Object tracking

Provided are methods for object tracking, which can include receiving sensor data characterizing respective detected objects. The methods can also include generating a data structure based on the data characterizing the respective detected objects. The data structure can include a graph of nodes representing states of the objects and edges representing hypothetical transitions in states of the objects. The methods can also include applying a predictive model to the data structure. The predictive model can be trained to receive the state as inputs and produce an identification of a set of nodes and edges corresponding to the one of the respective detected objects. The methods can further include providing data based on the identification of the set of nodes and edges to a planning system of the vehicle and causing the vehicle to operate based on providing the data. Systems and computer program products are also provided.

BACKGROUND

An autonomous vehicle may be capable of sensing its surrounding environment and navigating to a goal location with minimal to no human input. In order to safely traverse a selected 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 be avoided. For example, the sensor data can be associated with a vehicle or a pedestrian moving relative to the vehicle. The ability to determine and track objects accurately can be reduced in crowded or highly occluded environments.

DETAILED DESCRIPTION

In the following description numerous specific details are set forth in order to provide a thorough understanding of the present disclosure for the purposes of explanation. It will be apparent, however, that the embodiments described by the present disclosure can be practiced without these specific details. In some instances, well-known structures and devices are illustrated in block diagram form in order to avoid unnecessarily obscuring aspects of the present disclosure.

Specific arrangements or orderings of schematic elements, such as those representing systems, devices, modules, instruction blocks, data elements, and/or the like are illustrated in the drawings for ease of description. However, it will be understood by those skilled in the art that the specific ordering or arrangement of the schematic elements in the drawings is not meant to imply that a particular order or sequence of processing, or separation of processes, is required unless explicitly described as such. Further, the inclusion of a schematic element in a drawing is not meant to imply that such element is required in all embodiments or that the features represented by such element may not be included in or combined with other elements in some embodiments unless explicitly described as such.

Further, where connecting elements such as solid or dashed lines or arrows are used in the drawings to illustrate a connection, relationship, or association between or among two or more other schematic elements, the absence of any such connecting elements is not meant to imply that no connection, relationship, or association can exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the 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, where a connecting element represents communication of signals, data, or instructions (e.g., “software instructions”), it should be understood by those skilled in the art that such element can represent one or multiple signal paths (e.g., a bus), as may be needed, to affect the communication.

Although the terms first, second, third, and/or the like are used to describe various elements, these elements should not be limited by these terms. The terms first, second, third, and/or the like are used only to distinguish one element from another. For example, a first contact could be termed a second contact and, similarly, a second contact could be termed a first contact without departing from the scope of the described embodiments. The first contact and the second contact are both contacts, but they are not the same contact.

As used herein, the term “if” is, optionally, construed to mean “when”, “upon”, “in response to determining,” “in response to detecting,” and/or the like, depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining,” “in response to determining,” “upon detecting [the stated condition or event],” “in response to detecting [the stated condition or event],” and/or the like, depending on the context. Also, as used herein, the terms “has”, “have”, “having”, or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise.

General Overview

In some aspects and/or embodiments, systems, methods, and computer program products described herein include and/or implement object tracking for objects present in an environment in which an autonomous vehicle is operating. The embodiments herein provide systems, methods, and computer program products for receiving data characterizing an object detected by sensors of the vehicle. Based on the sensor data, a data structure corresponding to a state and a hypothetical transition or change in the state can be generated. For example, the state can be represented as nodes in the data structure and can include a velocity, a heading, and/or a physical appearance of the object. The hypothetical transition in state of the object can be represented as edges or links connecting two nodes of the data structure and can correspond to changes in the state of the object between two or more instances of time at which the object was detected by the sensors. A predictive model can be applied to the data structure to produce an identification of a set of nodes and edges corresponding to the detected object. The identification can correspond to a trajectory of the object in the 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.

By virtue of the implementation of systems, methods, and computer program products described herein, the techniques for object detection can provide improved accuracy in object state determination by minimizing cost functions in state transitions over a user-defined temporal period as compared to an entire history of acquired sensors data. As a result, computational processing can be reduced and the need for specialized computing equipment in the vehicle can be minimized. Because the processing is performed over a user-defined temporal period, errors do not accumulate and improper data elements generated during the state determination can be replaced dynamically. The resulting data structure used to determine object state can thus be improved and identification of moving objects vs. 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 planning system of the vehicle to plan routes with greater precision and less likelihood of contact, or even collision, with a detected object.

Referring now toFIG.1, illustrated is example environment100in which vehicles that include autonomous systems, as well as vehicles that do not, are operated. As illustrated, environment100includes vehicles102a-102n, objects104a-104n, routes106a-106n, area108, vehicle-to-infrastructure (V2I) device110, network112, remote autonomous vehicle (AV) system114, fleet management system116, and V2I system118. Vehicles102a-102n, vehicle-to-infrastructure (V2I) device110, network112, autonomous vehicle (AV) system114, fleet management system116, and V2I system118interconnect (e.g., establish a connection to communicate and/or the like) via wired connections, wireless connections, or a combination of wired or wireless connections. In some embodiments, objects104a-104ninterconnect with at least one of vehicles102a-102n, vehicle-to-infrastructure (V2I) device110, network112, autonomous vehicle (AV) system114, fleet management system116, and V2I system118via wired connections, wireless connections, or a combination of wired or wireless connections.

Vehicles102a-102n(referred to individually as vehicle102and collectively as vehicles102) include at least one device configured to transport goods and/or people. In some embodiments, vehicles102are configured to be in communication with V2I device110, remote AV system114, fleet management system116, and/or V2I system118via network112. In some embodiments, vehicles102include cars, buses, trucks, trains, and/or the like. In some embodiments, vehicles102are the same as, or similar to, vehicles200, described herein (seeFIG.2). In some embodiments, a vehicle200of a set of vehicles200is associated with an autonomous fleet manager. In some embodiments, vehicles102travel along respective routes106a-106n(referred to individually as route106and collectively as routes106), as described herein. In some embodiments, one or more vehicles102include an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system202).

Objects104a-104n(referred to individually as object104and collectively as objects104) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, at least one structure (e.g., a building, a sign, a fire hydrant, etc.), and/or the like. Each object104is stationary (e.g., located at a fixed location for a period of time) or mobile (e.g., having a velocity and associated with at least one trajectory). In some embodiments, objects104are associated with corresponding locations in area108.

Routes106a-106n(referred to individually as route106and collectively as routes106) are each associated with (e.g., prescribe) a sequence of actions (also known as a trajectory) connecting states along which an AV can navigate. Each route106starts at an initial state (e.g., a state that corresponds to a first spatiotemporal location, velocity, and/or the like) and a final goal state (e.g., a state that corresponds to a second spatiotemporal location that is different from the first spatiotemporal location) or goal region (e.g. a subspace of acceptable states (e.g., terminal states)). In some embodiments, the first state includes a location at which an individual or individuals are to be picked-up by the AV and the second state or region includes a location or locations at which the individual or individuals picked-up by the AV are to be dropped-off. In some embodiments, routes106include a plurality of acceptable state sequences (e.g., a plurality of spatiotemporal location sequences), the plurality of state sequences associated with (e.g., defining) a plurality of trajectories. In an example, routes106include only high level actions or imprecise state locations, such as a series of connected roads dictating turning directions at roadway intersections. Additionally, or alternatively, routes106may include more precise actions or states such as, for example, specific target lanes or precise locations within the lane areas and targeted speed at those positions. In an example, routes106include a plurality of precise state sequences along the at least one high level action sequence with a limited lookahead horizon to reach intermediate goals, where the combination of successive iterations of limited horizon state sequences cumulatively correspond to a plurality of trajectories that collectively form the high level route to terminate at the final goal state or region.

Area108includes a physical area (e.g., a geographic region) within which vehicles102can navigate. In an example, area108includes at least one state (e.g., a country, a province, an individual state of a plurality of states included in a country, etc.), at least one portion of a state, at least one city, at least one portion of a city, etc. In some embodiments, area108includes 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 area108includes at least one unnamed road such as a driveway, a section of a parking lot, a section of a vacant and/or undeveloped lot, a dirt path, etc. In some embodiments, a road includes at least one lane (e.g., a portion of the road that can be traversed by vehicles102). In an example, a road includes at least one lane associated with (e.g., identified based on) at least one lane marking.

Vehicle-to-Infrastructure (V2I) device110(sometimes referred to as a Vehicle-to-Infrastructure (V2X) device) includes at least one device configured to be in communication with vehicles102and/or V2I infrastructure system118. In some embodiments, V2I device110is configured to be in communication with vehicles102, remote AV system114, fleet management system116, and/or V2I system118via network112. In some embodiments, V2I device110includes a radio frequency identification (RFID) device, signage, cameras (e.g., two-dimensional (2D) and/or three-dimensional (3D) cameras), lane markers, streetlights, parking meters, etc. In some embodiments, V2I device110is configured to communicate directly with vehicles102. Additionally, or alternatively, in some embodiments V2I device110is configured to communicate with vehicles102, remote AV system114, and/or fleet management system116via V2I system118. In some embodiments, V2I device110is configured to communicate with V2I system118via network112.

Network112includes one or more wired and/or wireless networks. In an example, network112includes 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., the public switched telephone network (PSTN), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, a cloud computing network, etc., a combination of some or all of these networks, and/or the like.

Remote AV system114includes at least one device configured to be in communication with vehicles102, V2I device110, network112, remote AV system114, fleet management system116, and/or V2I system118via network112. In an example, remote AV system114includes a server, a group of servers, and/or other like devices. In some embodiments, remote AV system114is co-located with the fleet management system116. In some embodiments, remote AV system114is involved in the installation of some or all of the components of a vehicle, including an autonomous system, an autonomous vehicle compute, software implemented by an autonomous vehicle compute, and/or the like. In some embodiments, remote AV system114maintains (e.g., updates and/or replaces) such components and/or software during the lifetime of the vehicle.

Fleet management system116includes at least one device configured to be in communication with vehicles102, V2I device110, remote AV system114, and/or V2I infrastructure system118. In an example, fleet management system116includes a server, a group of servers, and/or other like devices. In some embodiments, fleet management system116is associated with a ridesharing company (e.g., an organization that controls operation of multiple vehicles (e.g., vehicles that include autonomous systems and/or vehicles that do not include autonomous systems) and/or the like).

In some embodiments, V2I system118includes at least one device configured to be in communication with vehicles102, V2I device110, remote AV system114, and/or fleet management system116via network112. In some examples, V2I system118is configured to be in communication with V2I device110via a connection different from network112. In some embodiments, V2I system118includes a server, a group of servers, and/or other like devices. In some embodiments, V2I system118is associated with a municipality or a private institution (e.g., a private institution that maintains V2I device110and/or the like).

The number and arrangement of elements illustrated inFIG.1are provided as an example. There can be additional elements, fewer elements, different elements, and/or differently arranged elements, than those illustrated inFIG.1. Additionally, or alternatively, at least one element of environment100can perform one or more functions described as being performed by at least one different element ofFIG.1. Additionally, or alternatively, at least one set of elements of environment100can perform one or more functions described as being performed by at least one different set of elements of environment100. In some embodiments, a goal determination system505can be included in the environment100. The object tracking system505can be configured within a vehicle102or external to a vehicle102. In some embodiments, first portions of the object tracking system505can be configured within a vehicle102and second portions of the object tracking system505can be configured external to a vehicle102.

Referring now toFIG.2, vehicle200includes autonomous system202, powertrain control system204, steering control system206, and brake system208. In some embodiments, vehicle200is the same as or similar to vehicle102(seeFIG.1). In some embodiments, vehicle200can correspond to any one of vehicles102. In some embodiments, vehicles102have autonomous capability (e.g., implement at least one function, feature, device, and/or the like that enable vehicle200to be partially or fully operated without human intervention including, without limitation, fully autonomous vehicles (e.g., vehicles that forego reliance on human intervention), highly autonomous vehicles (e.g., vehicles that forego reliance on human intervention in certain situations), and/or the like). 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, which is incorporated by reference in its entirety. In some embodiments, vehicle200is associated with an autonomous fleet manager and/or a ridesharing company.

Autonomous system202includes a sensor suite that includes one or more devices such as cameras202a, LiDAR sensors202b, radar sensors202c, and microphones202d. In some embodiments, autonomous system202can include more or fewer devices and/or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), odometry sensors that generate data associated with an indication of a distance that vehicle200has traveled, and/or the like). In some embodiments, autonomous system202uses the one or more devices included in autonomous system202to generate data associated with environment100, described herein. The data generated by the one or more devices of autonomous system202can be used by one or more systems described herein to observe the environment (e.g., environment100) in which vehicle200is located. In some embodiments, autonomous system202includes communication device202e, autonomous vehicle compute202f, and drive-by-wire (DBW) system202h.

Cameras202ainclude at least one device configured to be in communication with communication device202e, autonomous vehicle compute202f, and/or safety controller202gvia a bus (e.g., a bus that is the same as or similar to bus302ofFIG.3). Cameras202ainclude at least one camera (e.g., a digital camera using a light sensor such as a charge-coupled device (CCD), a thermal camera, an infrared (IR) camera, an event camera, and/or the like) to capture images including physical objects (e.g., cars, buses, curbs, people, and/or the like). In some embodiments, camera202agenerates camera data as output. In some examples, camera202agenerates camera data that includes image data associated with an image. In this example, the image data may specify at least one parameter (e.g., image characteristics such as exposure, brightness, etc., an image timestamp, and/or the like) corresponding to the image. In such an example, the image may be in a format (e.g., RAW, JPEG, PNG, and/or the like). In some embodiments, camera202aincludes a plurality of independent cameras configured on (e.g., positioned on) a vehicle to capture images for the purpose of stereopsis (stereo vision). In some examples, camera202aincludes a plurality of cameras that generate image data and transmit the image data to autonomous vehicle compute202fand/or a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system116ofFIG.1). In such an example, autonomous vehicle compute202fdetermines depth to one or more objects in a field of view of at least two cameras of the plurality of cameras based on the image data from the at least two cameras. In some embodiments, cameras202ais configured to capture images of objects within a distance from cameras202a(e.g., up to 100 meters, up to a kilometer, and/or the like). Accordingly, cameras202ainclude features such as sensors and lenses that are optimized for perceiving objects that are at one or more distances from cameras202a.

In an embodiment, camera202aincludes 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, camera202agenerates traffic light data associated with one or more images. In some examples, camera202agenerates TLD data associated with one or more images that include a format (e.g., RAW, JPEG, PNG, and/or the like). In some embodiments, camera202athat generates TLD data differs from other systems described herein incorporating cameras in that camera202acan include one or more cameras with a wide field of view (e.g., a wide-angle lens, a fish-eye lens, a lens having a viewing angle of approximately 120 degrees or more, and/or the like) to generate images about as many physical objects as possible.

Laser Detection and Ranging (LiDAR) sensors202binclude at least one device configured to be in communication with communication device202e, autonomous vehicle compute202f, and/or safety controller202gvia a bus (e.g., a bus that is the same as or similar to bus302ofFIG.3). LiDAR sensors202binclude a system configured to transmit light from a light emitter (e.g., a laser transmitter). Light emitted by LiDAR sensors202binclude light (e.g., infrared light and/or the like) that is outside of the visible spectrum. In some embodiments, during operation, light emitted by LiDAR sensors202bencounters a physical object (e.g., a vehicle) and is reflected back to LiDAR sensors202b. In some embodiments, the light emitted by LiDAR sensors202bdoes not penetrate the physical objects that the light encounters. LiDAR sensors202balso include at least one light detector which detects the light that was emitted from the light emitter after the light encounters a physical object. In some embodiments, at least one data processing system associated with LiDAR sensors202bgenerates an image (e.g., a point cloud, a combined point cloud, and/or the like) representing the objects included in a field of view of LiDAR sensors202b. In some examples, the at least one data processing system associated with LiDAR sensor202bgenerates an image that represents the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object, and/or the like. In such an example, the image is used to determine the boundaries of physical objects in the field of view of LiDAR sensors202b.

Radio Detection and Ranging (radar) sensors202cinclude at least one device configured to be in communication with communication device202e, autonomous vehicle compute202f, and/or safety controller202gvia a bus (e.g., a bus that is the same as or similar to bus302ofFIG.3). Radar sensors202cinclude a system configured to transmit radio waves (either pulsed or continuously). The radio waves transmitted by radar sensors202cinclude radio waves that are within a predetermined spectrum In some embodiments, during operation, radio waves transmitted by radar sensors202cencounter a physical object and are reflected back to radar sensors202c. In some embodiments, the radio waves transmitted by radar sensors202care not reflected by some objects. In some embodiments, at least one data processing system associated with radar sensors202cgenerates signals representing the objects included in a field of view of radar sensors202c. For example, the at least one data processing system associated with radar sensor202cgenerates an image that represents the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object, and/or the like. In some examples, the image is used to determine the boundaries of physical objects in the field of view of radar sensors202c.

Microphones202dincludes at least one device configured to be in communication with communication device202e, autonomous vehicle compute202f, and/or safety controller202gvia a bus (e.g., a bus that is the same as or similar to bus302ofFIG.3). Microphones202dinclude one or more microphones (e.g., array microphones, external microphones, and/or the like) that capture audio signals and generate data associated with (e.g., representing) the audio signals. In some examples, microphones202dinclude transducer devices and/or like devices. In some embodiments, one or more systems described herein can receive the data generated by microphones202dand determine a position of an object relative to vehicle200(e.g., a distance and/or the like) based on the audio signals associated with the data.

Communication device202einclude at least one device configured to be in communication with cameras202a, LiDAR sensors202b, radar sensors202c, microphones202d, autonomous vehicle compute202f, safety controller202g, and/or DBW system202h. For example, communication device202emay include a device that is the same as or similar to communication interface314ofFIG.3. In some embodiments, communication device202eincludes a vehicle-to-vehicle (V2V) communication device (e.g., a device that enables wireless communication of data between vehicles).

Autonomous vehicle compute202finclude at least one device configured to be in communication with cameras202a, LiDAR sensors202b, radar sensors202c, microphones202d, communication device202e, safety controller202g, and/or DBW system202h. In some examples, autonomous vehicle compute202fincludes a device such as a client device, a mobile device (e.g., a cellular telephone, a tablet, and/or the like) a server (e.g., a computing device including one or more central processing units, graphical processing units, and/or the like), and/or the like. In some embodiments, autonomous vehicle compute202fis the same as or similar to autonomous vehicle compute400, described herein. Additionally, or alternatively, in some embodiments autonomous vehicle compute202fis configured to be in communication with an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system114ofFIG.1), a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system116ofFIG.1), a V2I device (e.g., a V2I device that is the same as or similar to V2I device110ofFIG.1), and/or a V2I system (e.g., a V2I system that is the same as or similar to V2I system118ofFIG.1).

Safety controller202gincludes at least one device configured to be in communication with cameras202a, LiDAR sensors202b, radar sensors202c, microphones202d, communication device202e, autonomous vehicle computer202f, and/or DBW system202h. In some examples, safety controller202gincludes one or more controllers (electrical controllers, electromechanical controllers, and/or the like) that are configured to generate and/or transmit control signals to operate one or more devices of vehicle200(e.g., powertrain control system204, steering control system206, brake system208, and/or the like). In some embodiments, safety controller202gis configured to generate control signals that take precedence over (e.g., overrides) control signals generated and/or transmitted by autonomous vehicle compute202f.

DBW system202hincludes at least one device configured to be in communication with communication device202eand/or autonomous vehicle compute202f. In some examples, DBW system202hincludes one or more controllers (e.g., electrical controllers, electromechanical controllers, and/or the like) that are configured to generate and/or transmit control signals to operate one or more devices of vehicle200(e.g., powertrain control system204, steering control system206, brake system208, and/or the like). Additionally, or alternatively, the one or more controllers of DBW system202hare configured to generate and/or transmit control signals to operate at least one different device (e.g., a turn signal, headlights, door locks, windshield wipers, and/or the like) of vehicle200.

Powertrain control system204includes at least one device configured to be in communication with DBW system202h. In some examples, powertrain control system204includes at least one controller, actuator, and/or the like. In some embodiments, powertrain control system204receives control signals from DBW system202hand powertrain control system204causes vehicle200to start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate in a direction, decelerate in a direction, perform a left turn, perform a right turn, and/or the like. In an example, powertrain control system204causes the energy (e.g., fuel, electricity, and/or the like) provided to a motor of the vehicle to increase, remain the same, or decrease, thereby causing at least one wheel of vehicle200to rotate or not rotate.

Steering control system206includes at least one device configured to rotate one or more wheels of vehicle200. In some examples, steering control system206includes at least one controller, actuator, and/or the like. In some embodiments, steering control system206causes the front two wheels and/or the rear two wheels of vehicle200to rotate to the left or right to cause vehicle200to turn to the left or right.

Brake system208includes at least one device configured to actuate one or more brakes to cause vehicle200to reduce speed and/or remain stationary. In some examples, brake system208includes at least one controller and/or actuator that is configured to cause one or more calipers associated with one or more wheels of vehicle200to close on a corresponding rotor of vehicle200. Additionally, or alternatively, in some examples brake system208includes an automatic emergency braking (AEB) system, a regenerative braking system, and/or the like.

In some embodiments, vehicle200includes at least one platform sensor (not explicitly illustrated) that measures or infers properties of a state or a condition of vehicle200. In some examples, vehicle200includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), a wheel speed sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, a steering angle sensor, and/or the like.

Referring now toFIG.3, illustrated is a schematic diagram of a device300. As illustrated, device300includes processor304, memory306, storage component308, input interface310, output interface312, communication interface314, and bus302. In some embodiments, device300corresponds to at least one device of vehicles102(e.g., at least one device of a system of vehicles102), at least one device of the object tracking system505, and/or one or more devices of network112(e.g., one or more devices of a system of network112). In some embodiments, one or more devices of vehicles102(e.g., one or more devices of a system of vehicles102), one or more devices of the object tracking system505, and/or one or more devices of network112(e.g., one or more devices of a system of network112) include at least one device300and/or at least one component of device300. As shown inFIG.3, device300includes bus302, processor304, memory306, storage component308, input interface310, output interface312, and communication interface314.

Bus302includes a component that permits communication among the components of device300. In some embodiments, processor304is implemented in hardware, software, or a combination of hardware and software. In some examples, processor304includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), and/or the like), a microphone, a digital signal processor (DSP), and/or any processing component (e.g., a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), and/or the like) that can be programmed to perform at least one function. Memory306includes random access memory (RAM), read-only memory (ROM), and/or another type of dynamic and/or static storage device (e.g., flash memory, magnetic memory, optical memory, and/or the like) that stores data and/or instructions for use by processor304.

Storage component308stores data and/or software related to the operation and use of device300. In some examples, storage component308includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, a solid state disk, and/or the like), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, a CD-ROM, RAM, PROM, EPROM, FLASH-EPROM, NV-RAM, and/or another type of computer readable medium, along with a corresponding drive.

Input interface310includes a component that permits device300to receive information, such as via user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, a camera, and/or the like). Additionally or alternatively, in some embodiments input interface310includes a sensor that senses information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, an actuator, and/or the like). Output interface312includes a component that provides output information from device300(e.g., a display, a speaker, one or more light-emitting diodes (LEDs), and/or the like).

In some embodiments, communication interface314includes a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, and/or the like) that permits device300to communicate with other devices via a wired connection, a wireless connection, or a combination of wired and wireless connections. In some examples, communication interface314permits device300to receive information from another device and/or provide information to another device. In some examples, communication interface314includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a WiFi® interface, a cellular network interface, and/or the like.

In some embodiments, device300performs one or more processes described herein. Device300performs these processes based on processor304executing software instructions stored by a computer-readable medium, such as memory305and/or storage component308. A computer-readable medium (e.g., a non-transitory computer readable medium) is defined herein as a non-transitory memory device. A non-transitory memory device includes memory space located inside a single physical storage device or memory space spread across multiple physical storage devices.

In some embodiments, software instructions are read into memory306and/or storage component308from another computer-readable medium or from another device via communication interface314. When executed, software instructions stored in memory306and/or storage component308cause processor304to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry is used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments described herein are not limited to any specific combination of hardware circuitry and software unless explicitly stated otherwise.

Memory306and/or storage component308includes data storage or at least one data structure (e.g., a database and/or the like). Device300is capable of receiving information from, storing information in, communicating information to, or searching information stored in the data storage or the at least one data structure in memory306or storage component308. In some examples, the information includes network data, input data, output data, or any combination thereof.

In some embodiments, device300is configured to execute software instructions that are either stored in memory306and/or in the memory of another device (e.g., another device that is the same as or similar to device300). As used herein, the term “module” refers to at least one instruction stored in memory306and/or in the memory of another device that, when executed by processor304and/or by a processor of another device (e.g., another device that is the same as or similar to device300) cause device300(e.g., at least one component of device300) to perform one or more processes described herein. In some embodiments, a module is implemented in software, firmware, hardware, and/or the like.

The number and arrangement of components illustrated inFIG.3are provided as an example. In some embodiments, device300can include additional components, fewer components, different components, or differently arranged components than those illustrated inFIG.3. Additionally or alternatively, a set of components (e.g., one or more components) of device300can perform one or more functions described as being performed by another component or another set of components of device300.

Referring now toFIG.4, illustrated is an example block diagram of an autonomous vehicle compute400(sometimes referred to as an “AV stack”). As illustrated, autonomous vehicle compute400includes perception system402(sometimes referred to as a perception module), planning system404(sometimes referred to as a planning module), localization system406(sometimes referred to as a localization module), control system408(sometimes referred to as a control module), and database410. In some embodiments, perception system402, planning system404, localization system406, control system408, and database410are included and/or implemented in an autonomous navigation system of a vehicle (e.g., autonomous vehicle compute202fof vehicle200). Additionally, or alternatively, in some embodiments perception system402, planning system404, localization system406, control system408, and database410are included in one or more standalone systems (e.g., one or more systems that are the same as or similar to autonomous vehicle compute400and/or the like). In some examples, perception system402, planning system404, localization system406, control system408, and database410are included in one or more standalone systems that are located in a vehicle and/or at least one remote system as described herein. In some embodiments, any and/or all of the systems included in autonomous vehicle compute400are implemented in software (e.g., in software instructions stored in memory), computer hardware (e.g., by microprocessors, microcontrollers, application-specific integrated circuits [ASICs], Field Programmable Gate Arrays (FPGAs), and/or the like), or combinations of computer software and computer hardware. It will also be understood that, in some embodiments, autonomous vehicle compute400is configured to be in communication with a remote system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system114, a fleet management system116that is the same as or similar to fleet management system116, a V2I system that is the same as or similar to V2I system118, and/or the like).

In some embodiments, perception system402receives data associated with at least one physical object (e.g., data that is used by perception system402to detect the at least one physical object) in an environment and classifies the at least one physical object. In some examples, perception system402receives image data captured by at least one camera (e.g., cameras202a), the image associated with (e.g., representing) one or more physical objects within a field of view of the at least one camera. In such an example, perception system402classifies at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, pedestrians, and/or the like). In some embodiments, perception system402transmits data associated with the classification of the physical objects to planning system404based on perception system402classifying the physical objects.

In some embodiments, planning system404receives data associated with a destination and generates data associated with at least one route (e.g., routes106) along which a vehicle (e.g., vehicles102) can travel along toward a destination. In some embodiments, planning system404periodically or continuously receives data from perception system402(e.g., data associated with the classification of physical objects, described above) and planning system404updates the at least one trajectory or generates at least one different trajectory based on the data generated by perception system402. In some embodiments, planning system404receives data associated with an updated position of a vehicle (e.g., vehicles102) from localization system406and planning system404updates the at least one trajectory or generates at least one different trajectory based on the data generated by localization system406.

In some embodiments, localization system406receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicles102) in an area. In some examples, localization system406receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensors202b). In certain examples, localization system406receives data associated with at least one point cloud from multiple LiDAR sensors and localization system406generates a combined point cloud based on each of the point clouds. In these examples, localization system406compares the at least one point cloud or the combined point cloud to two-dimensional (2D) and/or a three-dimensional (3D) map of the area stored in database410. Localization system406then determines the position of the vehicle in the area based on localization system406comparing the at least one point cloud or the combined point cloud to the map. In some embodiments, the map includes a combined point cloud of the area generated prior to navigation of the vehicle. In some embodiments, maps include, without limitation, high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations thereof), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types. In some embodiments, the map is generated in real-time based on the data received by the perception system.

In another example, localization system406receives Global Navigation Satellite System (GNSS) data generated by a global positioning system (GPS) receiver. In some examples, localization system406receives GNSS data associated with the location of the vehicle in the area and localization system406determines a latitude and longitude of the vehicle in the area. In such an example, localization system406determines the position of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, localization system406generates data associated with the position of the vehicle. In some examples, localization system406generates data associated with the position of the vehicle based on localization system406determining the position of the vehicle. In such an example, the data associated with the position of the vehicle includes data associated with one or more semantic properties corresponding to the position of the vehicle.

In some embodiments, control system408receives data associated with at least one trajectory from planning system404and control system408controls operation of the vehicle. In some examples, control system408receives data associated with at least one trajectory from planning system404and control system408controls operation of the vehicle by generating and transmitting control signals to cause a powertrain control system (e.g., DBW system202h, powertrain control system204, and/or the like), a steering control system (e.g., steering control system206), and/or a brake system (e.g., brake system208) to operate. In an example, where a trajectory includes a left turn, control system408transmits a control signal to cause steering control system206to adjust a steering angle of vehicle200, thereby causing vehicle200to turn left. Additionally, or alternatively, control system408generates and transmits control signals to cause other devices (e.g., headlights, turn signal, door locks, windshield wipers, and/or the like) of vehicle200to change states.

In some embodiments, perception system402, planning system404, localization system406, and/or control system408implement 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, at least one transformer, and/or the like). In some examples, perception system402, planning system404, localization system406, control system408, and/or the object tracking system505implement at least one machine learning model alone or in combination with one or more of the above-noted systems. In some examples, perception system402, planning system404, localization system406, control system408, and/or the object tracking system505implement 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 and/or the like). An example of an implementation of a machine learning model is included below with respect toFIG.4B.

Database410stores data that is transmitted to, received from, and/or updated by perception system402, planning system404, localization system406and/or control system408. In some examples, database410includes a storage component (e.g., a storage component that is the same as or similar to storage component308ofFIG.3) that stores data and/or software related to the operation and uses at least one system of autonomous vehicle compute400. In some embodiments, database410stores data associated with 2D and/or 3D maps of at least one area. In some examples, database410stores data associated with 2D and/or 3D maps of a portion of a city, multiple portions of multiple cities, multiple cities, a county, a state, a State (e.g., a country), and/or the like). In such an example, a vehicle (e.g., a vehicle that is the same as or similar to vehicles102and/or vehicle200) can drive along one or more drivable regions (e.g., single-lane roads, multi-lane roads, highways, back roads, off road trails, and/or the like) and cause at least one LiDAR sensor (e.g., a LiDAR sensor that is the same as or similar to LiDAR sensors202b) to generate data associated with an image representing the objects included in a field of view of the at least one LiDAR sensor.

In some embodiments, database410can be implemented across a plurality of devices. In some examples, database410is included in a vehicle (e.g., a vehicle that is the same as or similar to vehicles102and/or vehicle200), an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system114, a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system116ofFIG.1, a V2I system (e.g., a V2I system that is the same as or similar to V2I system118ofFIG.1) and/or the like.

Referring now toFIG.4B, illustrated is a diagram of an implementation of a machine learning model. More specifically, illustrated is a diagram of an implementation of a convolutional neural network (CNN)420. For purposes of illustration, the following description of CNN420will be with respect to an implementation of CNN420by perception system402. However, it will be understood that in some examples CNN420(e.g., one or more components of CNN420) is implemented by other systems different from, or in addition to, perception system402such as planning system404, localization system406, control system408, and/or the object tracking system505. While CNN420includes certain features as described herein, these features are provided for the purpose of illustration and are not intended to limit the present disclosure.

CNN420includes a plurality of convolution layers including first convolution layer422, second convolution layer424, and convolution layer426. In some embodiments, CNN420includes sub-sampling layer428(sometimes referred to as a pooling layer). In some embodiments, sub-sampling layer428and/or other subsampling layers have a dimension (i.e., an amount of nodes) that is less than a dimension of an upstream system. By virtue of sub-sampling layer428having a dimension that is less than a dimension of an upstream layer, CNN420consolidates the amount of data associated with the initial input and/or the output of an upstream layer to thereby decrease the amount of computations necessary for CNN420to perform downstream convolution operations. Additionally, or alternatively, by virtue of sub-sampling layer428being associated with (e.g., configured to perform) at least one subsampling function (as described below with respect toFIGS.4C and4D), CNN420consolidates the amount of data associated with the initial input.

Perception system402performs convolution operations based on perception system402providing respective inputs and/or outputs associated with each of first convolution layer422, second convolution layer424, and convolution layer426to generate respective outputs. In some examples, perception system402implements CNN420based on perception system402providing data as input to first convolution layer422, second convolution layer424, and convolution layer426. In such an example, perception system402provides the data as input to first convolution layer422, second convolution layer424, and convolution layer426based on perception system402receiving data from one or more different systems (e.g., one or more systems of a vehicle that is the same as or similar to vehicle102), a remote AV system that is the same as or similar to remote AV system114, a fleet management system that is the same as or similar to fleet management system116, a V2I system that is the same as or similar to V2I system118, and/or the like). A detailed description of convolution operations is included below with respect toFIG.4C.

In some embodiments, perception system402provides data associated with an input (referred to as an initial input) to first convolution layer422and perception system402generates data associated with an output using first convolution layer422. In some embodiments, perception system402provides an output generated by a convolution layer as input to a different convolution layer. For example, perception system402provides the output of first convolution layer422as input to sub-sampling layer428, second convolution layer424, and/or convolution layer426. In such an example, first convolution layer422is referred to as an upstream layer and sub-sampling layer428, second convolution layer424, and/or convolution layer426are referred to as downstream layers. Similarly, in some embodiments perception system402provides the output of sub-sampling layer428to second convolution layer424and/or convolution layer426and, in this example, sub-sampling layer428would be referred to as an upstream layer and second convolution layer424and/or convolution layer426would be referred to as downstream layers.

In some embodiments, perception system402processes the data associated with the input provided to CNN420before perception system402provides the input to CNN420. For example, perception system402processes the data associated with the input provided to CNN420based on perception system420normalizing sensor data (e.g., image data, LiDAR data, radar data, and/or the like).

In some embodiments, CNN420generates an output based on perception system420performing convolution operations associated with each convolution layer. In some examples, CNN420generates an output based on perception system420performing convolution operations associated with each convolution layer and an initial input. In some embodiments, perception system402generates the output and provides the output as fully connected layer430. In some examples, perception system402provides the output of convolution layer426as fully connected layer430, where fully connected layer420includes data associated with a plurality of feature values referred to as F1, F2 . . . FN. In this example, the output of convolution layer426includes data associated with a plurality of output feature values that represent a prediction.

In some embodiments, perception system402identifies a prediction from among a plurality of predictions based on perception system402identifying a feature value that is associated with the highest likelihood of being the correct prediction from among the plurality of predictions. For example, where fully connected layer430includes feature values F1, F2, . . . FN, and F1 is the greatest feature value, perception system402identifies the prediction associated with F1 as being the correct prediction from among the plurality of predictions. In some embodiments, perception system402trains CNN420to generate the prediction. In some examples, perception system402trains CNN420to generate the prediction based on perception system402providing training data associated with the prediction to CNN420.

As shown inFIG.5, the implementation500includes an object tracking system505. The object tracking system505includes an object tracker510configured to acquire and process sensor data from one or more sensors202affixed to the vehicle. In some embodiments, the sensor data is used to determine and provide trajectories of objects within an environment in which the vehicle is co-located. Based on the received sensor data, the object tracker510determines hypothetical object trajectories515, which are provided to the planning system404for use in autonomously navigating the vehicle with respect to the detected object. For example, the trajectory of the vehicle is determined by the planning system404so as to avoid or otherwise navigate away from or with respect to the object trajectories515determined by the object tracking system505.

Referring now toFIG.6, illustrated is a diagram of a detailed implementation of the object tracking system505ofFIG.5. As shown inFIG.6, the object tracking system505includes an object tracker510. The object tracker510receives sensor data520from any one of sensors202. The sensor data520is processed by a graph builder525to form a data structure of nodes and edges corresponding to one or more detected objects associated with the sensor data520. In some embodiments, the data structure is a graph. A node of the data structure corresponds to a state of a detected object at a particular time, such as a velocity, a heading, or a physical appearance of the object. An edge connecting two nodes corresponds to a hypothetical transition in state of the object between a first instance of time and second instance of time.

For example, a data structure is formed based on sensor data associated with a pedestrian walking in proximity of an intersection the vehicle is approaching. A node is associated with the speed, direction, and appearance of the pedestrian. An edge is associated with a hypothetical change in the pedestrian's speed, direction, or appearance. For example, a hypothetical change in the pedestrian's direction can be associated with the pedestrians continued travel outside of the intersection or the pedestrian's change in travel into the intersection.

The graph builder525implements a processing window parameter configured to build the graph in relation to sensor data520that is associated with a user-defined amount of time. For example, the processing window parameter is configured as 1 or 2 seconds in length and the graph builder constructs the data structure based on sensor data acquired over the previous 1 or 2 seconds. Advantageously, the use of a processing window parameter corrects for previously erroneous states or state transitions because previous incorrect state or state transition determinations do not incorrectly influence a subsequent state or state transition determination. This enables the graph builder525to operate with conditional independence such that a current state or state transition is determined based on a partial past history (e.g., corresponding to a value of the processing window parameter) and not a longer history for which the sensor data520was 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.

As new sensor data520is acquired, it can be provided to the graph builder525and a new data structure, e.g., a new graph, can be formed. Thus, as new detection instances are captured by the sensor data520, new nodes and new edges are determined and configured in the data structure so as to update the graph in a streaming manner.

In some embodiments, the sensor data520includes data from different sensing modalities, such as data from cameras202a, LiDAR sensors202b, radar sensors202c, microphones202d, or a combination thereof. As result, processing the sensor data520from different modalities creates out-of-sequence sensor data that is not temporally consistent from a first time instance to a subsequent time instance. To address this condition the graph builder525is configured to detect sensor data associated with an earlier graph state as compared to an initial or starting graph state. Responsive to the detecting, the graph builder525determines a new node position of the object and inserts a new node into the determined new node position. The graph builder525then reorders and sort the graph based on the new node position.

The data structure or graph built by the graph builder525is provided to the graph element generator530. The graph element generator is configured with a predictive model, such as CNN420, configured to receive the state (e.g., at least one of a velocity, a heading, and/or a physical appearance of the detected object) as an input and to generate as an output a set of at least one node and at least one edge associated with the object for which the input of state was received. The predictive model is configured to solve the output by linear assignment to minimize costs. The cost is considered to be the measured distance between the state and transition of state at a past time as compared to a current time. For example, the measured distance between states can be associated with a magnitude of changes in the speed, heading, and/or appearance of the sensed object. Thus, a greater cost is indicative of larger changes in the object's state and permitting larger costs to persist can lead to inaccurate predicted object state transitions. Jointly solving the assignment for all object detections at any point in time minimizes the distance for all object detections and thus, output the new state and hypothetical new state more robustly and more accurately. In some embodiments, the graph element generator530is tuned using different motion models.

As the graph element generator530identifies new nodes and edges based on the state of the detected object a trajectory of the object is formed as a best estimate of the object in time. In some embodiments, the newly identified nodes and edges are provided to a smoothing function improve state accuracy and state transition accuracy. For example, in some embodiments, the smoothing function includes a Kalman filter. In some embodiments, the Kalman filter is applied to output nodes and edges associated with new state and state transitions of a detected moving object. In some embodiments, the smoothing function includes a Kalman filter in conjunction with a static object motion model to output nodes and edges associated with new state and state transitions of a detected static object. A trajectory515is formed based on tracks associated with the incremental changes in nodes (e.g., object state) and edges (e.g., hypothetical changes in state of the object) as the object is detected by the sensors through time.

The trajectory515is provided to the vehicle planning system404and the vehicle is operated to navigate to the vehicle in relation to the trajectories515corresponding to objects104.

Referring now toFIG.7, illustrated is a flowchart of a process700for object tracking using the object tracking system described herein. In some embodiments, one or more of the steps described with respect to process700are performed (e.g., completely, partially, and/or the like) by object tracking system505. Additionally, or alternatively, in some embodiments one or more steps described with respect to process700are performed (e.g., completely, partially, and/or the like) by another device or group of devices separate from or including object tracking system505such as perception system402, planning system404, localization system406, and/or control system408.

At702, the process includes receiving from at least two sensor systems of a vehicle data characterizing respective detected objects. The data includes sensor data received from at least two sensors configured in the autonomous system202and associated with at least one object104. For example, the data is received from at least one of camera202a, LiDAR sensor202b, radar sensor202c, or microphone202d, or a combination thereof. In some embodiments, the object104can be a stationary object or a moving object.

At704, the process incudes generating a data structure based on the data characterizing the respective detected objects. The data structure includes a graph of nodes and edges connecting the nodes. A node represents a state of one of the respective detected objects at a particular time. The state can include a position, a velocity, an acceleration, a heading, a heading rate (e.g., an angular velocity), an existence probability, a physical appearance and/or a class of the object. An edge represents a hypothetical transition of the state of the one of the respective detected objects. Edges are considered as valid or invalid state transitions. Invalid state transitions can be determined based on spatiotemporal constraints. For example, state transitions within the same time frame can be invalid because different nodes cannot belong to the same object at the same time. Additionally, temporal constraints can be configured such that a node cannot have an edge to another node in a future time frame if the position of the object is outside of (e.g., too far away) the physically possible range for the node of the earlier time frame to travel to. For example, an edge connecting a first node associated with a state of a first object is an invalid edge, if the edge connects the first node to a second node associated with a state of a second object, e.g., an object that is different than the object associated with the first node and thus two different objects have been detected and misidentified as the same object. An edge is considered a valid edge when it connects nodes associated with the same object and thus represents transitions of a first object from a first state to a second data of the first object. For example, a valid transition state can include an optimal transition state determined using a linear assignment solver configured to generate the graph of nodes and edges at704. Costs for each edge are computed. Each edge cost can represent a likelihood that a later node continues from an earlier node, and thus represents the same underlying object. Based on the constraint that a single node can continue forward in time by at most one node (e.g., the same object cannot split into multiple objects over time), the solver can be configured to solve the graph as a bipartite matching problem to minimize the total likelihood of state transitions among all nodes in the graph.

In some embodiments, the hypothetical transition of the state of the 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.

At706, the process includes applying a predictive model to the data structure. The predictive model is trained in a machine learning process as described herein to receive the velocity, 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 the one of the respective detected objects. The predictive model can be trained using 3D annotations to receive multiple frames of Lidar point cloud data and camera data and to output 3D object detection with associated object states (e.g., velocity, heading, acceleration of a detected object), object appearance (e.g., the object's shape and/or features), as well as a classification of a type of object (e.g., a vehicle, a bicycle, or a pedestrian).

The predictive model identifies node and edges which can correspond to states of the same object that was detected by the sensors202. The predictive model identifies an edge to connect nodes associated with the object detected by the sensors202, e.g., different instances in time of the same object. The output of the predictive model, e.g., the identification of a set of nodes and edges, corresponds to a hypothetical trajectory to be performed by the one of the respective detected objects. For example, the identification includes at least one of a velocity, a heading, and/or a physical appearance of the detected object.

In some embodiments, instances of sensor data associated with particular times is acquired, stored, or otherwise received and indexed according to the particular time. In this way, the predictive model is utilized the indexed sensor data to predict edges as hypothetical transitions in state of the object between two particular times.

In some embodiments, producing the identification of the 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. For example, a Kalman filter can be applied to improve the accuracy of the state determinations.

At708, the process includes providing data based on the identification of the set of nodes and edges to a planning system of the vehicle, such as the planning system404. In some embodiments, the generating, applying, and providing are performed continuously as data characterizing the respective detected objects is received. For example, every instance of received sensor data causes the generating, applying, and providing operations to be performed dynamically and continuously so that predicted trajectories of detected objects are constantly being determined and incorporated by the planning system404.

At710, the process includes causing the vehicle102to operate based on providing the data to the planning system404of the vehicle.

Referring now toFIG.8, a graph800includes six nodes, e.g., nodes 1-6. The nodes are respectively associated with a state of at least one detected object and the edges connecting pairs of nodes are associated with valid hypothetical transitions of the objects velocity, heading, or physical appearance. For example, the edges can represent a respective valid hypothetical transition in state of an object that is located in proximity of the vehicle and for which the sensors of the vehicle have acquired sensor data as described herein. The graph800corresponds to a plurality of tracklets for a particular object. A tracklet can be associated with or defined as at least one edge and can include a plurality of edges as described herein. A tracklet is one or more hypothetical transitions in state that have been determined to be valid transitions in state for the respective detected object. In some embodiments, a tracklet can include a short sequence of measurements determined to have originated from the same target or object. The sequence of measurements can be filtered to ensure estimations include a state with a minimal amount of covariance.

The valid tracklets can be fused or stitched together to form a trajectory of the object. Each node in a graph can include a tracklet because each node already contains all the states (e.g., a position, a velocity, a heading) of the object. In some embodiments, the stitching can be associated with or defined as joining two or more valid tracklets. For example, as shown inFIG.8, the state of a first object at an initial time (e.g., T=0) is identified by node 1 and the state of a second object at T=0 is identified by node 2. Based on the received sensor data, the object tracking system505generates a data structure800that includes a first set of hypothetical transitions for nodes 1 and 2 at future times T=1 and T=2. The data structure800is generated to include valid hypothetical state transitions as edges for future times T=1 and T=2, such as edges 1-10. The object tracking system505can determine multiple possible solutions. For example, node 1, node 4, and node 5 via edges 1 and 10 can represent one valid tracklet solution. Node 2, node 3, and node 6 via edges 6 and 7 can represent a second valid tracklet solution. The object tracking system505can further determine, based on one or more cost parameter settings, that the optimized solution or tracklet for each object considering all hypothetical state transitions jointly is one in which 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.

The techniques for object tracking described herein can provide technical solutions, which can provide technical advantages over existing object tracking systems. The advantages can include, but are not limited to, increased processing times and accuracy for object determination in autonomous vehicle operating environments. The object tracking systems described herein can also provide improved detection of moving objects compared to static objects in either sparse or dense operating environments. As a result, more accurate object detection data can be provided to the planning system of the vehicle and the vehicle can be operate more safely in a larger variety of operating conditions in which the objects can be present.

In the foregoing description, aspects and embodiments of the present disclosure have been described with reference to numerous specific details that can vary from implementation to implementation. Accordingly, the description and drawings are to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Any definitions expressly set forth herein for terms contained in such claims shall govern the meaning of such terms as used in the claims. In addition, when we use the term “further comprising,” in the foregoing description or following claims, what follows this phrase can be an additional step or entity, or a sub-step/sub-entity of a previously-recited step or entity.