However, other rule violations are caused by specific situations or the behavior of other agents. For example, other agents in the simulation may not respond to the AV's actions, so the rule violation is not the AV's fault. These two situations are not distinguished in real life and in simulation, which leads to slowing down the AV development process by directing engineering resources to solve problems that appear to be AV behavior problems but are actually problems with the behavior of other agents.

[0002] Testing AVs in simulation is a way to test AV operations in complex scenarios, such as scenarios involving extreme situations not often encountered in the real world. In some of these complex scenarios, simulated agents (e.g., simulated pedestrians and / or simulated vehicles, etc.) may be configured (intentionally or unintentionally) to interact with the simulated AV and cause the simulated AV to generate trajectories that violate rules or policies. In some instances, it may be difficult to identify when the simulated agents are causing the generation of these trajectories, which makes it difficult to update the software that operates the simulated AV and improve the trajectories generated by the simulated AV. BRIEF DESCRIPTION OF THE DRAWINGS

[0003] Figure 1 is an example environment in which a vehicle including one or more components of an autonomous system may be implemented;

[0004] Figure 2 is a diagram of one or more systems of a vehicle including an autonomous system;

[0005] Figure 3 yes Figure 1 and Figure 2 a diagram of one or more devices and / or components of one or more systems;

[0006] Figure 4 is a diagram of some components of an autonomous system;

[0007] Figure 5 is a flow chart of a process for determining whether an AV is at fault for a traffic conflict caused by a rule violation;

[0008] Figure 6 Illustrate a rulebook priority structure for AV route planning that takes into account responsibility for rule violations;

[0009] Figure 7is a block diagram of a planning system capable of discerning whether an AV is at fault for a rule violation that causes a traffic conflict;

[0010] Figure 8 is a flow chart of a process for distinguishing fault between a vehicle and an agent that violated a rule causing a traffic conflict; and

[0011] Fig. 9 1 is a flowchart of a process for identifying the initiator of a traffic conflict involving an AV and an intelligent agent. DETAILED DESCRIPTION

[0012] In the following description, for the purpose of explanation, many specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent that the embodiments described in the present disclosure can be implemented without these specific details. In some instances, well-known configurations and devices are illustrated in block diagram form to avoid unnecessarily obscuring aspects of the present disclosure.

[0013] In the accompanying drawings, for ease of description, the specific arrangement or order of schematic elements (such as those representing systems, devices, modules, instruction blocks and / or data elements, etc.) is illustrated. However, those skilled in the art will understand that, unless explicitly described, the specific order or arrangement of schematic elements in the accompanying drawings is not intended to mean that a specific processing order or sequence, or separation of processing is required. In addition, unless explicitly described, the inclusion of schematic elements in the accompanying drawings is not intended to mean that such elements are required in all embodiments, nor is it intended to mean that the features represented by such elements cannot be included in some embodiments or cannot be combined with other elements in some embodiments.

[0014] In addition, in the accompanying drawings, connecting elements (such as solid or dotted lines or arrows, etc.) are used to illustrate the connection, relationship or association between or among two or more other schematic elements, and the absence of any such connecting elements is not intended to mean that there can be no connection, relationship or association. In other words, some connections, relationships or associations between elements are not illustrated in the accompanying drawings so as not to obscure the present disclosure. In addition, for ease of illustration, a single connecting element can be used to represent multiple connections, relationships or associations between elements. For example, if the connecting element represents the communication of a signal, data or instruction (e.g., "software instruction"), it will be understood by those skilled in the art that such an element can represent one or more than one signal path (e.g., bus) that may be needed to affect the communication.

[0015] Although the terms "first", "second" and / or "third", etc. are used to describe various elements, these elements should not be limited by these terms. The terms "first", "second" and / or "third" are only used to distinguish one element from another. For example, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact without departing from the scope of the described embodiments. Both the first contact and the second contact are contacts, but they are not the same contacts.

[0016] The terms used in the specification of the various embodiments described herein are included only for the purpose of describing specific embodiments and are not intended to be limiting. As used in the specification of the various embodiments described and the appended claims, the singular forms "a", "an" and "the" are also intended to include plural forms and can be used interchangeably with "one or more than one" or "at least one", unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more than one of the associated listed items. It will also be understood that when the terms "include", "comprise", "have" and / or "have" are used in this specification, the stated features, integers, steps, operations, elements and / or components are specifically stated, but the presence or addition of one or more than one other features, integers, steps, operations, elements, components and / or groups thereof are not excluded.

[0017] As used herein, the terms "communication" and "communicating" refer to at least one of receiving, receiving, transmitting, transmitting and / or providing information (or information represented by, for example, data, signals, messages, instructions and / or commands, etc.). For a unit (e.g., a device, a system, a component of a device or system, and / or a combination thereof) to communicate with another unit, this means that the unit is able to directly or indirectly receive information from the other unit and / or send (e.g., transmit) information to the other unit. This can refer to a direct or indirect connection that is wired and / or wireless in nature. In addition, even if the transmitted information can be modified, processed, relayed and / or routed between the first unit and the second unit, the two units can communicate with each other. For example, even if the first unit passively receives information and does not actively transmit information to the second unit, the first unit can communicate with the second unit. As another example, if at least one intermediary unit (e.g., a third unit located between the first unit and the second unit) processes the information received from the first unit and transmits the processed information to the second unit, the first unit can communicate with the second unit. In some embodiments, a message may refer to a network packet (eg, a data packet, etc.) that includes data.

[0018] As used herein, the term "if" is optionally interpreted to mean "when," "at," "in response to being determined to be," and / or "in response to being detected," etc., depending on the context. Similarly, the phrases "if it is determined" or "if [the stated condition or event] is detected" are optionally interpreted to mean "when determining," "in response to being determined to be" or "when [the stated condition or event] is detected," and / or "in response to being detected," etc., depending on the context. In addition, as used herein, the terms "have," "have," or "possess," etc. are intended to be open-ended terms. Furthermore, unless expressly stated otherwise, the phrase "based on" is intended to mean "based at least in part on."

[0019] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various embodiments described. However, it will be apparent to one of ordinary skill in the art that the various embodiments described may be implemented without these specific details. In other cases, well-known methods, processes, components, circuits, and networks have not yet been described in detail in order not to unnecessarily obscure aspects of the embodiments.

[0020] General Overview

[0021] In some aspects and / or embodiments, the systems, methods, and computer program products described herein include and / or implement techniques for identifying faults of AVs for rule violations for use in data processing and other applications.

[0022] Agents used in simulation tools typically follow predefined paths and thus may not react to decisions made by the simulated AV. This can result in a large number of traffic conflicts in the simulation that are not the AV's fault. A traffic conflict is any potential accident situation including, but not limited to, evasive actions taken by the AV (e.g., braking, weaving) and traffic violations (e.g., running a red light). Rulemaking for traffic conflicts may not take into account the responsibility of the agents in the environment during a traffic conflict, resulting in the AV behavior being penalized for any traffic conflict with the agent, even if the traffic conflict was initiated by the agent. This issue becomes a problem during simulation / testing of the AV to establish whether the AV's behavior is acceptable.

[0023] In an embodiment, determining whether a rule violation that results in a traffic conflict is the fault of the AV or the agent includes three steps. First, the simulation tool determines whether the situation is "reasonably foreseeable," which may be defined by an applicable standard such as the IEEE P2846 standard. Any desired standard or process may be used, as long as it imposes constraints on the behavior of the agents around the AV to give clear boundaries of what the AV should be able to handle in a given situation. If the situation is not reasonably foreseeable, the rule violation should not be considered "at fault" to the disadvantage of the AV.

[0024] Second, the simulation tool determines whether the AV's rule violation involves a vulnerable road user (VRU), which is a specific class of agents (e.g., pedestrians, cyclists), and whether the VRU's behavior is reasonably foreseeable. If the AV's rule violation is part of an interaction with a VRU and the VRU's behavior is not reasonably foreseeable, the AV's rule violation should not be considered against the AV.

[0025] Third, the simulation tool determines whether the non-VRU agents violate rules by applying at least one rulebook (e.g., the same rulebook as applied to the AV) to other agents (e.g., to actions or movements taken by other agents) to check whether the non-VRU agents violate rules as part of their respective interactions with the AV. If the agent violates a rule that is higher in priority than the rule violated by the AV (except for the case where both the AV and the agent violate the highest level rule and / or a predefined non-violation rule), the AV's rule violation is not considered to be at fault against the AV.

[0026] Priority in this context refers to the importance of a rule compared to other rules in the rulebook priority structure. In some instances, a lower priority rule can be violated to avoid violating a higher priority rule. For example, if a first rule has a higher priority than a second rule, the rulebook priority structure means that a trajectory that violates the second rule but does not violate the first rule is preferred to any trajectory that violates the first rule.

[0027] In an embodiment, at-fault-aware on-vehicle decisions may be made based on the prioritization of at-fault and no-fault traffic conflicts in a rulebook priority structure that takes into account responsibility for the traffic conflict. For example, to determine the initiator of a traffic conflict involving an AV and at least one agent, the traffic conflicts are classified by cataloging the rules violated by both the AV and the (at least one) agent that caused the traffic conflict. In some rulebook formulations, if at least one vehicle does not violate at least one rule, it is unlikely that a traffic conflict will occur. However, it is more likely that each vehicle may violate more than one rule.

[0028] After determining which vehicle first violated any rules that led to the traffic conflict, the process determines which vehicle first violated the most important rule (relative to the other rules violated) that led to the traffic conflict. In the event that both vehicles violated at least one rule, the process determines which vehicle first violated the highest priority rule that led to the traffic conflict. In the scenario where the leading vehicle brakes in front of the trailing vehicle, the trailing vehicle violated the more important rule to maintain a gap ahead. Thus, in this scenario, the trailing vehicle is considered the initiator of the traffic conflict.

[0029] In an embodiment, a method includes: using at least one processor to determine that a vehicle violated at least one rule that caused a traffic conflict involving at least one agent; using the at least one processor to determine that the behavior of the at least one agent was reasonably foreseeable; using the at least one processor to determine that the at least one agent is not a vulnerable road user; using the at least one processor to determine that the at least one agent did not violate a higher priority rule that caused the traffic conflict than the at least one rule violated by the vehicle; and using the at least one processor to designate the vehicle as being at fault for the traffic conflict.

[0030] In an embodiment, the method further comprises: utilizing the at least one processor to increment a count of traffic conflicts determined to be the fault of the vehicle; and comparing the count to a statistical safety target to assess whether the vehicle maintains a positive risk balance compared to an average human driver.

[0031] In an embodiment, assessing whether the vehicle maintains a positive risk balance compared to an average human driver further comprises: determining a rate of traffic conflicts caused by the vehicle; and determining that the rate is lower than an average rate of traffic conflicts caused by human drivers.

[0032] In an embodiment, the rate of traffic conflicts caused by the vehicle is determined based on at least one of the number of actual traffic conflicts of the vehicle and the number of human takeovers to prevent traffic conflicts per specified distance, wherein the specified distance is driven by the vehicle.

[0033] In an embodiment, determining, using the at least one processor, that the at least one agent has not violated a higher priority rule than the at least one rule violated by the vehicle that caused the traffic conflict further includes: evaluating a rulebook priority structure for describing responsibilities to determine that the at least one agent has not violated a higher priority rule than the at least one rule violated by the vehicle that caused the traffic conflict.

[0034] In an embodiment, a method includes: using at least one processor to determine a first trajectory for a vehicle in an environment; using the at least one processor to determine a second trajectory for the vehicle in the environment; using the at least one processor to evaluate the first trajectory and the second trajectory using a rule set, the evaluation including: using the at least one processor to determine, based on the first trajectory, that the vehicle will violate a first rule in the rule set, and the violation of the first rule will result in a traffic conflict with at least one intelligent agent in the environment; using the at least one processor to determine, based on the second trajectory, that the vehicle will violate the first rule in the rule set, and the violation of the first rule will result in a traffic conflict with at least one intelligent agent in the environment. traffic conflict with at least one intelligent agent; using the at least one processor, determining based on the first trajectory that the at least one intelligent agent will violate a second rule in the rule set, and the violation of the second rule will result in a traffic conflict with the vehicle in the environment; using the at least one processor, determining based on the second trajectory that the at least one intelligent agent will violate a third rule in the rule set, and the violation of the third rule will result in a traffic conflict with the vehicle in the environment; using the at least one processor, determining that the second rule has a lower priority than the first rule and the third rule has a higher priority than the first rule; and using the control circuit of the vehicle, causing the vehicle to travel according to the first trajectory.

[0035] In an embodiment, a system includes: at least one processor; and a memory storing instructions, which, when executed by the at least one processor, cause the at least one processor to perform any one of the aforementioned methods.

[0036] In an embodiment, a method includes: using at least one processor to determine a first set of rules that is violated by a vehicle and causes a traffic conflict; using the at least one processor to determine a second set of rules that is violated by at least one agent and causes the traffic conflict; using the at least one processor to determine which of the vehicle and the agent is at fault for the traffic conflict by: using the at least one processor to determine which of the vehicle and the at least one agent first violates any rule in their respective rule sets that causes the traffic conflict; using the at least one processor to determine which of the vehicle and the at least one agent first violates the highest priority rule in their respective rule sets that causes the traffic conflict, wherein the highest priority rule is a rule that cannot be violated before violating any other rule in the rule set; and using the at least one processor to determine which of the vehicle and the agent is at fault for the traffic conflict based on whether the vehicle or the at least one agent first violates a rule in their respective rule sets and whether the vehicle or the at least one agent first violates their respective highest priority rule in their respective rule sets that causes the traffic conflict.

[0037] In one embodiment, in the event of a violation of at least one rule in the first rule set or the second rule set, the at least one rule is designated as indicating a traffic conflict.

[0038] In one embodiment, a frequency of violations of the at least one rule designated as indicating a traffic conflict in the event that the at least one rule is violated is tracked as a performance metric for evaluating safety performance for the first vehicle or the second vehicle.

[0039] By means of implementation of the systems, methods, and computer program products described herein, techniques for identifying faults for rule violations for AVs may provide at least the following advantages.

[0040] By determining that the rule violation is due to the agent behavior rather than the AV behavior, engineering analysis of the AV behavior can be avoided and engineering resources can be deployed on other development tasks.

[0041] AV stack development is improved by filtering out situations where the AV is not at fault during the AV’s simulation. AV simulation is also improved by directly encoding constraints on the behavior of other agents to create only reasonably foreseeable situations.

[0042] The accumulation of public road mileage for statistical validation is improved by not unnecessarily restarting large amounts of validation work due to no-fault traffic conflicts.

[0043] The evaluation of on-vehicle decisions is improved by avoiding unnecessary changes to on-vehicle decision-making algorithms due to no-fault traffic conflicts.

[0044] Methods are provided for making behavioral norms for AVs sensitive to fault (eg, establishing some relative priority between at-fault and no-fault traffic conflicts).

[0045] Reference now Figure 1 , illustrates an example environment 100 in which vehicles including autonomous systems and vehicles not including autonomous systems operate. As illustrated, the environment 100 includes vehicles 102a-102n, objects 104a-104n, routes 106a-106n, areas 108, vehicle-to-infrastructure (V2I) devices 110, a network 112, a remote AV system 114, a fleet management system 116, and a V2I system 118. The vehicles 102a-102n, the vehicle-to-infrastructure (V2I) devices 110, the network 112, the AV system 114, the fleet management system 116, and the V2I system 118 are interconnected (e.g., establish connections for communication, etc.) via wired connections, wireless connections, or a combination of wired or wireless connections. In some embodiments, the objects 104a-104n are interconnected with at least one of the vehicles 102a-102n, the vehicle-to-infrastructure (V2I) devices 110, the network 112, the AV system 114, the fleet management system 116, and the V2I system 118 via a wired connection, a wireless connection, or a combination of wired or wireless connections.

[0046] Vehicles 102a-102n (individually referred to as vehicles 102 and collectively referred to as vehicles 102) include at least one device configured to transport goods and / or people. In some embodiments, vehicles 102 are configured to communicate with V2I devices 110, remote AV systems 114, fleet management systems 116, and / or V2I systems 118 via network 112. In some embodiments, vehicles 102 include cars, buses, trucks, and / or trains, etc. In some embodiments, vehicles 102 are similar to vehicles 200 described herein (see Figure 2 ). In some embodiments, vehicles 200 in the set of vehicles 200 are associated with an autonomous queue manager. In some embodiments, vehicles 102 travel along respective routes 106a-106n (individually referred to as routes 106 and collectively referred to as routes 106) as described herein. In some embodiments, one or more vehicles 102 include an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system 202).

[0047] Objects 104a-104n (individually referred to as objects 104 and collectively referred to as objects 104) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, and / or at least one structure (e.g., a building, a sign, a fire hydrant, etc.), etc. Each object 104 is stationary (e.g., located at a fixed location and over a period of time) or moves (e.g., has a speed and is associated with at least one trajectory). In some embodiments, objects 104 are associated with corresponding locations in area 108.

[0048] Routes 106a-106n (individually referred to as routes 106 and collectively referred to as routes 106) are each associated with (e.g., specify a series of actions (also referred to as trajectories) connecting states along which the AV can navigate. Each route 106 begins at an initial state (e.g., a state corresponding to a first spatiotemporal location and / or speed, etc.) and ends at a final target state (e.g., a state corresponding to a second spatiotemporal location different from the first spatiotemporal location) or a target zone (e.g., a subspace of acceptable states (e.g., terminal states)). In some embodiments, the first state includes a location where one or more individuals will board the AV, and the second state or zone includes one or more locations where one or more individuals boarding the AV will disembark. In some embodiments, routes 106 include multiple acceptable state sequences (e.g., multiple spatiotemporal location sequences) that are associated with (e.g., define multiple trajectories). In an example, routes 106 include only high-level actions or imprecise state locations, such as a series of connecting roads indicating a change of direction at a roadway intersection, etc. Additionally or alternatively, the route 106 may include more precise actions or states, such as, for example, specific target lanes or precise locations within lane regions and target speeds at those locations, etc. In an example, the route 106 includes a plurality of precise state sequences along at least one high-level action with a limited look-ahead horizon to an intermediate target, wherein a combination of consecutive iterations of the limited horizon state sequences cumulatively correspond to a plurality of trajectories that collectively form a high-level route terminating at a final target state or region.

[0049] The area 108 includes a physical area (e.g., a geographic region) in which the vehicle 102 can navigate. In an example, the area 108 includes at least one state (e.g., a country, a province, a separate state of a plurality of states included in a country, etc.), at least a portion of a state, at least one city, at least a portion of a city, etc. In some embodiments, the area 108 includes at least one named thoroughfare (referred to herein as a "road"), such as a highway, an interstate highway, a parkway, a city street, etc. Additionally or alternatively, in some examples, the area 108 includes at least one unnamed road, such as a driveway, a section of a parking lot, a section of an open space and / or undeveloped area, a dirt road, etc. In some embodiments, the road includes at least one lane (e.g., a portion of the road that the vehicle 102 can traverse). In an example, the road includes at least one lane associated with (e.g., identified based on) at least one lane marking line.

[0050] The vehicle-to-infrastructure (V2I) device 110 (sometimes referred to as a vehicle-to-everything (V2X) device) includes at least one device configured to communicate with the vehicle 102 and / or the V2I system 118. In some embodiments, the V2I device 110 is configured to communicate with the vehicle 102, the remote AV system 114, the queue management system 116, and / or the V2I system 118 via the network 112. In some embodiments, the V2I device 110 includes a radio frequency identification (RFID) device, a sign, a camera (e.g., a two-dimensional (2D) and / or three-dimensional (3D) camera), lane markings, street lights, parking meters, etc. In some embodiments, the V2I device 110 is configured to communicate directly with the vehicle 102. Additionally or alternatively, in some embodiments, the V2I device 110 is configured to communicate with the vehicle 102, the remote AV system 114, and / or the queue management system 116 via the V2I system 118. In some embodiments, the V2I device 110 is configured to communicate with the V2I system 118 via the network 112 .

[0051] The network 112 includes one or more wired and / or wireless networks. In an example, the network 112 includes a cellular network (e.g., a long-term evolution (LTE) network, a third generation (3G) network, a fourth generation (4G) network, a fifth generation (5G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., a public switched telephone network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber-based network, a cloud computing network, etc., and / or a combination of some or all of these networks, etc.

[0052] The remote AV system 114 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the network 112, the fleet management system 116, and / or the V2I system 118 via the network 112. In an example, the remote AV system 114 includes a server, a server group, and / or other similar devices. In some embodiments, the remote AV system 114 is co-located with the fleet management system 116. In some embodiments, the remote AV system 114 participates in the installation of some or all of the components of the vehicle (including autonomous systems, autonomous vehicle computing, and / or software implemented by autonomous vehicle computing, etc.). In some embodiments, the remote AV system 114 maintains (e.g., updates and / or replaces) these components and / or software during the life of the vehicle.

[0053] The queue management system 116 includes at least one device configured to communicate with the vehicles 102, the V2I devices 110, the remote AV system 114, and / or the V2I system 118. In an example, the queue management system 116 includes a server, a server group, and / or other similar devices. In some embodiments, the queue management system 116 is associated with a ridesharing company (e.g., an organization for controlling the operation of multiple vehicles (e.g., vehicles including autonomous systems and / or vehicles not including autonomous systems), etc.).

[0054] In some embodiments, the V2I system 118 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the remote AV system 114, and / or the fleet management system 116 via the network 112. In some examples, the V2I system 118 is configured to communicate with the V2I device 110 via a connection other than the network 112. In some embodiments, the V2I system 118 includes a server, a server group, and / or other similar devices. In some embodiments, the V2I system 118 is associated with a municipality or a private agency (e.g., a private agency for maintaining the V2I device 110, etc.).

[0055] supply Figure 1 The number and arrangement of elements illustrated are examples. Figure 1 There may be additional elements, fewer elements, different elements, and / or differently arranged elements than those illustrated. Additionally or alternatively, at least one element of environment 100 may be described as being Figure 1 Additionally or alternatively, at least one set of elements of environment 100 may perform one or more functions described as being performed by at least one different set of elements of environment 100.

[0056] Reference now Figure 2 , vehicle 200 includes autonomous system 202, powertrain control system 204, steering control system 206, and braking system 208. In some embodiments, vehicle 200 is compatible with vehicle 102 (see Figure 1 ) is the same or similar. In some embodiments, the vehicle 200 has autonomous capabilities (e.g., implementing at least one function, feature and / or device, etc., which enables the vehicle 200 to operate partially or completely without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon reliance on human intervention) and / or highly autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in certain situations), etc.). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference can be made to SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, the entire contents of which are incorporated by reference. In some embodiments, the vehicle 200 is associated with an autonomous queue manager and / or a ridesharing company.

[0057] Autonomous system 202 includes a sensor suite that includes one or more devices such as camera 202a, LiDAR sensor 202b, Radar sensor 202c, and microphone 202d. In some embodiments, autonomous system 202 may include more or fewer devices and / or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), and / or odometer sensors for generating data associated with an indication of the distance that vehicle 200 has traveled, etc.). In some embodiments, autonomous system 202 uses one or more devices included in autonomous system 202 to generate data associated with environment 100 described herein. Data generated by one or more devices of autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which vehicle 200 is located. In some embodiments, autonomous system 202 includes communication device 202e, autonomous vehicle computing 202f, safety controller 202g, and drive-by-wire (DBW) system 202h.

[0058] The camera 202a includes a communication device 202e, an autonomous vehicle computer 202f, and / or a safety controller 202g configured to communicate with the communication device 202e via a bus (e.g., Figure 3 The camera 202a includes at least one device for communicating with the autonomous vehicle computing 202f (e.g., an image processing unit 202a, a bus ... Figure 1 In some embodiments, the autonomous vehicle computing 202f determines a depth to one or more objects in a field of view of at least two of the plurality of cameras based on image data from the at least two cameras. In some embodiments, the camera 202a is configured to capture images of objects within a distance relative to the camera 202a (e.g., up to 100 meters and / or up to 1 kilometer, etc.). Thus, the camera 202a includes features such as sensors and lenses that are optimized for sensing objects at one or more distances relative to the camera 202a.

[0059] In an embodiment, the camera 202a includes at least one camera configured to capture one or more images associated with one or more traffic lights, street signs, and / or other physical objects that provide visual navigation information. In some embodiments, the camera 202a generates traffic light data associated with the one or more images. In some examples, the camera 202a generates TLD data associated with one or more images including a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, the camera 202a that generates TLD data differs from other systems incorporating cameras described herein in that the camera 202a may include one or more cameras with a wide field of view (e.g., a wide-angle lens, a fisheye lens, and / or a lens with a viewing angle of approximately 120 degrees or greater, etc.) to generate images related to as many physical objects as possible.

[0060] The laser detection and ranging (LiDAR) sensor 202b includes a communication device 202e, an autonomous vehicle computing device 202f, and / or a safety controller 202g configured to communicate with the communication device 202e via a bus (e.g., Figure 3 The LiDAR sensor 202b includes at least one device that communicates with a bus (the same or similar bus as the bus 302 of the embodiment of the present invention). The LiDAR sensor 202b includes a system configured to emit light from a light emitter (e.g., a laser emitter). The light emitted by the LiDAR sensor 202b includes light outside the visible spectrum (e.g., infrared light, etc.). In some embodiments, during operation, the light emitted by the LiDAR sensor 202b encounters a physical object (e.g., a vehicle) and is reflected back to the LiDAR sensor 202b. In some embodiments, the light emitted by the LiDAR sensor 202b does not penetrate the physical object encountered by the light. The LiDAR sensor 202b also includes at least one light detector that detects the light emitted from the light emitter after encountering the physical object. In some embodiments, at least one data processing system associated with the LiDAR sensor 202b generates an image (e.g., a point cloud and / or a combined point cloud, etc.) representing objects included in the field of view of the LiDAR sensor 202b. In some examples, at least one data processing system associated with the LiDAR sensor 202b generates an image representing the boundaries of the physical object and / or the surface of the physical object (e.g., the topology of the surface), etc. In such examples, the image is used to determine the boundaries of the physical object in the field of view of the LiDAR sensor 202b.

[0061] The radio detection and ranging (Radar) sensor 202c includes a sensor configured to communicate with the communication device 202e, the autonomous vehicle computing device 202f, and / or the safety controller 202g via a bus (e.g., Figure 3At least one device for communicating with a bus (same or similar bus as bus 302 of the embodiment of the present invention). Radar sensor 202c includes a system configured to transmit (pulsed or continuous) radio waves. The radio waves transmitted by Radar sensor 202c include radio waves within a predetermined spectrum. In some embodiments, during operation, the radio waves transmitted by Radar sensor 202c encounter physical objects and are reflected back to Radar sensor 202c. In some embodiments, the radio waves transmitted by Radar sensor 202c are not reflected by some objects. In some embodiments, at least one data processing system associated with Radar sensor 202c generates a signal representing an object included in the field of view of Radar sensor 202c. For example, at least one data processing system associated with Radar sensor 202c generates an image representing the boundary of a physical object and / or the surface of a physical object (e.g., the topology of the surface), etc. In some examples, the image is used to determine the boundary of a physical object in the field of view of Radar sensor 202c.

[0062] The microphone 202d includes a microphone configured to communicate with the communication device 202e, the autonomous vehicle computing device 202f, and / or the safety controller 202g via a bus (e.g., Figure 3 At least one device for communicating with the vehicle 200 (the same or similar bus as bus 302 of FIG. 1 ). Microphone 202d includes one or more microphones (e.g., an array microphone and / or an external microphone, etc.) that capture an audio signal and generate data associated with (e.g., representing) the audio signal. In some examples, microphone 202d includes a transducer device and / or the like. In some embodiments, one or more systems described herein can receive the data generated by microphone 202d and determine the position (e.g., distance, etc.) of an object relative to vehicle 200 based on an audio signal associated with the data.

[0063] The communication device 202e includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the autonomous vehicle computing 202f, the safety controller 202g, and / or the drive-by-wire (DBW) system 202h. For example, the communication device 202e may include at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the autonomous vehicle computing 202f, the safety controller 202g, and / or the drive-by-wire (DBW) system 202h. Figure 3 The communication device 202e may be a device that is the same as or similar to the communication interface 314 of the vehicle. In some embodiments, the communication device 202e includes a vehicle-to-vehicle (V2V) communication device (eg, a device for enabling wireless communication of data between vehicles).

[0064] Autonomous vehicle computing 202f includes at least one device configured to communicate with camera 202a, LiDAR sensor 202b, Radar sensor 202c, microphone 202d, communication device 202e, safety controller 202g, and / or DBW system 202h. In some examples, autonomous vehicle computing 202f includes devices such as client devices, mobile devices (e.g., cellular phones and / or tablet computers, etc.), and / or servers (e.g., computing devices including one or more central processing units and / or graphics processing units, etc.). In some embodiments, autonomous vehicle computing 202f is the same or similar to autonomous vehicle computing 400 described herein. Additionally or alternatively, in some embodiments, autonomous vehicle computing 202f is configured to communicate with an autonomous vehicle system (e.g., with Figure 1 remote AV system 114 of the same or similar autonomous vehicle system), a fleet management system (e.g., Figure 1 of the same or similar queue management system as the queue management system 116), V2I devices (e.g., Figure 1 V2I device 110 that is the same as or similar to V2I device 110) and / or V2I system (e.g., Figure 1 The V2I system 118 may communicate with the same or similar V2I system.

[0065] Safety controller 202g includes at least one device configured to communicate with camera 202a, LiDAR sensor 202b, Radar sensor 202c, microphone 202d, communication device 202e, autonomous vehicle computing 202f, and / or DBW system 202h. In some examples, safety controller 202g includes one or more controllers (electrical controllers and / or electromechanical controllers, etc.) configured to generate and / or transmit control signals to operate one or more devices of vehicle 200 (e.g., powertrain control system 204, steering control system 206, and / or braking system 208, etc.). In some embodiments, safety controller 202g is configured to generate control signals that take precedence over (e.g., override) control signals generated and / or transmitted by autonomous vehicle computing 202f.

[0066] The DBW system 202h includes at least one device configured to communicate with the communication device 202e and / or the autonomous vehicle computing 202f. In some examples, the DBW system 202h includes one or more controllers (e.g., electrical controllers and / or electromechanical controllers, etc.) configured to generate and / or transmit control signals to operate one or more devices of the vehicle 200 (e.g., powertrain control system 204, steering control system 206, and / or braking system 208, etc.). Additionally or alternatively, one or more controllers of the DBW system 202h are configured to generate and / or transmit control signals to operate at least one different device of the vehicle 200 (e.g., turn signals, headlights, door locks, and / or windshield wipers, etc.).

[0067] The powertrain control system 204 includes at least one device configured to communicate with the DBW system 202h. In some examples, the powertrain control system 204 includes at least one controller and / or actuator, etc. In some embodiments, the powertrain control system 204 receives a control signal from the DBW system 202h, and the powertrain control system 204 causes the vehicle 200 to start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate in a certain direction, decelerate in a certain direction, turn left and / or turn right, etc. In an example, the powertrain control system 204 increases, maintains the same, or decreases the energy (e.g., fuel and / or electricity, etc.) provided to the motor of the vehicle, thereby rotating or not rotating at least one wheel of the vehicle 200.

[0068] The steering control system 206 includes at least one device configured to rotate one or more wheels of the vehicle 200. In some examples, the steering control system 206 includes at least one controller and / or actuator, etc. In some embodiments, the steering control system 206 rotates the two front wheels and / or the two rear wheels of the vehicle 200 to the left or right to turn the vehicle 200 left or right.

[0069] Braking system 208 includes at least one device configured to actuate one or more brakes to decelerate and / or hold vehicle 200 stationary. In some examples, braking system 208 includes at least one controller and / or actuator configured to cause one or more calipers associated with one or more wheels of vehicle 200 to close on respective rotors of vehicle 200. Additionally or alternatively, in some examples, braking system 208 includes an automatic emergency braking (AEB) system and / or a regenerative braking system, etc.

[0070] In some embodiments, vehicle 200 includes at least one platform sensor (not explicitly illustrated) for measuring or inferring a property of a state or condition of vehicle 200. In some examples, vehicle 200 includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), wheel rate sensors, wheel brake pressure sensors, wheel torque sensors, engine torque sensors, and / or steering angle sensors.

[0071] Reference now Figure 3 , a schematic diagram of an exemplary device 300. As illustrated, device 300 includes a computer processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, a communication interface 314, and a bus 302. In some embodiments, device 300 corresponds to: at least one device of vehicle 102 (e.g., at least one device of a system of vehicle 102); at least one device of network 112 and / or one or more devices (e.g., one or more devices of a system of network 112). In some embodiments, one or more devices of vehicle 102 (e.g., one or more devices of a system of vehicle 102) and / or one or more devices of network 112 (e.g., one or more devices of a system of network 112) include at least one device 300 and / or at least one component of device 300. As Figure 3 As shown, apparatus 300 includes a bus 302 , a computer processor 304 , a memory 306 , a storage component 308 , an input interface 310 , an output interface 312 , and a communication interface 314 .

[0072] The bus 302 includes components for communication between components of the licensing device 300. In some embodiments, the computer processor 304 is implemented in hardware, software, or a combination of hardware and software. In some examples, the computer processor 304 includes a computer processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), and / or an accelerated processing unit (APU), etc.), a microphone, a digital signal processor (DSP), and / or any processing component that can be programmed to perform at least one function (e.g., a field programmable gate array (FPGA) and / or an application specific integrated circuit (ASIC), etc.). The memory 306 includes a random access memory (RAM), a read-only memory (ROM), and / or another type of dynamic and / or static storage device (e.g., flash memory, magnetic memory, and / or optical memory, etc.) that stores data and / or instructions for use by the computer processor 304.

[0073] Storage component 308 stores data and / or software related to the operation and use of device 300. In some examples, storage component 308 includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid-state disk, etc.), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cassette, a tape, a CD-ROM, a RAM, a PROM, an EPROM, a FLASH-EPROM, an NV-RAM, and / or another type of computer-readable medium, and a corresponding drive.

[0074] The input interface 310 includes components that permit the device 300 to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, and / or a camera, etc.). Additionally or alternatively, in some embodiments, the input interface 310 includes a sensor for sensing information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, and / or an actuator, etc.). The output interface 312 includes components for providing output information from the device 300 (e.g., a display, a speaker, and / or one or more light emitting diodes (LEDs), etc.).

[0075] In some embodiments, communication interface 314 includes a transceiver-like component (e.g., a transceiver and / or a separate receiver and transmitter, etc.) that permits device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of a wired connection and a wireless connection. In some examples, communication interface 314 permits device 300 to receive information from another device and / or provide information to another device. In some examples, communication interface 314 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, interface and / or cellular network interface, etc.

[0076] In some embodiments, the device 300 performs one or more processes described herein. The device 300 performs these processes based on the computer processor 304 executing software instructions stored by a computer-readable medium such as a memory 306 and / or a storage component 308. Computer-readable media (e.g., non-transitory computer-readable media) are defined herein as non-transitory memory devices. Non-transitory memory devices include storage space located within a single physical storage device or storage space distributed across multiple physical storage devices.

[0077] In some embodiments, the software instructions are read into the memory 306 and / or storage component 308 from another computer-readable medium or from another device via the communication interface 314. The software instructions stored in the memory 306 and / or storage component 308, when executed, cause the computer processor 304 to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry is used in place of or in combination with the software instructions to perform one or more processes described herein. Therefore, unless expressly stated otherwise, the embodiments described herein are not limited to any specific combination of hardware circuitry and software.

[0078] The memory 306 and / or the storage component 308 include a data storage unit or at least one data structure (e.g., a database, etc.). The device 300 can receive information from the data storage unit or at least one data structure in the memory 306 or the storage component 308, store information in the data storage unit or at least one data structure, communicate information to the data storage unit or at least one data structure, or search for information stored in the data storage unit or at least one data structure. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0079] In some embodiments, the device 300 is configured to execute software instructions stored in the memory 306 and / or the memory of another device (e.g., another device that is the same or similar to the device 300). As used herein, the term "module" refers to at least one instruction stored in the memory 306 and / or the memory of another device, which, when executed by the computer processor 304 and / or the computer processor of another device (e.g., another device that is the same or similar to the device 300), causes the device 300 (e.g., at least one component of the device 300) to perform one or more processes described herein. In some embodiments, the module is implemented in software, firmware, and / or hardware, etc.

[0080] supply Figure 3 The number and arrangement of components illustrated are examples. Figure 3 The device 300 may include additional components, fewer components, different components, or differently arranged components than those illustrated. Additionally or alternatively, a set of components (e.g., one or more components) of the device 300 may perform one or more functions described as being performed by another component or set of components of the device 300.

[0081] Reference now Figure 4, illustrates an example block diagram of an AV computing 400 (sometimes referred to as an "AV stack"). As illustrated, the AV computing 400 includes a perception system 402 (sometimes referred to as a perception module), a planning system 404 (sometimes referred to as a planning module), a positioning system 406 (sometimes referred to as a positioning module), a control system 408 (sometimes referred to as a control module), and a database 410. In some embodiments, the perception system 402, the planning system 404, the positioning system 406, the control system 408, and the database 410 are included in and / or implemented in an automatic navigation system of a vehicle (e.g., the autonomous vehicle computing 202f of the vehicle 200). Additionally or alternatively, in some embodiments, the perception system 402, the planning system 404, the positioning system 406, the control system 408, and the database 410 are included in one or more independent systems (e.g., one or more systems that are the same or similar to the autonomous vehicle computing 400, etc.). In some examples, the perception system 402, planning system 404, positioning system 406, control system 408, and database 410 are included in one or more independent systems located in the vehicle and / or at least one remote system as described herein. In some embodiments, any and / or all of the systems included in the autonomous vehicle computing 400 are implemented in software (e.g., software instructions stored in a memory), computer hardware (e.g., by a microprocessor, microcontroller, application specific integrated circuit (ASIC) and / or field programmable gate array (FPGA), etc.), or a combination of computer software and computer hardware. It will also be understood that in some embodiments, the autonomous vehicle computing 400 is configured to communicate with a remote system (e.g., an autonomous vehicle system that is the same or similar to the remote AV system 114, a fleet management system 116 that is the same or similar to the fleet management system 116, and / or a V2I system that is the same or similar to the V2I system 118, etc.).

[0082] In some embodiments, the perception system 402 receives data associated with at least one physical object in the environment (e.g., data used by the perception system 402 to detect at least one physical object) and classifies the at least one physical object. In some examples, the perception system 402 receives image data captured by at least one camera (e.g., camera 202a), the image being associated with (e.g., representing) one or more physical objects within the field of view of the at least one camera. In such examples, the perception system 402 classifies the at least one physical object based on one or more groups of physical objects (e.g., bicycles, vehicles, traffic signs, and / or pedestrians, etc.). In some embodiments, based on the classification of the physical object by the perception system 402, the perception system 402 transmits data associated with the classification of the physical object to the planning system 404.

[0083] In some embodiments, planning system 404 receives data associated with a destination and generates data associated with at least one route (e.g., route 106) along which a vehicle (e.g., vehicle 102) can travel toward the destination. In some embodiments, planning system 404 periodically or continuously receives data (e.g., data associated with the classification of physical objects described above) from perception system 402, and planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by perception system 402. In some embodiments, planning system 404 receives data associated with an updated position of a vehicle (e.g., vehicle 102) from positioning system 406, and planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by positioning system 406.

[0084] In some embodiments, the positioning system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicle 102) in an area. In some examples, the positioning system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensor 202b). In some examples, the positioning system 406 receives data associated with at least one point cloud from multiple LiDAR sensors, and the positioning system 406 generates a combined point cloud based on each point cloud. In these examples, the positioning system 406 compares the at least one point cloud or the combined point cloud with a two-dimensional (2D) and / or three-dimensional (3D) map of the area stored in the database 410. Then, based on the positioning system 406 comparing the at least one point cloud or the combined point cloud with the map, the positioning system 406 determines the position of the vehicle in the area. In some embodiments, the map includes a combined point cloud of the area generated before the navigation of the vehicle. In some embodiments, the map includes, but is not limited to, a high-precision map of roadway geometry, a map describing the road network connectivity, a map describing the physical properties of the roadway (such as traffic speed, traffic volume, the number of vehicle and bicycle traffic lanes, lane width, lane traffic direction or the type and location of lane markings, or a combination thereof, etc.), and a map describing the spatial location of road features (such as crosswalks, traffic signs, or various types of other driving lights, etc.). In some embodiments, the map is generated in real time based on data received by the perception system.

[0085] In another example, positioning system 406 receives global navigation satellite system (GNSS) data generated by a global positioning system (GPS) receiver. In some examples, positioning system 406 receives GNSS data associated with a location of a vehicle in an area, and positioning system 406 determines the latitude and longitude of the vehicle in the area. In such an example, positioning system 406 determines the position of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, positioning system 406 generates data associated with the position of the vehicle. In some examples, based on the location of the vehicle determined by positioning system 406, positioning system 406 generates data associated with the position of the vehicle. In such an example, the data associated with the position of the vehicle include data associated with one or more semantic properties corresponding to the position of the vehicle.

[0086] In some embodiments, the control system 408 receives data associated with at least one trajectory from the planning system 404, and the control system 408 controls the operation of the vehicle. In some examples, the control system 408 receives data associated with at least one trajectory from the planning system 404, and the control system 408 controls the operation of the vehicle by generating and transmitting control signals to operate a powertrain control system (e.g., DBW system 202h and / or powertrain control system 204, etc.), a steering control system (e.g., steering control system 206), and / or a braking system (e.g., braking system 208). In the example, in the case where the trajectory includes a left turn, the control system 408 transmits a control signal to cause the steering control system 206 to adjust the steering angle of the vehicle 200, thereby causing the vehicle 200 to turn left. Additionally or alternatively, the control system 408 generates and transmits control signals to cause other devices of the vehicle 200 (e.g., headlights, turn signals, door locks, and / or windshield wipers, etc.) to change state.

[0087] In some embodiments, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model (e.g., at least one multi-layer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, and / or at least one transformer, etc.). In some examples, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model alone or in combination with one or more of the above systems. In some examples, perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model as part of a pipeline (e.g., a pipeline for identifying one or more objects located in an environment, etc.).

[0088] Database 410 stores data transmitted to, received from, and / or updated by perception system 402, planning system 404, positioning system 406, and / or control system 408. In some examples, database 410 includes a storage component (e.g., a storage component associated with at least one system of AV computing 400) for storing data and / or software related to operation and using AV computing 400. Figure 3 In some embodiments, database 410 stores data associated with a 2D and / or 3D map of at least one area. In some examples, database 410 stores data associated with a 2D and / or 3D map of a portion of a city, portions of multiple cities, multiple cities, counties, states, and / or countries (State) (e.g., a country), etc. In such an example, a vehicle (e.g., a vehicle that is the same or similar to vehicle 102 and / or vehicle 200) can be driven along one or more drivable areas (e.g., single-lane roads, multi-lane roads, highways, remote roads, and / or off-road roads, etc.) and cause at least one LiDAR sensor (e.g., a LiDAR sensor that is the same or similar to LiDAR sensor 202b) to generate data associated with an image representing an object included in the field of view of the at least one LiDAR sensor.

[0089] In some embodiments, database 410 can be implemented across multiple devices. In some examples, database 410 includes a vehicle (e.g., a vehicle that is the same or similar to vehicle 102 and / or vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system that is the same or similar to remote AV system 114), a fleet management system (e.g., a vehicle that is the same or similar to remote AV system 114), and a fleet management system (e.g., a vehicle that is the same or similar to remote AV system 114). Figure 1 The same or similar queue management system as the queue management system 116 of FIG. 1 and / or the V2I system (e.g., Figure 1 The V2I system 118 is the same as or similar to the V2I system 118).

[0090] Figure 5 is a flow chart of a process 500 for determining whether an AV is at fault for a rule violation. Figure 4The process 500 is performed by a planning system that is the same as or similar to the planning system 404 (e.g., completely, partially, etc.). In some embodiments, one or more of the steps of the process 500 are performed by another device or system, or another device and / or system group that is separate from or includes the planning system (e.g., completely and / or partially, etc.). For example, one or more of the steps of the process 500 may be performed by the remote AV system 114, the vehicle 200 (e.g., the autonomous system 202 of the vehicle 200), and / or the AV computing 400 (e.g., one or more systems of the AV computing 400) (e.g., completely and / or partially, etc.). In some embodiments, the steps of the process 500 may be performed in cooperation with each other between any of the above-mentioned systems.

[0091] Process 500 begins by determining whether a rule violation has been registered for the AV (501). For example, a planning system (e.g., the same or similar planning system as planning systems 404 and 700) generates trajectory suggestions for the AV based on a dynamic world model (e.g., modeling the AV, agents, road conditions, traffic, etc.). The planning system uses a priority structure (such as Figure 6 Trajectory suggestions are scored using one or more rulebooks associated with (e.g., defining a rulebook priority structure, etc., as illustrated) a rulebook priority structure that specifies responsibility for rule violations. Rules may be specified using logical expressions that, when satisfied or not satisfied, result in a rule violation. For example, an AV may have violated a speed limit or lateral gap rule by exceeding a posted speed limit or failing to maintain a specified gap distance relative to an agent, respectively.

[0092] Processing 500 determines whether the situation of violating the rules is reasonably foreseeable (501) and continues. In an embodiment, the above-mentioned planning system determines whether the situation of violating the rules is reasonably foreseeable. Whether the rules are reasonably foreseeable can be defined by the publicly available IEEE P2846 standard entitled "Assumptions for Models in Safety-Related Automated Vehicle Behavior". IEEE P2846 describes the minimum reasonable assumption set used in the development of safety-related models as part of an automated driving system (ADS) for foreseeable scenarios to be considered for road vehicles. The standard includes considerations of the rules of the road and the area dependence and / or time dependence of these rules. Thus, IEEE P2846 imposes restrictions on the behavior of the intelligent body around the AV to give a clear boundary of what the AV should be able to handle in a given situation. IEEE P2846 defines "reasonably foreseeable" as technically feasible and with a credible or measurable incidence. As used herein, AV or agent "behavior" is how the AV or agent reacts to other agents, static or dynamic objects, and the AV or agent's operating environment (including but not limited to: road conditions, traffic conditions, weather conditions, emergency scenarios, etc.).

[0093] If the rule violation is not reasonably foreseeable, the planning system does not consider the rule violation to be detrimental to the AV (502) or is not subject to further review in certain contexts (e.g., statistical verification of AV safety). If the rule violation is reasonably foreseeable, processing 500 continues with the planning system determining whether the rule violation involves a vulnerable road user (VRU) (503). Since VRUs are a specific class of agents, a rule violation by an AV should be considered detrimental to the AV if the behavior is reasonably foreseeable, regardless of whether the VRU violates the rules. An example VRU would be a pedestrian or a cyclist. If the rule violation involves a VRU, the rule violation is considered detrimental to the AV (504).

[0094] If the rule violation does not involve a VRU, process 500 is based on a rulebook that specifies responsibilities, such as Figure 6 The non-VRU agent is then used to determine whether the non-VRU agent has violated a higher priority rule than the rule violated by the AV (505). If the agent has violated a higher priority rule, the rule violation is not considered to be detrimental to the AV (506). If the non-VRU agent has violated a lower priority rule than the rule violated by the AV, the rule violation is considered to be detrimental to the AV (507).

[0095] The rulebook is applied to the agent to check whether the agent has violated a rule as part of the interaction with the AV. If the rule violated by the agent is higher in the rulebook than the rule violated by the AV, the rule violation by the AV should not be considered against the AV. If the rules violated by both the AV and the agent involve rules at the same priority level or in the same priority group (e.g., both violated a safety rule at the highest priority level), the rule violation is considered against the AV even if the fault for the traffic conflict is shared between the AV and the agent.

[0096] The above process 500 can be further explained by examples. In a first example, an agent (e.g., another vehicle) in the simulation runs a red light at an intersection and contacts an AV that is proceeding through a green light. In this example, since the agent violated the traffic law and the AV did not, the traffic conflict may not be considered disadvantageous to the AV.

[0097] The process 500 described above provides several advantages and benefits, including but not limited to: accelerating engineering development of the AV stack and improving on-vehicle decision making. In an embodiment, the process 500 can be added as a performance metric to a dashboard in a rulebook-based AV behavior assessment application to help separate the difficulty of the operational design domain from the actual performance of the AV.

[0098] In an embodiment, a count of traffic conflicts determined to be the fault of the vehicle is maintained based on designating the vehicle as being at fault for the traffic conflict. This count can then be compared to a statistical safety goal to assess whether the vehicle maintains a positive risk balance associated with an average human driver. For example, a positive risk balance exists if the AV meets a statistical safety goal that requires the AV's rate of unknown hazardous scenarios to be lower than (some number) X times the average rate of human collisions in urban areas of the United States with high confidence. In an embodiment, actual collisions per driven mile and / or collision prevention takeovers (CPTOs) can be used to measure the AV's rate of unknown hazardous scenarios.

[0099] In an embodiment, assessing whether a vehicle maintains a positive risk balance compared to an average human driver may be accomplished by determining a rate of traffic conflicts caused by the vehicle and then determining that the rate is lower than an average rate of traffic conflicts associated with human drivers.

[0100] In an embodiment, the frequency of violations of at least one rule is tracked as a performance metric for evaluating performance for the first vehicle or the second vehicle.

[0101] Figure 6A rulebook priority structure 600 is illustrated for AV route planning that takes into account responsibility for rule violations. While it may be preferred to avoid any traffic conflicts regardless of fault, it is desirable to be able to distinguish between at-fault and no-fault rule violations using the rulebook priority structure 600. In the example priority structure 600 shown, there are different priority groups of rules at different priority levels, where the priority increases as one moves up the priority structure 600, where the priority group "human safety" is the highest priority group of rules.

[0102] In this example, the human safety priority group is specified as having a fault component and a no-fault component. For example, by the planning system (e.g. Figure 4 and Figure 7 The trajectory scoring algorithm performed by the planning system 404 / 700 shown can be Figure 6 The rulebook priority structure 600 shown runs the trajectory recommendations and evaluates the extent to which the rules in each priority group have been violated by the trajectory, if any. Based on the extent to which the rules in each priority group have been violated, the planning system determines whether the AV is at fault.

[0103] In the priority structure 600, the lowest priority is progress toward the goal, followed by occupant comfort, followed by no victim rule violations and safety perception (e.g., maintaining gaps and predictability so that occupants feel safe), followed by helping traffic flow, followed by physical interactions and animal safety, followed by operational constraints (e.g., lane direction, drivable area), followed by property safety, and then human safety. In evaluating the human safety priority group, a reference is initiated. Figure 5 The process 500 is described to determine whether the AV is at fault.

[0104] For example, if an agent violates a speed limit in the No Victim Violation Priority Group of the rulebook priority structure 600 and collides with an AV that merges in front of the agent without signaling, thereby violating a traffic rule in the Physical Interaction Priority Group, the AV will be at fault for the traffic conflict because the situation was reasonably foreseeable, the situation did not involve a VRU, and the agent did not violate a higher priority rule than the rule violated by the AV.

[0105] Figure 7700 is a block diagram of an example planning system 700 capable of distinguishing fault for traffic conflicts according to an embodiment. The planning system 700 includes a rule-based trajectory evaluation tool 701, a rulebook 702, a semantic map 703, a reasonably foreseeable limit 704, and a fault evaluator 705. The planning system 700 can be applied in various scenarios, including but not limited to: real-world driving, simulation testing, replay of real-world data (e.g., re-simulation), and accumulation of public road mileage used for improved statistical verification. In an embodiment, the test of AV in the scene generates logs, which include but are not limited to: the trajectory (trace) of the tested AV, the trajectory (trace) of other intelligent agents (e.g., other vehicles, VRUs). In an embodiment, the trajectory can be a file (e.g., a JSON file) of the position, speed, and acceleration over time recorded on a semantic map with information related to lane markings, signs, traffic lights (and conditions over time), etc.

[0106] The rule-based trajectory assessment tool 701 implements the rules in the rulebook 702 for each trajectory and returns the degree of rule violation for each trajectory. Figure 6 As shown in the example rulebook in , the rulebook 702 contains traffic laws and other driving rules organized in a priority structure indicating the relative importance of each rule. Reasonable foreseeable restrictions 704 are constraints on the dynamic parameters (e.g., speed, acceleration / deceleration) of the agent (e.g., other vehicles, VRU) based on, for example, IEEE P2846. A fault assessor 705 assesses the agent behavior that caused the traffic conflict to determine whether the behavior is reasonably foreseeable and / or whether the agent violated a more important rule in the rulebook 702 than the rule violated by the AV.

[0107] In an embodiment, the rule-based trajectory assessment tool 701 takes the AV trajectory and the agent trajectory, the rule book 702, and the semantic map 703 as inputs, and generates the degree of rule violation for each trajectory. The fault assessor 705 takes the output of the rule-based trajectory assessment tool 701 and the reasonably foreseeable constraints 704 as inputs, and Figure 5 The fault logic shown is applied to these inputs to determine which of the AV and the agent(s) is at fault for the traffic conflict.

[0108] Figure 8 8 is a flow chart of a process 800 for fault identification for data processing, on-vehicle decision making, verification, and other applications. The process 800 may be performed by, for example, Figure 7 The planning system 700 is shown.

[0109] Process 800 begins by determining that a vehicle (e.g., an AV the same as or similar to vehicle 102) has violated at least one rule (e.g., stopped at a red light) that results in a traffic conflict with at least one agent (e.g., another vehicle) (801).

[0110] Process 800 continues by determining that the situation is reasonably foreseeable (802). For example, the determination of whether the situation is reasonably foreseeable can be based on comparing the situation to one or more rules specified by the publicly available IEEE P2846 standard, which describes the minimum set of reasonable assumptions used in the development of safety-related models as part of an ADS for foreseeable scenarios to be considered for road vehicles.

[0111] Process 800 continues by determining that the agent is not a VRU (803), and that the agent has not violated a higher priority rule that caused a traffic conflict than the rule violated by the vehicle (804). For example, a rulebook having a priority structure that describes responsibilities (e.g., rulebook priority structure 600) can be used to determine whether the agent has violated a higher priority rule.

[0112] Process 800 continues by designating the vehicle at fault for the traffic conflict (805).

[0113] Fig. 9 900 is a flow chart of a process for identifying the initiator of a traffic conflict involving an AV and an intelligent agent. Process 900 may be performed by, for example, Figure 3 The processor 304 is shown performing.

[0114] Process 900 begins by determining that a vehicle has violated a rule that results in a traffic conflict with at least one agent (901). Process 900 continues by determining that a situation associated with the traffic conflict is reasonably foreseeable (902). For example, whether a rule is reasonably foreseeable may be defined by the publicly available IEEE P2846 standard.

[0115] Process 900 continues by determining that the agent is not a VRU (903), determining that the vehicle violated any rule before the agent violated any rule (904), determining that the vehicle violated the highest priority rule before the agent violated the highest priority rule (905), and designating the vehicle as at fault for the traffic conflict (906). For example, as described in reference Figure 6 As described, a rulebook having a priority structure that illustrates responsibility by assigning errors can be used to determine rule violations.

[0116] In the previous description, aspects and embodiments of the present disclosure have been described with reference to many specific details, which may vary depending on the implementation. Therefore, the description and the accompanying drawings should be regarded as illustrative, not restrictive. The only and exclusive indication of the scope of the invention, and the applicant's expectation that the content of the scope of the invention is the literal and equivalent scope of the claims issued from this application in the specific form of the claims, including any subsequent amendments. Any definition of the terms used to be included in such claims that are clearly set forth herein should be based on the meaning of such terms as used in the claims. In addition, when the term "also includes" is used in the previous description or the attached claims, the following of the phrase may be an additional step or entity, or a sub-step / sub-entity of the previously described step or entity.

Claims

1. A method comprising: Determining, using at least one processor, that a vehicle violated at least one rule resulting in a traffic conflict involving at least one agent; Determining, using the at least one processor, that behavior of the at least one agent is reasonably foreseeable; Determining, utilizing the at least one processor, that the at least one agent is not a vulnerable road user; Determining, using the at least one processor, that the at least one agent did not violate a higher priority rule than the at least one rule violated by the vehicle that caused the traffic conflict; as well as The vehicle is designated, utilizing the at least one processor, as being at fault for the traffic conflict.

2. The method according to claim 1, further comprising: incrementing a count of traffic conflicts determined to be the vehicle's fault based on designating the vehicle as at fault for the traffic conflict; as well as The counts are compared to a statistical safety target to assess whether the vehicle maintains a positive risk balance associated with an average human driver.

3. The method according to claim 2, wherein: Assessing whether the vehicle maintains a positive risk balance compared to an average human driver also includes: determining a rate of traffic conflicts caused by the vehicle; and The rate is determined to be lower than an average rate of traffic conflicts associated with human drivers.

4. The method according to claim 3, wherein: The rate of traffic conflicts caused by the vehicle is determined based on at least one of a number of actual traffic conflicts of the vehicle and a number of human takeovers to prevent traffic conflicts per a specified distance driven by the vehicle.

5. The method according to claim 1, wherein: Determining that the agent has not violated a higher priority rule than the at least one rule violated by the vehicle that caused the traffic conflict includes: A rulebook priority structure for accounting for responsibility is evaluated to determine that the agent did not violate a higher priority rule than the at least one rule violated by the vehicle that caused the traffic conflict.

6. A system comprising: determining, with at least one processor, a first trajectory for a vehicle in an environment; determining, with the at least one processor, a second trajectory for the vehicle in the environment; Evaluating, with the at least one processor, the first trajectory and the second trajectory using a set of rules, the evaluating comprising: determining, based on the first trajectory, that the vehicle will violate a first rule in the rule set, and that the violation of the first rule will result in a traffic conflict with at least one agent in the environment, determining that the vehicle will violate the first rule in the rule set based on the second trajectory, and the violation of the first rule will result in a traffic conflict with the at least one agent in the environment, determining, based on the first trajectory, that the at least one agent will violate a second rule in the rule set, and that the violation of the second rule will result in a traffic conflict with the vehicle in the environment, determining, based on the second trajectory, that the at least one agent will violate a third rule in the rule set, and that the violation of the third rule will result in a traffic conflict with the vehicle in the environment, determining that the second rule has a lower priority than the first rule and the third rule has a higher priority than the first rule, and The control circuit of the vehicle is utilized to make the vehicle travel along the first trajectory.

7. A system comprising: at least one processor; a memory storing instructions, wherein when the instructions are executed by the at least one processor, the at least one processor performs operations including: determining that the vehicle violated at least one rule resulting in a traffic conflict involving at least one agent, determining that the behavior of the at least one agent is reasonably foreseeable, determining that the at least one agent is not a vulnerable road user, determining that the at least one agent did not violate a higher priority rule than the at least one rule violated by the vehicle that caused the traffic conflict, and The vehicle is designated as being at fault for the traffic conflict.

8. The system according to claim 7, further comprising: incrementing a count of traffic conflicts determined to be the vehicle's fault based on designating the vehicle as at fault for the traffic conflict; as well as The counts are compared to a statistical safety target to assess whether the vehicle maintains a positive risk balance associated with an average human driver.

9. The system according to claim 8, wherein: Assessing whether the vehicle maintains a positive risk balance compared to an average human driver also includes: determining a rate of traffic conflicts caused by the vehicle; and The rate is determined to be lower than an average rate of traffic conflicts associated with human drivers.

10. The system according to claim 9, wherein: The rate of traffic conflicts caused by the vehicle is determined based on at least one of a number of actual traffic conflicts for the vehicle and a number of human takeovers to prevent traffic conflicts per a specified distance driven by the vehicle.

11. The system according to claim 7, wherein: Determining that the agent has not violated a higher priority rule than the at least one rule violated by the vehicle that caused the traffic conflict includes: A rulebook priority structure for accounting for responsibility is evaluated to determine that the agent did not violate a higher priority rule than the at least one rule violated by the vehicle that caused the traffic conflict.

12. A non-transitory computer-readable storage medium having instructions stored thereon, wherein when the instructions are executed by at least one processor, the at least one processor is caused to perform operations, the operations comprising: determining that the vehicle violated at least one rule resulting in a traffic conflict involving at least one agent; Determining that the behavior of the at least one agent is reasonably foreseeable; determining that the at least one agent is not a vulnerable road user; determining that the at least one agent did not violate a higher priority rule than the at least one rule violated by the vehicle that caused the traffic conflict; as well as The vehicle is designated as being at fault for the traffic conflict.

13. A method comprising: Determining, using at least one processor, a first set of rules violated by the vehicle that resulted in a traffic conflict; determining, with the at least one processor, a second set of rules violated by the agent that resulted in the traffic conflict; Determining, using the at least one processor, which of the vehicle and the agent is at fault regarding the traffic conflict by: determining which of the vehicle and the agent first violates any rule in their respective rule sets that results in the traffic conflict, determining which of the vehicle and the agent first violates a highest priority rule in their respective rule sets that causes the traffic conflict, wherein the highest priority rule is a rule that cannot be violated before violating any other rule in the rule set, and Determine which of the vehicle and the agent is at fault for the traffic conflict based on whether the vehicle or the agent first violated a rule in their respective rule sets and whether the vehicle or the agent first violated their respective highest priority rule in their respective rule sets that caused the traffic conflict.

14. The method according to claim 13, wherein: In the event of a violation of at least one rule in the first set of rules or the second set of rules, the at least one rule is designated as indicating a traffic conflict.

15. The method according to claim 14, wherein: A frequency of violations of the at least one rule is tracked as a performance metric for evaluating performance for the first vehicle or the second vehicle.

16. A system comprising: at least one processor; a memory storing instructions, wherein when the instructions are executed by the at least one processor, the at least one processor performs operations including: determining a first set of rules violated by the vehicle that resulted in a traffic conflict; determining a second set of rules violated by the agent that resulted in the traffic conflict; Determine which of the vehicle and the agent is at fault regarding the traffic conflict by: determining which of the vehicle and the agent first violates any rule in their respective rule sets that results in the traffic conflict, determining which of the vehicle and the agent first violates a highest priority rule in their respective rule sets that causes the traffic conflict, wherein the highest priority rule is a rule that cannot be violated before violating any other rule in the rule set, and Determine which of the vehicle and the agent is at fault for the traffic conflict based on whether the vehicle or the agent first violated a rule in their respective rule sets and whether the vehicle or the agent first violated their respective highest priority rule in their respective rule sets that caused the traffic conflict.

17. The system of claim 16, wherein: In the event of a violation of at least one rule in the first set of rules or the second set of rules, the at least one rule is designated as indicating a traffic conflict.

18. The system of claim 17, wherein: A frequency of violations of the at least one rule is tracked as a performance metric for evaluating performance for the first vehicle or the second vehicle.

19. A non-transitory computer-readable storage medium having instructions stored thereon, wherein when the instructions are executed by at least one processor, the at least one processor is caused to perform operations, the operations comprising: determining a first set of rules violated by the vehicle that resulted in a traffic conflict; determining a second set of rules violated by the agent that resulted in the traffic conflict; Determine which of the vehicle and the agent is at fault regarding the traffic conflict by: determining which of the vehicle and the agent first violates any rule in their respective rule sets that results in the traffic conflict, determining which of the vehicle and the agent first violates a highest priority rule in their respective rule sets that causes the traffic conflict, wherein the highest priority rule is a rule that cannot be violated before violating any other rule in the rule set, and Determine which of the vehicle and the agent is at fault for the traffic conflict based on whether the vehicle or the agent first violated a rule in their respective rule sets and whether the vehicle or the agent first violated their respective highest priority rule in their respective rule sets that caused the traffic conflict.

20. The non-transitory computer readable storage medium of claim 19, wherein: In the event of a violation of at least one rule in the first set of rules or the second set of rules, the at least one rule is designated as indicating a traffic conflict.

21. The non-transitory computer readable storage medium of claim 20, wherein: A frequency of violations of the at least one rule is tracked as a performance metric for evaluating performance for the first vehicle or the second vehicle.

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