Discriminator network for detecting out-of-run design domain scenarios

Abstract

A method for detecting when a vehicle encounters an out-of-operation design domain (ODD) scene is provided. The method may include training a generative adversarial network (GAN) including a generator network and a discriminator network. A generator network may be trained to generate a composite scene. The discriminator network may be trained to discriminate between the real scene and the synthetic scene generated by the generator network. A trained discriminator network may be applied to detect when a vehicle encounters an out-of-operation design domain (ODD) scene. Some described methods also include controlling motion of the vehicle in response to an output of the trained discriminator network indicating that the vehicle is encountering an out-of-operation design domain (ODD) scene. A system and a computer program product are also provided.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. patent application No. 17 / 884,384, filed on August 9, 2022, entitled “DISCRIMINATOR NETWORK FOR DETECTING OUT OF OPERATIONAL DESIGN DOMAIN SCENARIOS,” the contents of which are hereby incorporated by reference in their entirety. Background Art

[0003] An autonomous vehicle is capable of sensing and navigating through its surroundings with little or no human input. In order to safely navigate the vehicle along a selected path, the vehicle can rely on motion planning processing to generate, update, and execute one or more trajectories through its current surroundings. The trajectory of the vehicle can be generated based on the current conditions of the vehicle itself and the conditions existing in the vehicle's surroundings (which conditions can include moving objects such as other vehicles and pedestrians, and fixed objects such as buildings and street poles). For example, a trajectory can be generated to avoid collisions between the vehicle and objects present in its surroundings. In addition, a trajectory can be generated so that the vehicle operates according to other desired characteristics such as path length, ride quality or comfort, required travel time, compliance with traffic regulations, and / or following driving practices. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Figure 1 is an example environment in which a vehicle including one or more components of an autonomous system may be implemented;

[0005] Figure 2 is a diagram of one or more systems of a vehicle including an autonomous system;

[0006] Figure 3 yes Figure 1 and Figure 2 a diagram of one or more devices and / or components of one or more systems;

[0007] Figure 4A is a diagram of some components of an autonomous system;

[0008] Figure 4B is a graph of the implementation of a neural network;

[0009] Figure 4C and Figure 4D is a diagram illustrating an example operation of a CNN;

[0010] Figure 5Ais a schematic diagram of an example of a discriminator network trained to detect out of operational design domain (ODD) scenarios;

[0011] Figure 5B is a schematic diagram of an example of a discriminator network deployed to detect scenarios outside the operational design domain; and

[0012] Figure 6 is a flow chart of a process for detecting out of operational design domain scenarios. DETAILED DESCRIPTION

[0013] 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.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] 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.

[0021] General Overview

[0022] In some aspects and / or embodiments, the systems, methods, and computer program products described herein include and / or implement machine learning-enabled techniques for detecting when a vehicle (e.g., an autonomous vehicle) encounters an out-of-operational design domain (ODD) scenario that includes one or more conditions outside the vehicle design (e.g., location, speed, weather, road type, and / or obstacle type, etc.). For example, a generative adversarial network (GAN) including a generator network and a discriminator network can be trained in the following manner, that is, the generator network is trained to generate one or more synthetic scenarios, and the discriminator network is trained to distinguish between at least one real scenario originating from the perception system of the autonomous vehicle and one or more synthetic scenarios generated by the generator network. The trained discriminator network can be applied to detect when the vehicle encounters an out-of-operational design domain (ODD) scenario so that the movement of the vehicle can be controlled in response to the vehicle encountering an out-of-operational design domain (ODD) scenario.

[0023] With the implementation of the systems, methods, and computer program products described herein, techniques for detecting when a vehicle (e.g., an autonomous vehicle) encounters an out-of-operational design domain (ODD) scenario enable deployment of appropriate countermeasures, which may be necessary to safely navigate the vehicle's surroundings while maintaining desired operating characteristics such as path length, ride quality or comfort, required travel time, compliance with traffic regulations, and / or following driving practices. Existing solutions rely on statistical analysis of sensor models or neural networks for processing sensor data from the vehicle's perception system, which are cumbersome and difficult to scale when the range of the out-of-operational design domain (ODD) is extended. In contrast, various implementations of the out-of-operational design domain (ODD) scenario detection techniques described herein employ a discriminator network of a trained generative adversarial network to directly process sensor data from the vehicle's perception system. The discriminator network, which operates at an abstract level, is flexible and easy to retrain. For example, the range beyond the operational design domain can be extended by simply retraining the generative adversarial network (GAN) for new driving logs.

[0024] 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 autonomous vehicle (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 autonomous vehicle (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, objects 104a-104n are interconnected with at least one of vehicles 102a-102n, vehicle-to-infrastructure (V2I) devices 110, networks 112, autonomous vehicle (AV) systems 114, fleet management systems 116, and V2I systems 118 via wired connections, wireless connections, or a combination of wired or wireless connections.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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 .

[0030] 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.

[0031] 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.

[0032] 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)).

[0033] 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.).

[0034] 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.

[0035] Reference now Figure 2 , vehicle 200 (can be used with Figure 1 102) includes, or is associated with, autonomous system 202, powertrain control system 204, steering control system 206, and braking system 208. In some embodiments, vehicle 200 is similar to vehicle 102 (see Figure 1). In some embodiments, the autonomous system 202 is configured to give the vehicle 200 autonomous driving capabilities (e.g., implementing at least one of the following driving automatic or maneuvering-based functions, features, and / or devices, etc., which enables the vehicle 200 to partially or completely operate without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon reliance on human intervention, such as Level 5 ADS-operated vehicles, etc.), highly autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in certain situations, such as Level 4 ADS-operated vehicles, etc.), and / or conditionally autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in limited situations). In one embodiment, the autonomous system 202 includes the operational or tactical functions required to enable the vehicle 200 to operate in traffic on the road and continuously perform part or all of the dynamic driving tasks (DDT). In another embodiment, the autonomous system 202 includes an advanced driver assistance system (ADAS) including driver support features. The autonomous system 202 supports various levels of driving automation ranging from no driving automation (e.g., level 0) to full driving automation (e.g., level 5). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference may 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 ride-sharing company.

[0036] 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, drive-by-wire (DBW) system 202h, and safety controller 202g.

[0037] 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 1In 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.

[0038] 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 (TLD) 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.

[0039] The light 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 3The 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.

[0040] 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 3 At 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.

[0041] 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.

[0042] 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).

[0043] 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 1The V2I system 118 may communicate with the same or similar V2I system.

[0044] 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.

[0045] 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 signal lights, headlights, door locks, and / or windshield wipers, etc.).

[0046] 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 control signals from the DBW system 202h, and the powertrain control system 204 causes the vehicle 200 to make longitudinal vehicle movements such as starting to move forward, stopping to move forward, starting to move backward, stopping to move backward, accelerating in a certain direction, decelerating in a certain direction, etc., or to make lateral vehicle movements such as turning left and / or turning right. 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.

[0047] 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. In other words, the steering control system 206 causes the activities necessary for the adjustment of the y-axis component of the vehicle motion.

[0048] 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.

[0049] 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. Although braking system 208 is Figure 2 Although illustrated as being located on the proximal side of the vehicle 200 , the braking system 208 may be located anywhere in the vehicle 200 .

[0050] Reference now Figure 3, a schematic diagram illustrating device 300. As illustrated, device 300 includes processor 304, memory 306, storage component 308, input interface 310, output interface 312, communication interface 314, and 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 vehicle 200 (e.g., at least one device of autonomous system 202, powertrain control system 204, steering control system 206, and / or braking system 208 of vehicle 200); and / or one or more devices of network 112 (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), vehicle 200, 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. Figure 3 As shown, apparatus 300 includes a bus 302 , a processor 304 , a memory 306 , a storage component 308 , an input interface 310 , an output interface 312 , and a communication interface 314 .

[0051] The bus 302 includes components that permit communication between components of the device 300. In some cases, the processor 304 includes a 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 processor 304.

[0052] 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.

[0053] 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.).

[0054] 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.

[0055] In some embodiments, the device 300 performs one or more processes described herein. The device 300 performs these processes based on the 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.

[0056] 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 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.

[0057] 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.

[0058] 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 as 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 processor 304 and / or the processor of another device (e.g., another device that is the same as 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.

[0059] 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.

[0060] Reference now Figure 4A, illustrates an example block diagram of an autonomous vehicle computing 400 (sometimes referred to as an "AV stack"). As illustrated, the autonomous vehicle 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.).

[0061] 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.

[0062] In some embodiments, the 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, the planning system 404 periodically or continuously receives data (e.g., data associated with the classification of physical objects described above) from the perception system 402, and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the perception system 402. In other words, the planning system 404 can perform tasks related to the tactical functions required to operate the vehicle 101 in traffic on the road. Tactical efforts involve maneuvering the vehicle in traffic during the journey, which includes but is not limited to deciding whether and when to overtake another vehicle, change lanes, or select an appropriate rate, acceleration, deceleration, etc. In some embodiments, the planning system 404 receives data associated with an updated position of the vehicle (e.g., vehicle 102) from the positioning system 406, and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the positioning system 406.

[0063] 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.

[0064] 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.

[0065] 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 the powertrain control system (e.g., DBW system 202h and / or powertrain control system 204, etc.), the steering control system (e.g., steering control system 206) and / or the braking system (e.g., braking system 208). For example, the control system 408 is configured to perform operational functions such as lateral vehicle motion control or longitudinal vehicle motion control. Lateral vehicle motion control causes the necessary activities for the adjustment of the y-axis component of the vehicle motion. Longitudinal vehicle motion control causes the necessary activities for the adjustment of the x-axis component of the vehicle motion. 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 turning the vehicle 200 left. Additionally or alternatively, the control system 408 generates and transmits control signals to cause other devices of the vehicle 200 (eg, headlights, turn signal lights, door locks, and / or windshield wipers, etc.) to change states.

[0066] 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.). The following is about FIG. 4B to FIG. 4D Includes examples of implementations of machine learning models.

[0067] 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) for storing data and / or software related to operations and using at least one system of autonomous vehicle computing 400. Figure 3In 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.

[0068] 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).

[0069] Reference now Figure 4B , a diagram illustrating an implementation of a machine learning model. More specifically, a diagram illustrating an implementation of a convolutional neural network (CNN) 420. For purposes of illustration, the following description of CNN 420 will be about implementing CNN 420 by perception system 402. For example, CNN 420 may implement a generator network and / or a discriminator network of a generative adversarial network (GAN) trained to detect scenarios beyond the operational design domain (ODD). However, it will be understood that in some examples, CNN 420 (e.g., one or more components of CNN 420) is implemented by other systems (such as planning system 404, positioning system 406, and / or control system 408, etc.) other than perception system 402 or in addition to perception system 402. Although CNN 420 includes certain features as described herein, these features are provided for purposes of illustration and are not intended to limit the present disclosure.

[0070] CNN 420 includes a plurality of convolutional layers including a first convolutional layer 422, a second convolutional layer 424, and a convolutional layer 426. In some embodiments, CNN 420 includes a subsampling layer 428 (sometimes referred to as a pooling layer). In some embodiments, subsampling layer 428 and / or other subsampling layers have a dimension that is smaller than the dimension of the upstream system (i.e., the number of nodes). With the subsampling layer 428 having a dimension that is smaller than the dimension of the upstream layer, CNN 420 merges the amount of data associated with the initial input and / or output of the upstream layer, thereby reducing the amount of computation required for CNN 420 to perform downstream convolution operations. Additionally or alternatively, with the subsampling layer 428 being associated with (e.g., configured to perform) at least one subsampling function (as described below with respect to Figure 4C and Figure 4D As described above, CNN 420 incorporates the amount of data associated with the initial input.

[0071] The perception system 402 performs the convolution operation based on the perception system 402 providing respective inputs and / or outputs associated with each of the first convolution layer 422, the second convolution layer 424, and the convolution layer 426 to generate respective outputs. In some examples, the perception system 402 implements the CNN 420 based on the perception system 402 providing data as input to the first convolution layer 422, the second convolution layer 424, and the convolution layer 426. In such examples, the perception system 402 provides data as input to the first convolution layer 422, the second convolution layer 424, and the convolution layer 426 based on the perception system 402 receiving data from one or more different systems (e.g., one or more systems of a vehicle that is the same or similar to the vehicle 102, a remote AV system that is the same or similar to the remote AV system 114, a queue management system that is the same or similar to the queue management system 116, and / or a V2I system that is the same or similar to the V2I system 118, etc.). The following is about Figure 4C Includes a detailed description of the convolution operation.

[0072] In some embodiments, the perception system 402 provides data associated with the input (referred to as the initial input) to the first convolutional layer 422, and the perception system 402 generates data associated with the output using the first convolutional layer 422. In some embodiments, the perception system 402 provides the output generated by the convolutional layer as input to a different convolutional layer. For example, the perception system 402 provides the output of the first convolutional layer 422 as input to the subsampling layer 428, the second convolutional layer 424, and / or the convolutional layer 426. In such an example, the first convolutional layer 422 is referred to as an upstream layer, and the subsampling layer 428, the second convolutional layer 424, and / or the convolutional layer 426 are referred to as downstream layers. Similarly, in some embodiments, the perception system 402 provides the output of the subsampling layer 428 to the second convolutional layer 424 and / or the convolutional layer 426, and in this example, the subsampling layer 428 will be referred to as the upstream layer, and the second convolutional layer 424 and / or the convolutional layer 426 will be referred to as the downstream layer.

[0073] In some embodiments, before the perception system 402 provides the input to the CNN 420, the perception system 402 processes the data associated with the input provided to the CNN 420. For example, the perception system 402 processes the data associated with the input provided to the CNN 420 based on the perception system 402 normalizing the sensor data (e.g., image data, LiDAR data, and / or Radar data, etc.).

[0074] In some embodiments, CNN 420 generates an output based on perception system 402 performing convolution operations associated with each convolution layer. In some examples, CNN 420 generates an output based on perception system 402 performing convolution operations associated with each convolution layer and the initial input. In some embodiments, perception system 402 generates an output and provides the output to fully connected layer 430. In some examples, perception system 402 provides the output of convolution layer 426 to fully connected layer 430, wherein fully connected layer 430 includes data associated with multiple feature values ​​referred to as F1, F2, ..., FN. In this example, the output of convolution layer 426 includes data associated with multiple output feature values ​​representing predictions.

[0075] In some embodiments, perception system 402 identifies a prediction from the plurality of predictions based on perception system 402 identifying a feature value associated with a highest likelihood of being a correct prediction from the plurality of predictions. For example, where fully connected layer 430 includes feature values ​​F1, F2, ..., FN and F1 is the largest feature value, perception system 402 identifies the prediction associated with F1 as the correct prediction from the plurality of predictions. In some embodiments, perception system 402 trains CNN 420 to generate the predictions. In some examples, perception system 402 trains CNN 420 to generate the predictions based on perception system 402 providing training data associated with the predictions to CNN 420.

[0076] Reference now Figure 4C and Figure 4D , a diagram illustrating an example operation of CNN 440 utilizing perception system 402. In some embodiments, CNN 440 (e.g., one or more components of CNN 440) is coupled to CNN 420 (e.g., one or more components of CNN 420) (see Figure 4B ) are the same or similar.

[0077] At step 450, the perception system 402 provides data associated with the image as input to the CNN 440 (step 450). For example, as illustrated, the perception system 402 provides data associated with the image to the CNN 440, where the image is a grayscale image represented as values ​​stored in a two-dimensional (2D) array. In some embodiments, the data associated with the image may include data associated with a color image represented as values ​​stored in a three-dimensional (3D) array. Additionally or alternatively, the data associated with the image may include data associated with an infrared image and / or a Radar image, etc.

[0078] At step 455, CNN 440 performs a first convolution function. For example, CNN 440 performs a first convolution function based on CNN 440 providing a value representing an image as an input to one or more neurons (not explicitly illustrated) included in first convolution layer 442. In this example, the value representing the image may correspond to a value of a region (sometimes referred to as a receptive field) representing the image. In some embodiments, each neuron is associated with a filter (not explicitly illustrated). The filter (sometimes referred to as a kernel) may be represented as an array of values ​​corresponding in size to the value provided as input to the neuron. In one example, the filter may be configured to identify edges (e.g., horizontal lines, vertical lines, and / or straight lines, etc.). In successive convolution layers, the filters associated with the neurons may be configured to continuously identify more complex patterns (e.g., arcs and / or objects, etc.).

[0079] In some embodiments, CNN 440 performs a first convolution function based on CNN 440 multiplying the values ​​of each neuron provided as input to one or more neurons included in the first convolution layer 442 by the values ​​of the filters corresponding to each neuron in the same or more neurons. For example, CNN 440 may multiply the values ​​of each neuron provided as input to one or more neurons included in the first convolution layer 442 by the values ​​of the filters corresponding to each neuron in the one or more neurons to generate a single value or an array of values ​​as output. In some embodiments, the collective output of the neurons of the first convolution layer 442 is referred to as a convolution output. In some embodiments, when each neuron has the same filter, the convolution output is referred to as a feature map.

[0080] In some embodiments, CNN 440 provides the output of each neuron of the first convolutional layer 442 to the neurons of the downstream layer. For clarity, the upstream layer may be a layer that transmits data to a different layer (referred to as the downstream layer). For example, CNN 440 may provide the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the subsampling layer. In the example, CNN 440 provides the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the first subsampling layer 444. In some embodiments, CNN 440 adds a bias value to the aggregate set of all values ​​provided to each neuron of the downstream layer. For example, CNN 440 adds a bias value to the aggregate set of all values ​​provided to each neuron of the first subsampling layer 444. In such an example, CNN 440 determines the final value to be provided to each neuron of the first subsampling layer 444 based on the aggregate set of all values ​​provided to each neuron and the activation function associated with each neuron of the first subsampling layer 444.

[0081] At step 460, CNN 440 performs a first subsampling function. For example, based on CNN 440 providing the values ​​output by first convolutional layer 442 to the corresponding neurons of first subsampling layer 444, CNN 440 may perform the first subsampling function. In some embodiments, CNN 440 performs the first subsampling function based on an aggregation function. In an example, CNN 440 performs the first subsampling function based on CNN 440 determining the maximum input (referred to as a maximum pooling function) among the values ​​provided to a given neuron. In another example, CNN 440 performs the first subsampling function based on CNN 440 determining the average input (referred to as an average pooling function) among the values ​​provided to a given neuron. In some embodiments, based on CNN 440 providing values ​​to the respective neurons of first subsampling layer 444, CNN 440 generates an output, which is sometimes referred to as a subsampled convolution output.

[0082] At step 465, CNN 440 performs a second convolution function. In some embodiments, CNN 440 performs the second convolution function in a manner similar to how CNN 440 performs the first convolution function described above. In some embodiments, CNN 440 performs the second convolution function based on CNN 440 providing the value output by first subsampling layer 444 as input to one or more neurons (not explicitly illustrated) included in second convolution layer 446. In some embodiments, as described above, each neuron of second convolution layer 446 is associated with a filter. As described above, the filter (one or more) associated with second convolution layer 446 can be configured to recognize more complex patterns than the filter associated with first convolution layer 442.

[0083] In some embodiments, the CNN 440 performs a second convolution function based on the CNN 440 multiplying the value of each neuron provided as input to the one or more neurons included in the second convolution layer 446 by the value of the filter corresponding to each neuron of the one or more neurons. For example, the CNN 440 may multiply the value of each neuron provided as input to the one or more neurons included in the second convolution layer 446 by the value of the filter corresponding to each neuron of the one or more neurons to generate a single value or a value array as an output.

[0084] In some embodiments, the CNN 440 provides the output of each neuron of the second convolutional layer 446 to the neurons of the downstream layer. For example, the CNN 440 may provide the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the subsampling layer. In an example, the CNN 440 provides the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the second subsampling layer 448. In some embodiments, the CNN 440 adds a bias value to the aggregate set of all values ​​provided to each neuron of the downstream layer. For example, the CNN 440 adds a bias value to the aggregate set of all values ​​provided to each neuron of the second subsampling layer 448. In such an example, the CNN 440 determines the final value provided to each neuron of the second subsampling layer 448 based on the aggregate set of all values ​​provided to each neuron and the activation function associated with each neuron of the second subsampling layer 448.

[0085] At step 470, CNN 440 performs a second subsampling function. For example, based on CNN 440 providing the values ​​output by second convolutional layer 446 to corresponding neurons of second subsampling layer 448, CNN 440 may perform a second subsampling function. In some embodiments, based on CNN 440 using an aggregation function, CNN 440 performs a second subsampling function. In an example, as described above, based on CNN 440 determining the maximum input or average input among the values ​​provided to a given neuron, CNN 440 performs a first subsampling function. In some embodiments, based on CNN 440 providing values ​​to respective neurons of second subsampling layer 448, CNN 440 generates an output.

[0086] At step 475, CNN 440 provides the output of each neuron of the second subsampling layer 448 to the fully connected layer 449. For example, CNN 440 provides the output of each neuron of the second subsampling layer 448 to the fully connected layer 449 so that the fully connected layer 449 generates an output. In some embodiments, the fully connected layer 449 is configured to generate an output associated with a prediction (sometimes referred to as a classification). The prediction may include an indication that the objects included in the image provided as input to CNN 440 include objects and / or sets of objects, etc. In some embodiments, the perception system 402 performs one or more operations and / or provides data associated with the prediction to the various systems described herein.

[0087] Reference now FIG. 5A to FIG. 5B , a schematic diagram illustrating an example of a discriminator network 500 of a generative adversarial network that is trained and deployed to detect a vehicle 550 (e.g., such as Figure 1 The vehicle 102 shown in FIG. Figure 2 In some example embodiments, the discriminator network 500 may be trained offline before being deployed to detect when the vehicle 550 encounters an out of operational design domain (ODD) scenario. In some example embodiments, once deployed to the vehicle 550, the discriminator network 500 may operate on the perception output from the perception system 510, which may be the same or similar to the perception system 402 described above.

[0088] Figure 5A A schematic diagram of a discriminator network 500 trained to detect out of operational design domain (ODD) scenarios is depicted. Figure 5AAs shown in , the discriminator network 500 may form part of a generative adversarial network (GAN) that also includes a generator network 505. In some example embodiments, the generative adversarial network (GAN) may be trained in such a way that at least the generator network 505 is trained to generate one or more synthetic scenes 535 that simulate a real scene as closely as possible (such as a real scene 525 originating from a perception system 510, etc.), for example, based on an input 503. Examples of input 503 include one or more of random noise, a map, and / or raw sensor data, etc.

[0089] When the generator network 505 is trained to generate one or more synthetic scenes 535, the discriminator network 500 can be simultaneously trained to distinguish between the real scene 525 from the perception system 510 and the synthetic scene 535 generated by the generator network 505. In some cases, the real scene 525 can be reproduced by the perception system 510 based on one or more driving logs 515. Figure 5A The method shown in FIG. 5 uses new and / or additional driving logs to retrain at least the generative adversarial network (GAN) to update the discriminator network 500, for example, to update the scope of the scenario beyond the operational design domain (ODD). Figure 5A As shown in , the discriminator network 500 can generate an output 507 indicating the probability that the scene is a real scene (or a synthetic scene) for each scene input into the discriminator network 500.

[0090] In Figure 5A When trained in the manner shown in FIG. 5 , the discriminator network 500 can be deployed to detect when, for example, a vehicle 550 encounters an out of operational design domain (ODD) scenario. Figure 5B In the example deployment shown in FIG. 5 , the discriminator network 500 may receive a real-life scenario 560 from a perception system 510 of a vehicle 550. The discriminator network 500 may generate an output 565 indicating a probability that the real-life scenario 560 is an out of operational design domain (ODD) scenario based at least on the real-life scenario 560. In some example embodiments, one or more countermeasures may be triggered in response to the output 565 of the discriminator network 500 indicating that the vehicle 550 is encountering an out of operational design domain (ODD) scenario. An example countermeasure includes controlling the motion of the vehicle, such as by control signals transmitted by the control system 408 to the drive-by-wire (DBW) system 202h, the powertrain control system 204, the steering control system 206, and / or the braking system 208, to decelerate and / or brake when the vehicle 550 is encountering an out of operational design domain (ODD) scenario. Alternatively and / or additionally, a remote vehicle assistance (RVA) request may be sent in response to vehicle 550 encountering an out of operational design domain (ODD) scenario.

[0091] In some example embodiments, the output 565 of the discriminator network 500 can be determined based on multiple probabilities that the vehicle 550 is in an out of operation design domain (ODD) scenario at different locations and / or different time periods. For example, the discriminator network 500 can determine a first probability that the vehicle is in an out of operation design domain (ODD) scenario for a first time period and / or a first location. In addition, the discriminator network 500 can determine a second probability that the vehicle is in an out of operation design domain scenario for a second time period and / or a second location. Therefore, the output 565 of the discriminator network 500 can be generated by fusing the first probability and the second probability together, for example, by applying Bayesian fusion, Dempster-Shafer fusion, Yager combination rule, Dubois-Prade combination rule, and / or Denoeux cautious rule. The application of Bayesian fusion can include applying a Bayesian estimation algorithm (e.g., if the observations are independent) or a specific custom (ad hoc) operator. For example, a pessimistic approach may include taking a maximum probability of exceeding the operational design domain (ODD) over time, an optimistic approach may include taking a minimum probability of exceeding the operational design domain (ODD) over time, and a specific custom approach may include taking an average probability of exceeding the operational design domain (ODD) over time. When a current observation is fused with one or more previous observations, the one or more previous observations may be associated with a lesser (or discounted) quality of evidence value. The fusion of multiple observations may at least increase the robustness of the discriminator network 500, meaning that the output 565 of the discriminator network 500 exhibits low variance when applied to new input scenarios.

[0092] In some example embodiments, instead of or in addition to the real life scenario 560 being a single probability of being an out of operation design domain (ODD) scenario, the output 565 of the discriminator network 500 may be a probability map in which each of the plurality of regions is associated with a probability that the region is out of the operation design domain (ODD). For example, the output 565 of the discriminator network 500 may be a probability map in which a first region is associated with a first probability that the first region is out of the operation design domain (ODD), and a second region is associated with a second probability that the second region is out of the operation design domain (ODD). In the case where the output 565 of the discriminator network 500 is a probability map, the countermeasure triggered by the output 565 of the discriminator network 500 may include controlling the movement of the vehicle 550 to avoid one or more regions having a probability of being out of the operation design domain (ODD) greater than a threshold value based at least on the probability map included in the output 565 of the discriminator network 500.

[0093] In some example embodiments, the output 565 of the discriminator network 500 may also trigger an update to the perception system 510 of the vehicle 550. For example, for at least some of the scenarios that the output 565 of the discriminator network 500 indicates as having a probability greater than a threshold value of being synthetic scenarios, the perception system 510 of the vehicle 550 may be updated to recognize these scenarios so that these scenarios are no longer outside the operational design domain (ODD). In some cases, the threshold associated with the vehicle 550 encountering a synthetic scenario may be lower than the threshold associated with the vehicle 550 being in a scenario outside the operational design domain (ODD), so that the perception system 510 is fine-tuned to better respond to uncertain situations where the vehicle 550 is in a scenario that is unfamiliar but not outside the existing operational design domain (ODD) of the perception system 510.

[0094] In some example embodiments, the output 565 of the discriminator network 500 may be used to measure the difficulty of deploying a fleet including, for example, vehicles 550 in a new location. For example, the output 565 of the discriminator network 500 may be used to determine the number of out of operational design domain (ODD) scenarios encountered by the vehicles 550 as they are navigating in the new location. The number of out of operational design domain (ODD) scenarios encountered by the vehicles 550 in the new location may indicate the difficulty associated with deploying the vehicles 550 in the new location. Thus, the greater the number of out of operational design domain (ODD) scenarios encountered by the vehicles 550 in the new location, the more difficult the deployment may be because the perception system 510 of the vehicles 550 may need to be updated more significantly in order for the vehicles 550 to successfully navigate the new location.

[0095] In some example embodiments, the output 565 of the discriminator network 500 may undergo refinement to increase its accuracy and robustness. For example, in some cases, the output 565 of the discriminator network 500 may be adjusted based on the uncertainty associated with the output 565 of the discriminator network 500. The uncertainty associated with the output 565 of the discriminator network 500 may correspond to the uncertainty associated with the first probability that the vehicle 550 is in an out of operational design domain (ODD) scenario and / or the second probability that the vehicle 550 is not in an out of operational design domain (ODD) scenario. In addition, the uncertainty associated with the output 565 of the discriminator network 500 may be determined in various ways, including, for example, probabilistic calibration by applying evidence classification and / or temperature scaling, etc.

[0096] Reference now Figure 6, illustrates a flow chart of a process 600 for detecting an out of operational design domain (ODD) scenario. In some embodiments, one or more steps described for process 600 are performed by autonomous system 202 of vehicle 200 (e.g., completely and / or partially, etc.). Additionally or alternatively, in some embodiments, one or more steps described for process 600 are performed by another device or device group such as device 300 (e.g., completely and / or partially, etc.) separate from autonomous system 202 of vehicle 200 or including autonomous system 202 of vehicle 200.

[0097] Continue to refer Figure 6 , a generative adversarial network (GAN) including a generator network and a discriminator network can be trained (block 602). Figure 5A As shown in , a discriminator network 500 of a generative adversarial network (GAN) can be trained together with a generator network 505 of a generative adversarial network (GAN). The generator network 505 can be trained to generate one or more synthetic scenes 535 that simulate a real scene (such as a real scene 525 originating from a perception system 510) as closely as possible based on an input 503 (including one or more of random noise, a map, and / or raw sensor data, etc.). On the other hand, the discriminator network 500 can be trained to distinguish between a real scene 525 from a perception system 510 and a synthetic scene 535 generated by the generator network 505.

[0098] Continue to refer Figure 6 , the trained discriminator network may be applied to detect when a vehicle is encountering an out of operational design domain (ODD) scenario (block 604). For example, the trained discriminator network 500 may be deployed to detect when the vehicle 550 is encountering an out of operational design domain scenario. Figure 5BIn the example deployment shown in , the discriminator network 500 can generate an output 565 indicating the probability that the real-life scenario 560 is an out-of-operation design domain (ODD) scenario based at least on the real-life scenario 560 (e.g., received from the perception system 510 of the vehicle 550). In some cases, the output 565 of the discriminator network 500 can be determined based on multiple probabilities that the vehicle 550 is in an out-of-operation design domain (ODD) scenario at different locations and / or different time periods. Alternatively and / or additionally, the output 565 of the discriminator network 500 can be a probability map in which each of the multiple regions is associated with the probability that the region is out of the operational design domain (ODD). The output 565 of the discriminator network 500 can also undergo certain refinements to increase its accuracy and robustness. For example, in some cases, the output 565 of the discriminator network 500 can be adjusted based on the uncertainty associated with the output 565 of the discriminator network 500, which can be determined by applying evidence classification and / or temperature scaling, etc. for probability calibration.

[0099] Some example techniques for determining uncertainty in the output 565 of the discriminator network 500 include calculating the entropy of the probability that the vehicle 550 is in an out-of-operation design domain (ODD) scenario. This may include applying Monte-Carlo discarding to change the probability of out-of-operation design domain (ODD) determined by the discriminator network 500. The output 565 of the discriminator network 500 may therefore correspond to an average of different probabilities, which may be associated with an overall uncertainty score corresponding to the standard deviation between the different probabilities determined by the discriminator network 500. Applying evidence classification and temperature scaling to the output 565 of the discriminator network 500 may reduce extreme variations in the output 565. For example, the output 565 of the discriminator network 500 may include high contrast probabilities close to 0 or 1. Temperature scaling makes the probabilities included in the output 565 of the discriminator network 500 more detailed and evenly distributed throughout the [0, 1] range. On the other hand, using evidence classification, the output 565 of the discriminator network 500 can include three separate probabilities (e.g., evidence in the operational design domain (ODD), evidence outside the operational design domain (ODD), and the overall uncertainty about the previous assignment) instead of a single probability.

[0100] Continue to refer Figure 6, the movement of the vehicle can be controlled in response to the output of the trained discriminator network indicating that the vehicle is encountering an out of operation design domain (ODD) scenario (block 606). In some example embodiments, when the output 565 of the discriminator network 500 indicates that the vehicle 550 is encountering an out of operation design domain (ODD) scenario, one or more countermeasures can be triggered. An example countermeasure includes controlling the movement of the vehicle 550 to decelerate and / or brake when the vehicle 550 is encountering an out of operation design domain (ODD) scenario. Alternatively and / or additionally, a remote vehicle assistance (RVA) request can be sent in response to the vehicle 550 encountering an out of operation design domain (ODD) scenario. In some cases, one or more countermeasures may include avoiding one or more areas that are identified as having a probability greater than a threshold value out of the operation design domain (ODD) based on the output 565 of the discriminator network 500. In addition, in some cases, one or more countermeasures may include updating the perception system 510 of the vehicle 550. For example, the perception system 510 of the vehicle 550 may be fine-tuned to recognize scenes that are unfamiliar (e.g., having a probability greater than a threshold of being synthetic scenes according to the output 565 of the discriminator network 500) but not necessarily outside the existing operational design domain (ODD) of the perception system 510.

[0101] According to some non-limiting embodiments or examples, a system is provided, comprising: at least one data processor and at least one memory storing instructions. The instructions are executed by the at least one data processor so that the at least one data processor at least: uses the at least one data processor to apply a discriminator network of a generative adversarial network (GAN) to detect when a vehicle encounters an out-of-operation design domain (ODD) scenario, the generative adversarial network (GAN) comprising a generator network and the discriminator network, the generator network is trained to generate one or more synthetic scenarios, and the discriminator network is trained to distinguish between at least one real scenario and the one or more synthetic scenarios generated by the generator network; and controls the movement of the vehicle in response to the output of the trained discriminator network indicating that the vehicle is encountering an out-of-operation design domain (ODD) scenario.

[0102] According to some non-limiting embodiments or examples, a non-transitory computer-readable medium is provided, comprising one or more instructions, which, when executed by at least one processor, causes the at least one processor to: use at least one data processor to apply a discriminator network of a generative adversarial network (GAN) to detect when a vehicle encounters an out of operational design domain (ODD) scenario, wherein the generative adversarial network (GAN) includes a generator network and the discriminator network, wherein the generator network is trained to generate one or more synthetic scenarios, and the discriminator network is trained to distinguish between at least one real scenario and the one or more synthetic scenarios generated by the generator network; and control the movement of the vehicle in response to an output of the trained discriminator network indicating that the vehicle is encountering an out of operational design domain (ODD) scenario.

[0103] According to some non-limiting embodiments or examples, a method is provided, comprising: applying, using at least one data processor, a discriminator network of a generative adversarial network (GAN) to detect when a vehicle encounters an out of operational design domain (ODD) scenario, wherein the generative adversarial network (GAN) includes a generator network and the discriminator network, wherein the generator network is trained to generate one or more synthetic scenarios, and the discriminator network is trained to distinguish between at least one real scenario and the one or more synthetic scenarios generated by the generator network; and controlling the movement of the vehicle using the at least one data processor in response to an output of the trained discriminator network indicating that the vehicle is encountering an out of operational design domain (ODD) scenario.

[0104] Further non-limiting aspects or embodiments are set forth in the following numbered clauses:

[0105] Item 1: A method comprising: using at least one data processor to apply a discriminator network of a generative adversarial network (GAN) to detect when a vehicle encounters an out of operational design domain scenario, i.e., an out of ODD scenario, wherein the generative adversarial network (GAN) includes a generator network and the discriminator network, wherein the generator network is trained to generate one or more synthetic scenarios, and the discriminator network is trained to distinguish between at least one real scenario and the one or more synthetic scenarios generated by the generator network; and using the at least one data processor to control the movement of the vehicle in response to an output of the trained discriminator network indicating that the vehicle is encountering an out of operational design domain scenario, i.e., an out of ODD scenario.

[0106] Clause 2: The method of clause 1, wherein the generative adversarial network (GAN) is trained to generate the one or more synthetic scenes based at least on one or more driving logs representing one or more real scenes.

[0107] Clause 3: The method according to any one of clauses 1 to 2 further includes: using the at least one data processor to send a remote vehicle assistance request (RVA request) in response to the output of the trained discriminator network indicating that the vehicle is encountering an out of operational design domain scenario (out of ODD scenario).

[0108] Clause 4: The method of any one of clauses 1 to 3, wherein controlling the movement of the vehicle in response to the vehicle encountering an out of operational design domain scenario (out of ODD scenario) comprises at least one of deceleration and braking.

[0109] Clause 5: A method according to any one of clauses 1 to 4, wherein the trained discriminator network outputs a probability map in which each region of a plurality of regions is associated with a probability that the region is outside the operational design domain, i.e., outside the ODD.

[0110] Clause 6: A method according to clause 5, wherein controlling the movement of the vehicle includes avoiding one or more areas of the multiple areas having a probability greater than a threshold of exceeding the operational design domain, i.e., exceeding the ODD, based at least on the probability map output by the trained discriminator network.

[0111] Clause 7: A method according to any one of clauses 1 to 6, wherein the generative adversarial network (GAN) is trained based on training data including one or more of random noise, a map and raw sensor data.

[0112] Clause 8: The method according to any one of clauses 1 to 7 further includes: using the at least one data processor and at least based on the output of the trained discriminator network to identify one or more scenes encountered by the vehicle with a probability greater than a threshold being a synthetic scene; and using the at least one data processor and at least based on the one or more scenes to update the perception system of the vehicle.

[0113] Clause 9: The method according to any one of clauses 1 to 8 further includes: using the at least one data processor and based at least on the output of the trained discriminator network, determining the number of out-of-operational design domain scenarios, i.e., out-of-ODD scenarios, encountered by the vehicle in a new location.

[0114] Clause 10: A method according to any one of clauses 1 to 9, wherein the trained discriminator network outputs a first probability that the vehicle is in a scenario beyond the operating design domain at a first time period and / or a first location, wherein the trained discriminator network also outputs a second probability that the vehicle is in a scenario beyond the operating design domain at a second time period and / or a second location, and wherein the output of the trained discriminator network is determined based at least on the first probability and the second probability.

[0115] Clause 11: The method of clause 10, wherein an output of the trained discriminator network is determined by fusing at least the first probability and the second probability.

[0116] Clause 12: The method of clause 11, wherein the first probability and the second probability are fused by applying one or more of Bayesian fusion, Dempster-Shafer fusion, Yager combination rule, Dubois-Prade combination rule, Denoeux cautious rule.

[0117] Clause 13: The method of any one of clauses 1 to 12, further comprising: determining an uncertainty associated with an output of the trained discriminator network; and adjusting the output of the trained discriminator network based at least on the uncertainty.

[0118] Clause 14: A method according to clause 13, wherein the uncertainty associated with the output of the trained discriminator network includes uncertainty associated with a first probability that the vehicle is in an out of operational design domain scenario, i.e., an out of ODD scenario and / or a second probability that the vehicle is not in an out of operational design domain scenario, i.e., an out of ODD scenario.

[0119] Clause 15: A method according to any of clauses 13 to 14, wherein the uncertainty associated with the output of the trained discriminator network is determined by probability calibration applying evidence classification and / or temperature scaling.

[0120] Clause 16: The method according to any one of clauses 1 to 15, further comprising: using at least one data processor to train a generative adversarial network (GAN) comprising a generator network and a discriminator network.

[0121] Clause 17: A system comprising: at least one data processor and at least one memory storing instructions, the instructions, when executed by the at least one data processor, causing operations including any of clauses 1 to 16.

[0122] Clause 18: A non-transitory computer readable medium storing instructions which, when executed by at least one data processor, result in operations including any of clauses 1 to 16.

[0123] Clause 19: A method comprising: using at least one data processor to train a generative adversarial network (GAN) comprising a generator network and a discriminator network, wherein the generator network is trained to generate one or more synthetic scenes, and the discriminator network is trained to distinguish between at least one real scene and the one or more synthetic scenes generated by the generator network; using the at least one data processor to apply the trained discriminator network to detect when a vehicle encounters an out of operational design domain scenario, i.e., an out of ODD scenario; and using the at least one data processor to control the movement of the vehicle in response to an output of the trained discriminator network indicating that the vehicle is encountering an out of operational design domain scenario, i.e., an out of ODD scenario.

[0124] Item 20: A system comprising: at least one data processor and at least one memory storing instructions, wherein the instructions, when executed by the at least one data processor, cause operations including: using the at least one data processor to train a generative adversarial network (GAN) comprising a generator network and a discriminator network, wherein the generator network is trained to generate one or more synthetic scenes, and the discriminator network is trained to distinguish between at least one real scene and the one or more synthetic scenes generated by the generator network; using the at least one data processor to apply the trained discriminator network to detect when a vehicle encounters an out-of-operation design domain scenario, i.e., an out-of-ODD scenario; and using the at least one data processor to control the movement of the vehicle in response to an output of the trained discriminator network indicating that the vehicle is encountering an out-of-operation design domain scenario, i.e., an out-of-ODD scenario.

[0125] Item 21: A non-transitory computer-readable medium storing instructions that, when executed by at least one data processor, cause operations, the operations comprising: using at least one data processor to train a generative adversarial network (GAN) comprising a generator network and a discriminator network, the generator network being trained to generate one or more synthetic scenes, and the discriminator network being trained to distinguish between at least one real scene and the one or more synthetic scenes generated by the generator network; using the at least one data processor to apply the trained discriminator network to detect when a vehicle encounters an out-of-operational design domain scenario, i.e., an out-of-ODD scenario; and using the at least one data processor to control the movement of the vehicle in response to an output of the trained discriminator network indicating that the vehicle is encountering an out-of-operational design domain scenario, i.e., an out-of-ODD scenario.

[0126] 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: Using at least one data processor, applying a discriminator network of a generative adversarial network (GAN) to detect when a vehicle encounters an out of operational design domain (ODD) scenario, the generative adversarial network (GAN) comprising a generator network and the discriminator network, the generator network being trained to generate one or more synthetic scenarios, and the discriminator network being trained to distinguish between at least one real scenario and the one or more synthetic scenarios generated by the generator network; as well as Using the at least one data processor, movement of the vehicle is controlled in response to the output of the trained discriminator network indicating that the vehicle is encountering the out of operational design domain scenario (out of ODD scenario).

2. The method according to claim 1, wherein: The generative adversarial network (GAN) is trained to generate the one or more synthetic scenes based at least on one or more driving logs representing one or more real scenes.

3. The method according to any one of claims 1 to 2, further comprising: Using the at least one data processor, a remote vehicle assistance request (RVA request) is sent in response to the output of the trained discriminator network indicating that the vehicle is encountering the out of operational design domain (ODD) scenario.

4. The method according to any one of claims 1 to 3, wherein: Controlling the movement of the vehicle in response to the vehicle encountering the out of operational design domain scenario (out of ODD scenario) includes at least one of deceleration and braking.

5. The method according to any one of claims 1 to 4, wherein: The trained discriminator network outputs a probability map in which each region of a plurality of regions is associated with a probability that the region is outside the operational design domain, ie, outside the ODD.

6. The method according to claim 5, wherein: Controlling the movement of the vehicle includes avoiding one or more regions of the plurality of regions having a probability greater than a threshold of exceeding an operational design domain, ie, exceeding an ODD, based at least on the probability map output by the trained discriminator network.

7. The method according to any one of claims 1 to 6, wherein: The generative adversarial network (GAN) is trained based on training data including one or more of random noise, maps and raw sensor data.

8. The method according to any one of claims 1 to 7, further comprising: identifying, using the at least one data processor and based at least on the trained output of the discriminator network, one or more scenes encountered by the vehicle having a probability greater than a threshold of being a synthetic scene; as well as A perception system of the vehicle is updated using the at least one data processor and based at least on the one or more scenarios.

9. The method according to any one of claims 1 to 8, further comprising: A number of out of operational design domain (ODD) scenarios encountered by the vehicle in a new location is determined using the at least one data processor and based at least on the trained output of the discriminator network.

10. The method according to any one of claims 1 to 9, wherein: The trained discriminator network outputs a first probability that the vehicle is in the out-of-operation design domain scenario at a first time period and / or a first location, wherein the trained discriminator network also outputs a second probability that the vehicle is in the out-of-operation design domain scenario at a second time period and / or a second location, and wherein the output of the trained discriminator network is determined based on at least the first probability and the second probability.

11. The method according to claim 10, wherein: An output of the trained discriminator network is determined by fusing at least the first probability and the second probability.

12. The method according to claim 11, wherein: The first probability and the second probability are fused by applying one or more of Bayesian fusion, Dempster-Shafer fusion, Yager combination rule, Dubois-Prade combination rule, Denoeux cautious rule.

13. The method according to any one of claims 1 to 12, further comprising: determining an uncertainty associated with an output of the trained discriminator network; as well as An output of the trained discriminator network is adjusted based at least on the uncertainty.

14. The method according to claim 13, wherein: The uncertainty associated with the trained output of the discriminator network includes uncertainty associated with a first probability that the vehicle is in the out of operational design domain scenario, i.e., out of ODD scenario, and / or a second probability that the vehicle is not in the out of operational design domain scenario, i.e., out of ODD scenario.

15. The method according to any one of claims 13 to 14, wherein: Uncertainty associated with an output of the trained discriminator network is determined by applying evidence classification and / or temperature scaling for probability calibration.

16. The method according to any one of claims 1 to 15, further comprising: The at least one data processor is used to train the generative adversarial network (GAN) including the generator network and the discriminator network.

17. A system comprising: at least one data processor; as well as at least one memory storing instructions that, when executed by the at least one data processor, cause operations comprising: Using the at least one data processor, applying a discriminator network of a generative adversarial network (GAN) to detect when a vehicle encounters an out of operational design domain (ODD) scenario, the generative adversarial network (GAN) comprising a generator network and the discriminator network, the generator network being trained to generate one or more synthetic scenarios, and the discriminator network being trained to distinguish between at least one real scenario and the one or more synthetic scenarios generated by the generator network; as well as The movement of the vehicle is controlled in response to the trained output of the discriminator network indicating that the vehicle is encountering the out of operational design domain scenario (out of ODD scenario).

18. The system of claim 17, wherein: The generative adversarial network (GAN) is trained to generate the one or more synthetic scenes based at least on one or more driving logs representing one or more real scenes.

19. A system according to any one of claims 17 to 18, wherein: The operations also include: Using the at least one data processor, a remote vehicle assistance request (RVA request) is sent in response to the output of the trained discriminator network indicating that the vehicle is encountering the out of operational design domain (ODD) scenario.

20. A system according to any one of claims 17 to 19, wherein: Controlling the movement of the vehicle in response to the vehicle encountering the out of operational design domain scenario (out of ODD scenario) includes at least one of deceleration and braking.

21. A system according to any one of claims 17 to 20, wherein: The trained discriminator network outputs a probability map in which each region of a plurality of regions is associated with a probability that the region is outside the operational design domain, ie, outside the ODD.

22. The system of claim 21, wherein: Controlling the movement of the vehicle includes avoiding one or more regions of the plurality of regions having a probability greater than a threshold of exceeding an operational design domain, ie, exceeding an ODD, based at least on the probability map output by the trained discriminator network.

23. A system according to any one of claims 17 to 22, wherein: The generative adversarial network (GAN) is trained based on training data including one or more of random noise, maps and raw sensor data.

24. A system according to any one of claims 17 to 23, wherein: The operations also include: identifying one or more scenes encountered by the vehicle with a probability greater than a threshold of being synthetic scenes based at least on the trained outputs of the discriminator network; and A perception system of the vehicle is updated based at least on the one or more scenarios.

25. A system according to any one of claims 17 to 24, wherein: The operations also include: A number of out of operational design domain (ODD) scenarios encountered by the vehicle in a new location is determined using the at least one data processor and based at least on the trained output of the discriminator network.

26. A system according to any one of claims 17 to 25, wherein: The trained discriminator network outputs a first probability that the vehicle is in the out-of-operation design domain scenario at a first time period and / or a first location, wherein the trained discriminator network also outputs a second probability that the vehicle is in the out-of-operation design domain scenario at a second time period and / or a second location, and wherein the output of the trained discriminator network is determined based on at least the first probability and the second probability.

27. The system of claim 26, wherein: An output of the trained discriminator network is determined by fusing at least the first probability and the second probability.

28. A system according to any one of claims 17 to 27, wherein: The operations also include: determining uncertainty associated with an output of the trained discriminator network by applying evidence classification and / or temperature scaling for probability calibration; and An output of the trained discriminator network is adjusted based at least on the uncertainty.

29. A non-transitory computer readable medium storing instructions that, when executed by at least one data processor, cause operations comprising: Using the at least one data processor, applying a discriminator network of a generative adversarial network (GAN) to detect when a vehicle encounters an out of operational design domain (ODD) scenario, the generative adversarial network (GAN) comprising a generator network and the discriminator network, the generator network being trained to generate one or more synthetic scenarios, and the discriminator network being trained to distinguish between at least one real scenario and the one or more synthetic scenarios generated by the generator network; as well as The movement of the vehicle is controlled in response to the trained output of the discriminator network indicating that the vehicle is encountering the out of operational design domain scenario (out of ODD scenario).

30. A method comprising: Using at least one data processor, training a generative adversarial network (GAN) comprising a generator network and a discriminator network, wherein the generator network is trained to generate one or more synthetic scenes, and the discriminator network is trained to distinguish between at least one real scene and the one or more synthetic scenes generated by the generator network; Using the at least one data processor, applying the trained discriminator network to detect when a vehicle encounters an out of operational design domain scenario, i.e., an out of ODD scenario; as well as Using the at least one data processor, movement of the vehicle is controlled in response to the output of the trained discriminator network indicating that the vehicle is encountering the out of operational design domain scenario (out of ODD scenario).

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