Object tracking

Abstract

A method for object tracking is provided. The method may include receiving sensor data characterizing a respective detected object. The method may also include generating a data structure based on data characterizing the respective detected objects. The data structure may include nodes representing a state of the object and a graph of edges representing an assumed transition of the state of the object. The method may further include applying the predictive model to the data structure. A prediction model may be trained to receive the state as input and produce an identification of a set of nodes and edges corresponding to one of the respective detected objects. The method may also include providing data based on the identification of the set of nodes and edges to a planning system of the vehicle, and operating the vehicle based on the provided data. A system and a computer program product are also provided.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of priority to U.S. Patent Application No. 17 / 866,743, filed on July 18, 2022, entitled “OBJECT TRACKING,” the contents of which are incorporated herein by reference in their entirety. Background Art

[0003] An autonomous vehicle is capable of sensing its surroundings and navigating to a target location with little to no human input. In order to safely traverse a chosen path while avoiding obstacles that may be present along the way, the vehicle may rely on various types of sensor data to detect objects to avoid. For example, sensor data may be associated with the vehicle or pedestrians moving relative to the vehicle. The ability to accurately determine and track objects may be reduced in crowded or highly occluded environments. 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 5 is a diagram of an implementation of a process for object tracking;

[0010] Figure 6 is a diagram of the detailed implementation of the object tracking system;

[0011] Figure 7 is a flow chart of a process for object tracking; and

[0012] Figure 8 is a diagram of an example graph generated by the object tracking system described herein. 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 object tracking for objects that appear in an environment in which an autonomous vehicle is operating. Embodiments herein provide systems, methods, and computer program products for receiving data characterizing an object detected by a sensor of a vehicle. Based on sensor data, a data structure corresponding to a state and a hypothetical transition or change of the state can be generated. For example, a state can be represented as a node in a data structure, and can include the speed, heading, and / or physical appearance of an object. The hypothetical transition of the state of an object can be represented as an edge or link connecting two nodes of a data structure, and can correspond to a change in the state of the object between two or more time instances in which an object is detected using a sensor. A prediction model can be applied to a data structure to generate an identification of a set of nodes and edges corresponding to a detected object. The identification can correspond to the trajectory of the object in an environment shared with the vehicle, and can be provided to a planning system of the vehicle. The vehicle can be operated based on the identification (or trajectory) of the detected object.

[0023] With the implementation of the systems, methods, and computer program products described herein, techniques for object detection can provide improved accuracy in object state determination by minimizing a cost function in state transitions during a user-defined time period compared to the entire history of acquired sensor data. As a result, computational processing can be reduced and the need for dedicated computing equipment in a vehicle can be minimized. Since processing is performed during a user-defined time period, errors do not accumulate, and incorrect data elements generated during state determination can be dynamically replaced. Thus, the resulting data structure for determining object state can be improved, and the identification of mobile objects from static objects can be more robust. This can provide the vehicle with more accurate object state information of its operating environment, and can allow the vehicle's planning system to plan routes with greater accuracy and less likelihood of contact or even collision with detected objects.

[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 remote AV system 114, 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 1In some embodiments, object tracking system 505 may be included in environment 100. Object tracking system 505 may be configured within vehicle 102 or external to vehicle 102. In some embodiments, a first portion of object tracking system 505 may be configured within vehicle 102, and a second portion of object tracking system 505 may be configured external to vehicle 102.

[0035] Reference now Figure 2 , vehicle 200 includes autonomous system 202, powertrain control system 204, steering control system 206, and braking system 208. In some embodiments, vehicle 200 is compatible with vehicle 102 (see Figure 1 ) is the same or similar. In some embodiments, vehicle 200 may correspond to any one of vehicles 102. In some embodiments, vehicle 102 has autonomous capabilities (e.g., implementing at least one function, feature, and / or device, etc., which enables vehicle 200 to operate partially or completely without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon reliance on human intervention) and / or highly autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in certain circumstances), etc.). 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, 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 devices 202e, autonomous vehicle computing 202f, and a drive-by-wire (DBW) system 202h.

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

[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.

[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.

[0050] Reference now Figure 3 , a schematic diagram of an exemplary device 300. As illustrated, device 300 includes a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, a communication interface 314, and a bus 302. In some embodiments, device 300 corresponds to: at least one device of vehicle 102 (e.g., at least one device of a system of vehicle 102); at least one device of object tracking system 505; 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), one or more devices of object tracking system 505, and / or one or more devices of network 112 (e.g., one or more devices of a system of network 112) include at least one device 300 and / or at least one component of device 300. As Figure 3 As shown, apparatus 300 includes a bus 302 , a 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 for communication between components of the licensing device 300. In some embodiments, the processor 304 is implemented in hardware, software, or a combination of hardware and software. In some examples, 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 3The 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, planning system 404 receives data associated with a destination and generates data associated with at least one route (e.g., route 106) along which a vehicle (e.g., vehicle 102) can travel toward the destination. In some embodiments, planning system 404 periodically or continuously receives data (e.g., data associated with the classification of physical objects described above) from perception system 402, and planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by perception system 402. In some embodiments, planning system 404 receives data associated with an updated position of a vehicle (e.g., vehicle 102) from positioning system 406, and planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by positioning system 406.

[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 a powertrain control system (e.g., DBW system 202h and / or powertrain control system 204, etc.), a steering control system (e.g., steering control system 206), and / or a braking system (e.g., braking system 208). In the example, in the case where the trajectory includes a left turn, the control system 408 transmits a control signal to cause the steering control system 206 to adjust the steering angle of the vehicle 200, thereby causing the vehicle 200 to turn left. Additionally or alternatively, the control system 408 generates and transmits control signals to cause other devices of the vehicle 200 (e.g., headlights, turn signals, door locks, and / or windshield wipers, etc.) to change state.

[0066] In some embodiments, the 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 multilayer 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, the perception system 402, planning system 404, positioning system 406, control system 408, and / or object tracking system 505 implement at least one machine learning model alone or in combination with one or more of the above systems. In some examples, the perception system 402, planning system 404, positioning system 406, control system 408, and / or object tracking system 505 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 Figure 4B 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 with respect to implementing CNN 420 by perception system 402. 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, control system 408, and / or object tracking system 505, etc.) other than 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 at least one subsampling function (e.g., configured to perform at least one subsampling function) (as described below with respect to FIGS. 4C and 4D ), CNN 420 merges the amount of data associated with the initial input.

[0071] The perception system 402 performs a 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.). A detailed description of the convolution operation is included below with respect to FIG. 4C.

[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 5 , a diagram illustrating an implementation 500 of a process for object tracking using sensor data. In some embodiments, the implementation 500 includes an object tracking system 505, vehicles 102a-102n and / or vehicles 200, objects 104a-104n, routes 106a-106n, areas 108, vehicle-to-infrastructure (V2I) devices 110, networks 112, remote autonomous vehicle (AV) systems 114, fleet management systems 116, and / or V2I systems 118. In some embodiments, the object tracking system 505 includes, forms a part of, is coupled to, and / or uses: vehicles 102a-102n and / or vehicles 200, objects 104a-104n, routes 106a-106n, areas 108, vehicle-to-infrastructure (V2I) devices 110, networks 112, remote autonomous vehicle (AV) systems 114, fleet management systems 116, and / or V2I systems 118.

[0077] like Figure 5 As shown, implementation 500 includes an object tracking system 505. Object tracking system 505 includes an object tracker 510 configured to obtain sensor data from one or more sensors 202 attached to the vehicle and process the sensor data. In some embodiments, the sensor data is used to determine and provide a trajectory of an object within an environment in which the vehicle is co-located. Based on the received sensor data, object tracker 510 determines hypothesized object trajectories 515, which are provided to planning system 404 for use in autonomously navigating the vehicle relative to the detected object. For example, the trajectory of the vehicle is determined by planning system 404 to avoid the object trajectory 515 determined by object tracking system 505, or otherwise navigate away from the object trajectory 515 or navigate relative to the object trajectory 515.

[0078] Reference now Figure 6 , example Figure 5 FIG. 501 is a diagram of a detailed implementation of an object tracking system 505. Figure 6 As shown, object tracking system 505 includes object tracker 510. Object tracker 510 receives sensor data 520 from any of sensors 202. Sensor data 520 is processed by graph builder 525 to form a data structure of nodes and edges corresponding to one or more detected objects associated with sensor data 520. In some embodiments, the data structure is a graph. The nodes of the data structure correspond to the state of the detected object at a particular time (such as the speed, heading, or physical appearance of the object). The edge connecting two nodes corresponds to a hypothetical transition of the state of the object between a first time instance and a second time instance.

[0079] For example, a data structure is formed based on sensor data associated with a pedestrian walking near an intersection that a vehicle is approaching. Nodes are associated with the pedestrian's speed, direction, and appearance. Edges are associated with hypothetical changes in the pedestrian's speed, direction, or appearance. For example, a hypothetical change in the pedestrian's direction can be associated with the pedestrian's continued travel outside the intersection or a change in the pedestrian's travel into the intersection.

[0080] The graph builder 525 implements a processing window parameter that is configured to build a graph in relation to the sensor data 520 associated with a user-defined amount of time. For example, the processing window parameter is configured to be 1 second or 2 seconds in length, and the graph builder builds a data structure based on the sensor data acquired within the previous 1 second or 2 seconds. Advantageously, the use of the processing window parameter corrects the previously erroneous state or state transition because the previously incorrect state or state transition determination does not incorrectly affect the subsequent state or state transition determination. This enables the graph builder 525 to operate with conditional independence, so that the current state or state transition is determined based on a portion of the past history (e.g., corresponding to the value of the processing window parameter) rather than the longer history for which the sensor data 520 is collected. In some embodiments, the processing window parameter can be 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5 or 5.0 seconds in length.

[0081] When new sensor data 520 is acquired, the new sensor data 520 can be provided to the graph builder 525, and a new data structure (e.g., a new graph) can be formed. Thus, when a new detection instance is captured by the sensor data 520, new nodes and new edges are determined and configured in the data structure, thereby updating the graph in a streaming manner.

[0082] In some embodiments, the sensor data 520 includes data from different sensing modalities, such as data from a camera 202a, a LiDAR sensor 202b, a Radar sensor 202c, a microphone 202d, or a combination thereof, etc. As a result, processing the sensor data 520 from different modalities creates unordered sensor data that is inconsistent in time from a first time instance to a subsequent time instance. To address this situation, the graph builder 525 is configured to detect sensor data associated with an earlier graph state than an initial or starting graph state. In response to the detection, the graph builder 525 determines a new node position for the object and inserts a new node into the determined new node position. The graph builder 525 then reorders and categorizes the graph based on the new node position.

[0083] The data structure or graph constructed by the graph builder 525 is provided to the graph element generator 530. The graph element generator is configured with a prediction model such as CNN 420, which is configured to receive a state (e.g., at least one of the speed, heading, and / or physical appearance of the detected object) as an input and generate a set of at least one node and at least one edge associated with the object for which the input of the state is received as an output. The prediction model is configured to solve the output by linear assignment to minimize the cost. The cost is considered to be the measured distance between the state and the transition of the state at the past time compared to the current time. For example, the measured distance between the states can be associated with the magnitude of the change in the speed, heading, and / or appearance of the sensed object. Thus, a greater cost indicates a greater change in the state of the object, and allowing a greater cost to continue may result in an inaccurate prediction of the object state transition. Solving the assignment for all object detections jointly at any point in time minimizes the distance for all object detections, thereby outputting new states and assuming new states more robustly and more accurately. In some embodiments, different motion models are used to tune the graph element generator 530.

[0084] When the graph element generator 530 identifies new nodes and edges based on the state of the detected object, the trajectory of the object is formed as the best estimate of the object in time. In some embodiments, the newly identified nodes and edges are provided to a smoothing function to improve state accuracy and state transition accuracy. For example, in some embodiments, the smoothing function includes a Kalman filter. In some embodiments, a Kalman filter is applied to output nodes and edges associated with the new state and state transition of the detected moving object. In some embodiments, the smoothing function includes a Kalman filter in combination with a static object motion model to output nodes and edges associated with the new state and state transition of the detected static object. As the sensor detects the object over time, a trajectory 515 is formed based on traces associated with incremental changes in nodes (e.g., object states) and edges (e.g., assumed changes in the state of the object).

[0085] Trajectory 515 is provided to vehicle planning system 404 , and the vehicle is operated to navigate the vehicle in relation to trajectory 515 corresponding to object 104 .

[0086] Reference now Figure 7 , a flow chart illustrating a process 700 for object tracking using the object tracking system described herein. In some embodiments, one or more of the steps described for process 700 are performed by object tracking system 505 (e.g., completely and / or partially, etc.). Additionally or alternatively, in some embodiments, one or more of the steps described for process 700 are performed by another device or group of devices (such as perception system 402, planning system 404, positioning system 406, and / or control system 408, etc.) separate from or including object tracking system 505 (e.g., completely and / or partially, etc.).

[0087] At 702, the process includes receiving data representing respective detected objects from at least two sensor systems of a vehicle. The data includes sensor data received from at least two sensors configured in the autonomous system 202 and associated with at least one object 104. For example, the data is received from at least one of the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, and the microphone 202d, or a combination thereof. In some embodiments, the object 104 may be a stationary object or a moving object.

[0088] At 704, the processing includes: generating a data structure based on data characterizing the respective detected objects. The data structure includes a graph of nodes and edges connecting these nodes. The node represents the state of one of the respective detected objects at a specific time. The state may include the position, speed, acceleration, heading, heading rate (e.g., angular velocity), probability of existence, physical appearance, and / or class of the object. The edge represents a hypothetical transition of the state of one of the respective detected objects. The edge is considered to be a valid or invalid state transition. Invalid state transitions can be determined based on spatiotemporal constraints. For example, state transitions within the same time frame may be invalid because different nodes cannot belong to the same object at the same time. In addition, the time constraint can be configured so that if the position of the object is outside the physically possible range that the node of the earlier time frame travels to (e.g., too far from the physically possible range), the node cannot have an edge to another node in the future time frame. For example, if an edge connecting a first node associated with a state of a first object connects the first node to a second node associated with a state of a second object (e.g., an object different from the object associated with the first node), and thus two different objects have been detected and misidentified as the same object, then the edge is an invalid edge. When an edge connects nodes associated with the same object, and thus represents a transition of the first object from a first state of the first object to a second state, the edge is considered a valid edge. For example, a valid transition state may include an optimal transition state determined using a linear assignment solver configured to generate a graph of nodes and edges at 704. Calculate the cost for each edge. The cost of each edge may represent the likelihood of a later node continuing from an earlier node, and therefore represents the same underlying object. Based on the constraint that a single node can be continued forward in time by at most one node (e.g., the same object cannot be split into multiple objects over time), the solver may be configured to solve the graph as a bipartite matching problem to minimize the total likelihood of state transitions between all nodes in the graph.

[0089] In some embodiments, the hypothesized transition of the state of one of the respective detected objects is a transition between a first state at a current time and a second state at a future time.

[0090] At 706, the process includes: applying the prediction model to the data structure. As described herein, the prediction model is trained in a machine learning process to receive speed, heading, and physical appearance as inputs and produce an identification of a set of nodes and edges (e.g., at least one node and at least one edge) corresponding to one of the respective detected objects. The prediction model can be trained using 3D annotations to receive multiple frames of LiDAR point cloud data and camera data and output 3D object detections with associated object states (e.g., speed, heading, acceleration of the detected object), object appearance (e.g., shape and / or features of the object), and a classification of the type of the object (e.g., vehicle, bicycle, or pedestrian).

[0091] The prediction model identifies nodes and edges that may correspond to states of the same object detected by sensor 202. The prediction model identifies edges that connect nodes associated with objects detected by sensor 202 (e.g., different time instances of the same object). An output of the prediction model (e.g., an identification of a set of nodes and edges) corresponds to a hypothesized trajectory that would be taken by one of the respective detected objects. For example, the identification includes at least one of a speed, a heading, and / or a physical appearance of the detected object.

[0092] In some embodiments, instances of sensor data associated with a specific time are acquired, stored, or otherwise received and indexed according to the specific time. Thus, the predictive model uses the indexed sensor data to predict edges as hypothetical transitions of the state of an object between two specific times.

[0093] In some embodiments, generating an identification of a set of nodes and edges corresponding to one of the respective detected objects includes applying a smoothing function, and outputting the set of nodes and edges based on the applied smoothing function. For example, a Kalman filter may be applied to improve the accuracy of state determination.

[0094] At 708, the processing includes providing data based on the identification of the set of nodes and edges to a planning system of the vehicle (such as planning system 404, etc.). In some embodiments, generating, applying, and providing are performed continuously as data characterizing the respective detected objects are received. For example, each instance of received sensor data causes the generating, applying, and providing operations to be performed dynamically and continuously so that the predicted trajectory of the detected object is continuously determined and incorporated by the planning system 404.

[0095] At 710 , the process includes causing the vehicle 102 to operate based on providing the data to the planning system 404 of the vehicle.

[0096] Reference now Figure 8, graph 800 includes six nodes (e.g., nodes 1 to 6). The nodes are respectively associated with the state of at least one detected object, and the edges connecting the node pairs are associated with valid hypothetical transitions of the speed, heading, or physical appearance of the object. For example, the edges can represent respective valid hypothetical transitions in the state of the following object, wherein the object is located near a vehicle and for which the sensor of the vehicle has acquired sensor data as described herein. Graph 800 is associated with multiple trace segments for a particular object. As described herein, a trace segment can be associated with or defined as at least one edge and can include multiple edges. A trace segment is one or more hypothetical transitions of states that have been determined to be valid transitions for the state of the respective detected object. In some embodiments, a trace segment may include a short measurement sequence that is determined to originate from the same target or object. The measurement sequence can be filtered to ensure that the estimate includes states with a minimum amount of covariance.

[0097] Valid trace segments may be fused or spliced ​​together to form a trajectory for an object. Each node in the graph may include a trace segment because each node already contains all of the state (e.g., position, velocity, heading) of the object. In some embodiments, splicing may be associated with or defined as joining two or more valid trace segments. For example, Figure 8 As shown, the state of the first object at an initial time (e.g., T=0) is identified by node 1, and the state of the second object at T=0 is identified by node 2. Based on the received sensor data, the object tracking system 505 generates a data structure 800, which includes a first set of hypothetical transitions of nodes 1 and 2 at future times T=1 and T=2. The data structure 800 is generated to include valid hypothetical state transitions as edges for future times T=1 and T=2, such as edges 1 to 10. The object tracking system 505 can determine multiple possible solutions. For example, nodes 1, 4, and 5 via edges 1 and 10 can represent a valid trace segment solution. Nodes 2, 3, and 6 via edges 6 and 7 can represent a second valid trace segment solution. The object tracking system 505 can further determine, based on one or more cost parameter settings, that an optimal solution or trail segment for each object that jointly accounts for all assumed state transitions is an optimal solution or trail segment as follows: a first object trajectory is associated with node 1 and node 6 via edge 2, and a second object trajectory is associated with node 2, node 3, and node 5 via edges 6 and 8.

[0098] The techniques for object tracking described herein may provide technical solutions that may provide technical advantages over existing object tracking systems. These advantages may include, but are not limited to, increased processing time and accuracy for object determination in an autonomous vehicle operating environment. The object tracking system described herein may also provide improved detection of moving objects compared to static objects in sparse or dense operating environments. As a result, more accurate object detection data may be provided to the planning system of the vehicle, and the vehicle may operate more safely in a wider variety of operating conditions where objects may be present.

[0099] 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: receiving, using at least one processor, data representative of respective detected objects from at least two sensor systems of the vehicle; generating, with the at least one processor, a data structure based on data characterizing the respective detected objects, the data structure comprising a graph of nodes and edges, wherein nodes of the nodes represent states of one of the respective detected objects at particular times, the states comprising speed, heading, and physical appearance, and wherein edges of the edges represent hypothesized transitions of states of the one of the respective detected objects; applying, with the at least one processor, a predictive model to the data structure, wherein the predictive model is trained to receive as input the speed, the heading, and the physical appearance and to produce an identification of a set of nodes and edges corresponding to the one of the respective detected objects; providing, using the at least one processor, data based on the identification of the set of nodes and edges to a planning system of the vehicle; and The vehicle is caused to operate based on providing the data to the planning system of the vehicle.

2. The method according to claim 1, wherein: The assumed transition of the state of the one of the respective detected objects is between a first state at a current time and a second state at a future time.

3. The method according to claim 1, wherein: Said generating, said applying and said providing are performed continuously upon receiving data characterizing said respective detected object.

4. The method according to claim 1, wherein: The identification of the set of nodes and edges corresponds to a hypothesized trajectory to be taken by the one of the respective detected objects.

5. The method according to claim 1, wherein: The data structure can be generated based on user defined processing window parameters that define a time period during which the data structure is generated using data characterizing the respective detected object.

6. The method according to claim 1, wherein: Generating an identification of a set of nodes and edges corresponding to the one of the respective detected objects includes applying a smoothing function and outputting the set of nodes and edges based on the applied smoothing function.

7. The method according to claim 6, wherein: The one of the respective detected objects is a moving object.

8. The method according to claim 6, wherein: The one of the respective detected objects is a static object.

9. A system comprising: at least one processor; as well as at least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to: receiving data representative of respective detected objects from at least two sensor systems of the vehicle; generating a data structure based on the data characterizing the respective detected objects, the data structure comprising a graph of nodes and edges, wherein nodes of the nodes represent states of one of the respective detected objects at a particular time, the states comprising speed, heading, and physical appearance, and wherein edges of the edges represent hypothesized transitions of states of the one of the respective detected objects; applying a prediction model to the data structure, wherein the prediction model is trained to receive as input the speed, the heading, and the physical appearance and to produce an identification of a set of nodes and edges corresponding to the one of the respective detected objects; providing data based on the identification of the set of nodes and edges to a planning system of the vehicle; and The vehicle is caused to operate based on providing the data to the planning system of the vehicle.

10. The system according to claim 9, wherein: The assumed transition of the state of the one of the respective detected objects is between a first state at a current time and a second state at a future time.

11. The system according to claim 9, wherein: Said generating, said applying and said providing are performed continuously upon receiving data characterizing said respective detected object.

12. The system according to claim 9, wherein: The identification of the set of nodes and edges corresponds to a hypothesized trajectory to be taken by the one of the respective detected objects.

13. The system according to claim 9, wherein: The data structure can be generated based on user defined processing window parameters that define a time period during which the data structure is generated using data characterizing the respective detected object.

14. The system according to claim 9, wherein: Generating an identification of a set of nodes and edges corresponding to the one of the respective detected objects includes applying a smoothing function and outputting the set of nodes and edges based on the applied smoothing function.

15. The system of claim 14, wherein: The one of the respective detected objects is a moving object.

16. The system of claim 14, wherein: The one of the respective detected objects is a static object.

17. At least one non-transitory storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to: receiving data representative of respective detected objects from at least two sensor systems of the vehicle; generating a data structure based on the data characterizing the respective detected objects, the data structure comprising a graph of nodes and edges, wherein: wherein a node of the nodes represents a state of one of the respective detected objects at a particular time, the state comprising speed, heading, and physical appearance, and wherein an edge of the edges represents a hypothetical transition of the state of the one of the respective detected objects; applying a prediction model to the data structure, wherein the prediction model is trained to receive as input the speed, the heading, and the physical appearance and to produce an identification of a set of nodes and edges corresponding to the one of the respective detected objects; providing data based on the identification of the set of nodes and edges to a planning system of the vehicle; and The vehicle is caused to operate based on providing the data to the planning system of the vehicle.

18. The at least one non-transitory storage medium of claim 17, wherein: The assumed transition of the state of the one of the respective detected objects is between a first state at a current time and a second state at a future time.

19. The at least one non-transitory storage medium of claim 17, wherein: The instructions further cause the generating, the applying, and the providing to be performed continuously upon receiving data characterizing the respective detected objects.

20. The at least one non-transitory storage medium of claim 17, wherein: The identification of the set of nodes and edges corresponds to a hypothesized trajectory to be taken by the one of the respective detected objects.

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