Patent ID: 12217492

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.

The terminology used in the description of the various described embodiments herein is included for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well and can be used interchangeably with “one or more” 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 encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this description specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

As used herein, the terms “communication” and “communicate” refer to at least one of the reception, receipt, transmission, transfer, provision, and/or the like of information (or information represented by, for example, data, signals, messages, instructions, commands, and/or the like). For one unit (e.g., a device, a system, a component of a device or system, combinations thereof, and/or the like) to be in communication with another unit means that the one unit is able to directly or indirectly receive information from and/or send (e.g., transmit) information to the other unit. This may refer to a direct or indirect connection that is wired and/or wireless in nature. Additionally, two units may be in communication with each other even though the information transmitted may be modified, processed, relayed, and/or routed between the first and second unit. For example, a first unit may be in communication with a second unit even though the first unit passively receives information and does not actively transmit information to the second unit. As another example, a first unit may be in communication with a second unit if at least one intermediary unit (e.g., a third unit located between the first unit and the second unit) processes information received from the first unit and transmits the processed information to the second unit. In some embodiments, a message may refer to a network packet (e.g., a data packet and/or the like) that includes data.

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.

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 described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments can be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

General Overview

In some aspects and/or embodiments, systems, methods, and computer program products described herein include and/or implement track segment cleaning of data representations of tracked objects. A track segment cleaning system can identify and remove potentially erroneous track segments using a machine learning system. Track segments are data associated with perceived objects encountered by a vehicle (such as an autonomous vehicle). Track segment cleaning is a technique in which the erroneous track segments are eliminated or combined with other track segments to capture a complete tracking for a tracked object. Examples of erroneous track segments include redundant, disconnected, or false track segments resulting from poor perception of an object exterior to the vehicle or the failure to identify object movements as belonging to the same object. To eliminate the erroneous track segments, the track segment cleaning system may eliminate or combine the track segments. This elimination or combination reduces computing resources necessary to monitor objects exterior to the vehicle and ensures the planning system of the vehicle makes safe planned movements in response to various driving scenarios.

As an example technique, a track segment cleaning system detects two track segments as potential candidates to combine into a single track segment. After detecting the track segment candidates, the track segment cleaning system applies a machine learning model trained to determine whether the first track segment and the second track segment are representative of an identical object surrounding the autonomous vehicle. The track segment cleaning system may combine the first track segment and the second track segment to form a single track segment. For example, the track segment cleaning system determines the first track segment and the second track segment are to be combined based on determining that the first track segment is associated with a bicycle over a first period of time and a second track segment is associated with the same bicycle over a second period of time. The track segment cleaning system combines the respective trajectories and/or cloud-point features of the first track segment and the second track segment. The combined track segment then has a single set of trajectories and a single set of cloud-point features. Track segment cleaning increases the likelihood of accurate movement planning by the planning system of the vehicle due to removing erroneous track segments. As such, the planning system has an accurate representation of the objects surrounding the vehicle. Further, the track segment cleaning system eliminates or combines erroneous track segments, thereby reducing the computing resources necessary to run the planning system and ensuring the planning system makes safe planned movements in response to various driving scenarios.

By virtue of the implementation of systems, methods, and computer program products described herein, techniques for track segment cleaning of tracked objects. Unlike other object tracking modules in machine learning models, the track segment cleaning framework herein includes techniques for eliminating and combining erroneous track segments into a single track. Instead of potentially tracking false objects or allowing multiple track segments to represent the same object, the track segment cleaning framework eliminates redundant, disconnected, and false track segments. Eliminating these erroneous track segments reduces the computing resources needed for movement planning and increases the safety of planned movements in response to various driving scenarios. More specifically, the track segment cleaning framework can compare the trajectory features, the cloud-point features, and/or the image features of the track segments to determine which track segments are to be stitched together or otherwise eliminated. These technical improvements lead to enhanced decision making by the planning system.

Further, the track segment cleaning system solves technical problems associated with planning modules configured to make planned movements. Technical problems for planning modules include obtaining metrics for evaluating track quality to ensure the vehicle is responding to existing and new object types. For example, the planning system can make poorly planned movements when the planning module is unable to identify erroneous track segments. Without identifying evaluation metrics (e.g., track segment quality score, stitching score) for the track segments, the planning module cannot discern between false and accurate track segments, especially as the vehicle encounters new objects. As a result, the erroneous track segments can negatively affect the planning module's ability to safely navigate the autonomous vehicle.

As such, there is a need for a track segment cleaning system configured to evaluate tracked object segments to eliminate or combine erroneous track segments.

Training samples are required for training the machine learning model. Training samples are generated by adding track quality labels to the track segments generated by the perception detection and tracking system. The labels added to the track segments can be a value representative of a quality of the track sample in comparison to a ground truth track. For example, a value of ‘1’ means the training sample corresponds to a ground truth track and a value of ‘0’ means the training sample corresponds to a false track. But without a sufficient number of labeled training samples, the machine learning model cannot accurately determine the quality score of the track segments representative of the objects encountered by the vehicle. As such, a training sample generator is implemented to correct the shortage of labeled training samples.

The training sample generator produces labeled training samples for training the machine learning model. The training sample generator is configured to associate a label with a track segment to generate a training sample. The training sample generator utilizes ground truth tracks including known good representations of objects observed by the vehicle. The training sample generator compares the ground truth tracks to one or more track segments to produce training samples with an assigned label.

Additionally, the training sample generator utilizes data augmentation techniques to generate labeled training samples. For example, a training sample generator divides a single track into two or more track segments to create training samples. The training samples are associated with a label identifying the two or more track segments as belonging to a single track. For example, a label for the training samples including the two or more track segments has a ground truth stitching score of ‘1’ to indicate that the two or more track segments are from the same track. Alternatively, the training samples are associated with a label identifying the two or more track segments as belonging to separate tracks. For example, a label for the training samples including the two or more track segments belonging to separate tracks have a ground truth stitching score of ‘0’ to indicate that the two or more track segments are from different tracks. These technical improvements provide accurate training tools for the machine learning model. The trained machine learning model can evaluate the quality of track segments and evaluate segments to be stitched together based on the training from the training samples.

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.

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, vehicle102have 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), 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), 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 Wi-Fi® 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, and/or control system408implement 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, and/or control system408implement 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).

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, and/or control system408. 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 F1is the greatest feature value, perception system402identifies the prediction associated with F1as 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.

Referring now toFIG.5, illustrated is a diagram of an implementation of track segment cleaning. Track segment cleaning identifies and eliminates potentially erroneous track segments using a machine learning model. A perception system can generate track segments as representations of objects encountered by the vehicle. But sometimes these track segments are inaccurate or erroneous. Examples of erroneous track segments include redundant, disconnected, or false track segments. Track segment cleaning is a technique in which the redundant, disconnected, or false track segments are eliminated or combined with other track segments to capture a single complete and accurate tracking for a tracked object. Erroneous track segments can be the result of poor perception of an object exterior to the vehicle, poor stitching of object information across timeframes, or perception system402and/or planning system404failing to identify object movements as belonging to the same object. To correct this problem, track segment cleaning includes identifying false tracks (e.g., tracks having a below-threshold quality). Furthermore, track segment cleaning includes identifying two or more separate track segments that can be combined into a track segment.

Pre-cleaned track segments510show multiple track segments that are potentially erroneous. The pre-cleaned track segments510can be created by an auto-labeling annotator, which can be implemented by perception system402in response to detecting an object. The auto-labeling annotator is configured to associate a track segment with an object as the object is perceived by a sensor at the vehicle. As shown inFIG.5, pre-cleaning track segments510includes four track segments: a first track segment515, a second track segment525, a third track segment535, and a fourth track segment545.

First track segment515includes a first object within its frames, indicating that first track segment515is tracking the same object. The first object is included in each frame of first track segment515and the first object is not included in any other track segments. The consistency of the first object occupying only one track is indicative that a single object is correctly paired to a single track and that no further cleaning of tracks is needed to obtain a complete track segment for the first object.

Second track segment525and third track segment535depict two track segments representative of movement for the second object. The second object in second track segment525and the second object in third track segment535are the same object. But associating two separate track segments for the same object is inefficient and erroneous. Multiple track segments tracking the same object can consume excessive computer resources of planning system404and potentially cause navigation decision difficulties for planning system404, localization system406, and/or control system408. As a result, the second object in both second track segment525and third track segment535presents various inefficiencies and safety concerns. Second track segment525and Third track segment535should be combined so that the second object is represented by only one track.

Fourth track segment545is a false track. No object corresponds to fourth track segment545. That is, the false track represents no object that exists in real life. As a result, fourth track segment545complicates navigation decisions for planning system404, localization system406, and/or control system408. Fourth track segment545should be eliminated so that the false track segment does not consume excessive computer resources. For offline perception auto-labeling systems, false tracks may reduce output object annotation quality, which can reduce the machine learning based online object detection and tracking models that are trained with these auto-labeled data.

Cleaned track segments520show pre-cleaned track segments510following the track cleaning process. Cleaned track segments520illustrates objects that are represented by only one track segment. Cleaned track segments520have a 1:1 correspondence between the number of objects tracked and the number of track segments. Cleaned track segments520do not include any false track segments or segments that do not correspond to an object in real life. As shown inFIG.5, first track segment515includes a first object within its frames, which indicates that first track segment515is associated with the same object. The first object is included in each frame of first track segment515and the first object is not included in any other track segments. The consistency of the first object occupying only one track is indicative that the single object is correctly paired to a single track and that no further consolidation of tracks is needed to obtain a complete track segment for the first object.

Similarly, second track segment525includes a second object within its frames, indicating that second track segment525is tracking the same object. The second object is included in each frame of second track segment525and the second object is not included in any other track segments. The consistency of the second object occupying only one track indicates that the single object is correctly paired to a single track and that no further consolidation of tracks is needed to obtain a complete track segment for the second object. The track segment cleaning process combined third track segment535with second track segment525to obtain a complete track. Additionally, the track segment cleaning process removed fourth track segment545as no real life object corresponds to fourth track segment545. Cleaned track segments520increase the accuracy of the planned movements of planning system404, reduce processing and memory resources necessary to monitor objects exterior to the vehicle, and increase the likelihood of safe planned movements in response to various driving scenarios.

Referring now toFIG.6, illustrated is a diagram of an implementation of a track segment cleaning dataflow600. Track segment cleaning dataflow600identifies potentially redundant, disconnected, or false track segments. The track segments are representative of perceived objects encountered by the vehicle. Track segment cleaning dataflow600is configured to eliminate or combine the potentially redundant, disconnected, or false track segments. Additionally, as described herein, Track segment cleaning dataflow600applies a machine learning model trained to determine whether track segments are representative of real objects exterior to a vehicle. The trained machine learning model determines the quality of track segments and determines whether the segments are to be stitched together.

An input tracks module610is configured to receive track segments. The track segments are representative of objects perceived exterior of the vehicle. A track feature extraction net620receives the input tracks selected by the input tracks module610. The input tracks include a first track segment612and a second track segment614. Track feature extraction net620extracts features from first track segment612and second track segment614. The track segment cleaning module630determines whether the track segments are representative of the same object by comparing the features from first track segment612and second track segment614. Track segment cleaning module630applies a trained machine learning model to determine whether first track segment612and second track segment614represent the same object. A track segment cleaning module630determines whether to combine first track segment612and second track segment614and output clean tracks640. Additionally, and/or alternatively, track segment cleaning module630can determine that a track segment is to be eliminated based on a track quality segment score.

In an embodiment, track segment cleaning dataflow600receives track segments as input tracks at input tracks module610. The input tracks include track segments representative of objects tracked by perception system402and/or planning system404. For example, a bicycle exterior to the vehicle is an object tracked in perception system402. The track segments are representative of tracked objects exterior to the vehicle that are spatially monitored relative to a position of the vehicle over a period of time. The tracked objects can be detected through the autonomous system202, which can include at least one of cameras202a, LiDAR sensors,202b, radar sensors202c, and microphones202d. For example, the bicycle exterior to the vehicle is initially detected by cameras202aand subsequently monitored using LiDAR sensors202b.

In an embodiment, an auto-labeling system automatically annotates the data collected from the vehicle sensors. The auto-labeling annotator can be implemented as an offline version of perception system402. The auto-labeling annotator is configured to annotate tracked objects representative of real objects and form a track segment. For example, the auto-labeling annotator annotates first track segment612representative of a first object. In an embodiment, a first auto-labeling annotation is attached to first track segment612and a second auto-labeling annotation is attached to second track segment614. The annotated track segments can be the input tracks received at input tracks module610to begin track segment cleaning dataflow600.

Input tracks module610is configured to select two or more track segments (e.g., first track segment612and second track segment614) from a plurality of track segments that can be potentially combined. Input tracks module610can examine traits or characteristics of the plurality of track segments to determine whether two or more track segments can possibly represent the same object. For example, perception system402detects a bicycle over a first time period represented by first track segment612and perception system402detects a bicycle over a second time period represented by second track segment614. Input tracks module610determines that the two tracks can be potentially combined based on a temporal proximity of the two track segments. In another embodiment, first track segment612has a first set of timestamps and second track segment614has a second set of timestamps where at least one timestamp of the second set of timestamps is different from the timestamp in the first set of timestamps. Input tracks module610determines that the two tracks can be potentially combined based on the closest timestamps not exceeding a certain period (for example 3 seconds), and tracked object types being the same between first track segment612and second track segment614.

Additionally, input tracks module610determines whether the two tracks can be potentially combined based on a physical proximity of the object in first track segment612and the object in second track segment614. The object in first track segment612and the object in second track segment614can satisfy a distance threshold. For example, if the distance threshold of five meters is satisfied, then input tracks module610determines that first track segment612and second track segment614possibly represent the same object. In an embodiment, first track segment612and second track segment614are selected from a plurality of track segments based on satisfying a timestamp threshold, a physical proximity threshold, and/or the like.

After input tracks module610determines the track segments that could be potentially combined (i.e., track segment candidates), track segment cleaning dataflow600extracts features from the track segment candidates in track feature extraction net620. Features extracted in track feature extraction net620can include trajectory features, cloud point features, and image features. For example, track feature extraction net620extracts a first trajectory feature and a first cloud point feature from first track segment612representing the bicycle over a first period of time. Additionally, track feature extraction net620extracts a second trajectory feature and a second cloud point feature from second track segment614representing the bicycle over a second period of time.

Following the extraction process, track segment cleaning dataflow600sends the extracted features to track segment cleaning module630. The track segment cleaning module630determines whether first track segment612and second track segment614are to be combined to a single track based on the extracted features. For example, track segment cleaning module630compares the first trajectory feature and the second trajectory feature and, similarly, compares the first cloud point feature and the second point feature corresponding to the bicycle over the two time periods.

Track segment cleaning module630can apply a machine learning model trained to determine whether first track segment612and the second track segment614are representative of an identical object exterior to the vehicle based on the extracted features. The machine learning model can provide analyses for generating scores representative of the likelihood that first track segment612and second track segment614are representative of the same object exterior to the vehicle. For example, track segment cleaning module630assigns a track quality score of 0.9 to first track segment612and track segment cleaning module630assigns a track quality score of 0.87 to second track segment614. Track segment cleaning module630assigns first track segment612and second track segment614a stitching score of 0.93.

After generating the scores, track segment cleaning module630determines whether the scores satisfy a threshold. For example, if the track quality score threshold is 0.75 and the stitching score threshold is 0.8, then the score thresholds generated by the machine learning model are satisfied. If the scores satisfy the threshold, then track segment cleaning module630is configured to combine first track segment612and second track segment614into a single track segment having a single trajectory and a single cloud point feature. For example, the two track segments representing the bicycle (e.g., first track segment612and second track segment614) and all of the extracted features are combined into a single track segment. The combined track segment then has a single set of trajectories and a single set of cloud-point features.

Track segment cleaning dataflow600increases the likelihood of accurate perception of objects and their trajectories, thereby preventing erroneous planned movements by planning system404. Further, track segment cleaning dataflow600identifies redundant, disconnected, or false track segments to eliminate or combine the track segments, thereby reducing computing resources necessary to run planning system404and ensuring safe planned movements in response to various types of objects.

Referring now toFIG.7, illustrated is a diagram of an implementation of a track feature extraction net620. Track feature extraction net620is configured to determine features from the track segments. The features determined or extracted from the track segments indicate the quality of the track. Additionally, the features indicate the likelihood that track segments can be combined with other track segments. The features extracted from one track segment are capable of being fused, concatenated, or averaged with features from a different track segment in a track feature fusion net750. Features that are extracted can include trajectory features, cloud point features, and image features.

Track feature extraction net620extracts trajectory features using a trajectory feature net710. Trajectory features include track boxes from a track segment that can be concatenated to learn the motion characteristics of an object. For example, a track box can be extracted from second track segment614that determines the motion of the bicycle is 10 km/hour in a direction perpendicular to the vehicle. Perception system402can indicate that planning system404is to add a rectangle to an image of the bicycle to indicate the trajectory features of the object.

A track box includes a rectangle having a center, a size, and a heading angle. The rectangle has a center with (x,y) coordinates to determine the middle point of the object. The size of the rectangle has a width and a height that determines the overall size of the object. The heading angle determines the direction of travel. The metadata associated with the rectangle determines the type of object within the rectangle. For example, a track box monitoring a bicycle has a center at the bicycle seat, a size encompassing the bicycle from wheel to wheel, a heading angle of a direction perpendicular to the vehicle, and a metadata identifying the object as a bicycle. The track box can include a timestamp. Multiple track boxes can be strung together, each track box having its own timestamp. With all the track boxes, a machine learning model (as an example, a Multilayer Perceptron network), can be applied to learn robust trajectory features that better represent the track segment trajectory and motion.

Track feature extraction net620extracts cloud point features using a point cloud feature net720. The cloud point features include track points along the track segment that represent a 3D characteristic and motion of the object. The track points include (x, y, z) coordinates, an intensity, and a timestamp. For example, a cloud point feature can be extracted from second track segment614that determines the structure of the bicycle. Determining the structure of the bicycle includes determining how the bicycle is to be represented in a 3D plane. Track points along the tires, the frame, the rider, and the handlebars of the bicycle are extracted to represent the bicycle in a rendering of the bicycle in a 3D plane. Multiple cloud point features can be strung together, each cloud point feature having its own timestamp. With all the track points, a machine learning model (as an example, a PointNet network) can be applied to learn robust point features that better represent the object structure and track motion.

Track feature extraction net620can extract image features using an image feature net730. The image features include extracting a corresponding image. Extracting a corresponding image includes extracting segmentation, embedded features, or pixels related to the object. For example, an image of the bicycle is captured to represent the bicycle. The object image region or pixels can be obtained by projecting the 3D track points of the cloud points to the image plane. The pixels can be represented with RGB values and the timestamps can be associated with the image. Multiple images are strung together, each image having its own timestamp. A machine learning model (as an example, a Convolutional Neural Network), is used to learn image features that better represent the appearance features of the tracked object.

With all the trajectory features learned from the trajectory feature net710, point features learned from the point cloud feature net720, and the image features learned from the image feature net730, track feature fusion net750uses a machine learning model (as an example, a Multilayer Perceptron network), to combine these multimodal features into a single feature vector that better depicts the track segment. The trajectory feature net710, point cloud feature net720, image feature net730and track feature fusion net750can be learned together. The fused feature learned by the track feature fusion net750can make use of all trajectory, point cloud, and image features, and complement each modality better. For example, one white vehicle track segment and one black track segment can be differentiated easily from image features. For another example, a large bus in far distance and a minibus middle-range distance may look similar from image appearance, but it could be easily differentiated from the point features. Trajectory features can differentiate static and dynamic segments easily even when the object appearance and size are similar. In practice, the feature fusion net is flexible to drop one feature modality. For example, it could be trained with only trajectory features and point cloud features when the data contains only the point clouds. Also, the track feature fusion net could also be extended to include other track features like radar features. After the track feature fusion net750, a track segment can be represented by the track features760.

Track features760is the output of track feature fusion net750. Track features760includes the extracted features from the track segments. For example, track features760includes fusion features learned from track trajectory features and point features for the bicycle over the two time periods corresponding to first track segment612and second track segment614.

Referring now toFIG.8, illustrated is a diagram of an implementation of a track segment cleaning module630. Track segment cleaning module630includes track features760, a machine learning model820, and track scores830. Track scores830includes a first track quality score832, a second track quality score834, and a track stitching score836.

Track features760are received from track feature extraction net620. Track features760include a first track features812and a second track features814. First track features812correspond to the features extracted from first track segment612and second track features814correspond to the features extracted from second track segment614. First track features812and second track features814are evaluated to determine the quality of the track segment. The quality of the track segment can be indicative of the likelihood that the track segment represents an object in real life. First track features812and second track features814are compared to determine the stitching score of the at least two track segments. The stitching score of the two tracks is indicative of the likelihood that the resulting stitched track segment is representative of the same object in real life.

Track segment cleaning module630applies the machine learning model820to determine the quality score and the stitching scores for first track features812and second track features814. More specifically, track segment cleaning module630applies machine learning model820to determine first track quality score832associated with first track segment612and second track quality score834associated with second track segment614. In an embodiment, machine learning model820has a network architecture with one common convolutional network to consume first track features812and second track features814. The network architecture is configured to output the analyses for the track quality scores for each track and the track stitching score. In another embodiment, the machine learning model820has a network architecture including a two-branch convolutional network. One branch regresses the quality score for each track and another branch regresses the stitching score. In another embodiment, machine learning model820includes one or more of a multilayer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN), autoencoder, transformer, and/or the like.

Machine learning model820is trained to determine whether first track segment612and second track segment614are representative of the same object based on first track features812and second track features814. For example, machine learning model820determines that first track segment612and second track segment614are representative of the same object based on the track points and intensities of the cloud point features from first track features812and second track features814. In another example, machine learning model820determines that first track segment612and second track segment614have a low likelihood of being the same object based on the center positions and the timestamps of the trajectory features from first track features812and second track features814.

The track segment cleaning module transforms comparative and qualitative analysis from machine learning model820into track scores830. Track scores830include three scores: first track quality score832, second track quality score834, and track stitching score836. First track quality score832and second track quality score834are indicative of the likelihood that the track segment represents an object in real life. Track stitching score836of the two tracks is indicative of the likelihood that the resulting stitched track segment of the two tracks is representative of the same object in real life. Machine learning model820and track segment cleaning module630can generate first track quality score832, second track quality score834, and track stitching score836to determine the likelihood that first track segment612and second track segment614are representative of the same object exterior to the vehicle. For example, machine learning model820and track segment cleaning module630determines 0.9 is first track quality score832and 0.87 is second track quality score834. Machine learning model820and track segment cleaning module630determine first track features812and second track features814together have a stitching score of 0.93. In an embodiment, the scores can be generated in a range between 0 and 1.

A low track quality score is indicative of a high likelihood that a track segment is a false positive of an object in real life. For example, first track features812include several erratic cloud point features that dramatically vary the structure of the object between at least two timestamps. On the other hand, a high quality track segment is indicative of a high likelihood that a track segment is representative of an object in real life. For example, first track features812include consistent cloud point features that maintain the structure of the object over multiple timestamps. In an embodiment, the quality of the track segment is indicative of whether the track segment is to be combined with another track segment. The quality of the track segment is evaluated independent of other track segments. For example, the quality of first track features812are evaluated independently of second track features814.

A low stitching score is indicative that the at least two track segments have a low likelihood of representing the same object. For example, the difference in sizes and heading angles of the trajectory features of first track features812and second track features814are indicative of the low likelihood that the resulting stitched track segment is representative of the same object in real life. A high stitching score is indicative that the at least two track segments have a high likelihood of representing the same object. For example, the similarity in sizes and heading angles of the trajectory features of first track features812and second track features814are indicative of the high likelihood that the resulting stitched track segment is representative of the same object in real life.

Track segment cleaning module630can determine that some track segments are to be eliminated due to low track segment quality scores. In an embodiment, track segment cleaning module630applies machine learning model820to determine a third track segment quality score associated with a third track segment. Machine learning model820can generate the third track segment quality score to determine the likelihood the third track segment is representative of a real-life object exterior to the vehicle. For example, Machine learning model820and the track segment cleaning module630determine 0.3 is the third track quality score. With the track quality score threshold as 0.75, the third track quality score fails to satisfy the track quality score threshold. As a result, the track segment cleaning module630eliminates the third track segment.

The track segment cleaning module630determines whether first track quality score832, second track quality score834, and track stitching score836satisfy a threshold. For example, if the track quality score threshold is 0.75 and the stitching score threshold is 0.8, then the scores would be satisfied. If the scores satisfy the thresholds, then track segment cleaning module630is configured to combine first track segment612and second track segment614into a single track segment having a single trajectory and a single cloud point feature. For example, the two track segments representing the bicycle (e.g., first track segment612and second track segment614) and all of the extracted features are combined into a single track segment. The combined track segment then has a single set of trajectories and a single set of cloud-point features. In an embodiment, the combined single track segment has a single trajectory and a single cloud-point feature.

In an embodiment, first track segment612and second track segment614are combined by at least concatenating a trajectory feature of first track segment612with a trajectory feature of second track segment614. In an embodiment, first track segment612and second track segment614are combined by at least concatenating a cloud point feature of first track segment612with a cloud point feature of second track segment614.

In an embodiment, track segment cleaning module630identifies false track segments or low quality track segments for elimination. Track segment cleaning module630determines whether a track segment is to be eliminated based on the extracted features. For example, track segment cleaning module630evaluates the trajectory feature and, similarly, evaluates the cloud point feature corresponding to the bicycle over the first time period.

In an embodiment, track segment cleaning module630can apply machine learning model820trained to determine whether the track segment is representative of an object exterior to the vehicle based on the extracted features. Machine learning model820can provide analyses for generating scores representative of the likelihood the false track segment is representative of the same object exterior to the vehicle. For example, track segment cleaning module630assigns a track quality score of 0.25 to the track segment.

In an embodiment, track segment cleaning module630determines whether the score satisfies a threshold. For example, if the track quality score threshold is 0.75, then the score generated by machine learning model820for the track segment is not satisfied. If the score does not satisfy the threshold, then track segment cleaning module630is configured to eliminate the false track segment. For example, the track segment falsely representing the bicycle and all of the extracted features are eliminated.

Referring now toFIG.9, illustrated is a diagram of an implementation of a training sample generator configured to generate samples for training the machine learning model. A training dataflow900utilizes a data generator910to generate the training samples for training the machine learning model. The training samples can be generated by training sample generator912. Training machine learning models involved in track feature extraction net620and track segment cleaning modules630improves the accuracy of the track segment cleaning. Additionally, a training sample generator912corrects the problem of the shortage of labeled training samples for training the machine learning model820.

Training sample generator912produces labeled training samples for training track feature extraction net620and machine learning model820of track segment cleaning module630. Training sample generator912is configured to associate a label with a track segment to generate a training sample. Training sample generator912can utilize ground truth tracks to generate the labels for the training samples. Ground truth tracks can include accurate representations of the movement of perceived objects. Ground truth tracks can also be at least partially synthetic and correspond to the movement of simulated objects. Training sample generator912compares the ground truth tracks to one or more track segments to produce training samples. If training sample generator912makes a match between a ground truth track from a plurality of ground truth tracks and the one or more track segments, training sample generator912assigns a first label to the track segment to produce a training sample with a first label. Additionally, if training sample generator912fails to make a match between a ground truth track from a plurality of ground truth tracks and the one or more track segments, training sample generator912assigns a second label to the track segment to produce a training sample with a second label.

Training sample generator912produces training samples indicating false track segments. For example, a training sample with a quality score label of ‘0’ indicates the training sample does not correspond to a ground truth track (i.e., the training sample is a false positive). In another example, a training sample with a quality score label of ‘1’ indicates the training sample corresponds to a ground truth track (i.e., the training sample matches a real object). The training samples can be a standard for comparison in determining the accuracy and quality of other track segments that are input into track feature extraction net620and machine learning model820of track segment cleaning module630.

In addition to a label indicating whether a track segment is a false track, the training samples produced by training sample generator912can also include a label to any two input track segments to indicate whether they should be stitched together. For example, two track segments that match with the same ground truth track are be assigned a stitching score ‘1’ to indicate they should be combined. Two track segments that matched with different ground truth tracks will be assigned with a stitching score ‘0’ to indicate they should not be combined. One track segment with matched ground truth track and another unmatched track segment will be assigned with stitching score of ‘0’ since they are from different tracks. For two track segments that have no matched ground truth tracks, the stitching score between them could be ignored during training, as they will be eliminated by the tracking quality scores as their tracking quality will be 0. It is not necessary to stitch these unmatched track segments.

Training samples can be like the other track segments and include the trajectory features, cloud point features of other track segments, and/or image features. The training samples are used to train track feature extraction net620and machine learning model820together to, for example, correctly identify false track segments and track segments that are to be combined. In an embodiment, training samples may also be used as validation samples to determine whether the performance of track feature extraction net620and machine learning model820of track segment cleaning module630in identifying false track segments and track segments that require stitching is satisfactory.

In an embodiment, training sample generator912generates training samples for track feature extraction net620and machine learning model820of track segment cleaning module630. In an embodiment, a training sample generated by training sample generator912includes at least two track segments. Training sample generator912compares a set of ground truth tracks to one or more track segments to produce the training samples with a quality score label. Training sample generator912compares a track segment to each of the ground truth tracks to determine whether a matching ground truth track exists for assigning a track quality label. To determine the matching ground truth track, training sample generator912walks through a ground truth track matching flowchart1000to match the correct ground truth track to the track segment and/or training sample for the quality score label. For example, if training sample generator912identifies a track segment matching a ground truth track, then training sample generator912assigns a quality score label of ‘1’ to the training sample including the track segment based on the track segment matching the ground truth track.

Additionally, the training sample generator912compares two or more track segments to produce the training samples with a stitching score. A training sample generated by training sample generator912includes at least two track segments. Training sample generator912can compare the track segments of the training samples for assigning a stitching score label. To determine two or more matching track segments of a training sample, training sample generator912checks whether two track segments are matched to the same ground truth track. For example, if training sample generator912identifies a track segment can be combined with another track segment, then training sample generator912assigns a stitching score label of ‘1’ to both track segments of the training sample' based on the two track segments matching the same ground truth track. If training sample generator912does not match a track segment to another track segment, then training sample generator912assigns a stitching score label of ‘0’ to both the track segments of the training sample based on the two or more track segments not matching the same ground truth track. A track quality label of 0 can indicate that the track segment is a false track.

In an embodiment, training sample generator912is configured to divide a single track into two or more track segments to create training samples. Training sample generator912is configured to add a label to the two or more track segments to create the two or more training samples. For example, a label for the training samples including the two or more track segments has a ground truth stitching score of ‘1’ to indicate that the two or more track segments are from the same track. Alternatively, the training samples are associated with a label identifying the two or more track segments as belonging to separate tracks. For example, a label for the training samples including the two or more track segments belonging to separate tracks has a ground truth stitching score of ‘0’ to indicate that the two or more track segments are from different tracks. In an implementation, training sample generator912can randomly divide a track segment with matched ground truth labels into at least two training samples.

Dividing a track segment to create two or more training samples creates a diversity of the training data. Without a sufficient number of labeled training samples, track feature extraction net620and machine learning model820cannot accurately determine the quality score of the track segments representative of the objects encountered by the vehicle. In an embodiment, a track segment is not eligible to be divided to produce training samples if the track segment fails to satisfy a frame threshold. For example, training sample generator912ignores attempts to divide a track segment if the track segment has fewer than five frames. A frame can be a timestamp. In an embodiment, track sample generator912has a rule in which each of the at least two divided track segments has at least three timeframes.

Now referring toFIG.10, illustrated is a flowchart for generating a training sample with a ground truth label for training a machine learning model. Ground truth track matching flowchart1000results in selecting a ground truth track that matches a track segment input to output a training sample. Training sample generator912can compare the track segment representations against the ground truth track representations to produce a labeled training sample. In an embodiment, ground truth track matching flowchart1000eliminates ineligible ground truth tracks to determine the ground truth track corresponding to the input track segment.

At1010, training sample generator912compares the timestamps of the track segment and the ground truth track. If there are no common timestamps between the track segment and the ground truth track from the plurality of ground truth tracks, training sample generator912determines the ground truth track from the plurality of ground truth tracks is an ineligible match and proceeds to the next ground truth track. Training sample generator912compares the timestamps for each ground truth track in the plurality of ground truth tracks and determines a subset of ground truth tracks that are potentially eligible matches based on the timestamps. Ground truth tracks outside of the subset of ground truth tracks are eliminated for being ineligible matches. In an embodiment, no ground truth tracks may remain in the subset of ground truth tracks. In this case, training sample generator912assigns a quality score label of ‘0’ to the track segment as none of the ground truth tracks are eligible matches.

At1020, training sample generator912determines whether the ground truth track from the subset of ground truth tracks has a trajectory feature exceeding a distance threshold from the trajectory feature of the track segment. The distance threshold can be a Euclidean distance. The distance threshold can be an affinity threshold representative of any affinity or dis-similarity function. If the trajectory feature of the ground truth track from the subset of ground truth tracks exceeds the distance threshold, then training sample generator912determines that the ground truth track from the subset of ground truth tracks is an ineligible match and proceeds to the next ground truth track. Training sample generator912compares the distances of the trajectory features for each ground truth track in the plurality of ground truth tracks and determines a second subset of ground truth tracks that are potentially eligible matches based on the trajectory features. Ground truth tracks outside of the second subset of ground truth tracks are eliminated for being ineligible matches. In an embodiment, no ground truth tracks may remain in the second subset of ground truth tracks. In this case, training sample generator912assigns a quality score label of ‘0’ to the track segment as none of the ground truth tracks are eligible matches.

At1030, training sample generator912determines the number of boxes from the trajectory feature of the ground truth track segment from the second subset of ground truth tracks that are proximate to the boxes of the track segment. A ground truth box is proximate to a track segment box if the ground center distance or the intersection of union (IoU between the two boxes is below a certain threshold. A ground truth box is proximate to a track segment if the ground center distance of the two boxes is less than scale_factor*(diagonal(box1)+diagonal(box2))/2.0. Scale_factor of 1.5 can be used. Comparing the boxes of the ground truth track and the track segment can be used to filter poor matches between the ground truth tracks from the second subset of ground truth tracks and the track segment. If the number of proximate boxes between the ground truth track and the track segment falls below a box threshold, then training sample generator912determines that the ground truth track from the second subset of ground truth tracks is an ineligible match and proceeds to the next ground truth track. Training sample generator912compares the proximate boxes for each ground truth track from the second subset of ground truth tracks and determines a third subset of ground truth tracks that are potentially eligible matches based on the number of close boxes. Ground truth tracks outside of the third subset of ground truth tracks are eliminated for being ineligible matches. In an embodiment, no ground truth tracks may remain in the third subset of ground truth tracks. In this case, training sample generator912assigns a quality score label of ‘0’ to the track segment as none of the ground truth tracks are eligible matches.

At1040, training sample generator912selects the ground truth track with the highest number of proximate boxes relative to the boxes of the track segment from the third subset of ground truth tracks. In an embodiment, the selected track segment has a track quality label assigned as ‘1’ based on the track segment matching the best ground truth track. If no ground truth tracks match the track segment, then training sample generator912assigns a quality score of ‘0’. Additionally, training sample generator912assigns a stitching score label of ‘1’ based on the track segment matching the same ground truth track as another track segment. Stitching score labels of ‘1’ are assigned to two or more two track segments if the two or more track segments correspond to the same ground truth track. If no two track segments match the ground truth track, then the training sample generator912assigns a stitching score of ‘0’ to the training sample.

Now referring toFIG.11, illustrated is a diagram of an implementation of matching ground truth tracks to a track segment. Three ground truth tracks are depicted: a first ground truth track1115, a second ground truth track1125, and a third ground truth track1135. A track segment is compared against each of the three ground truth tracks to produce a labeled training sample. Performing ground truth track matching flowchart1000described with respect toFIG.10, training sample generator912selects the ground truth track that matches with the track segment. The ground truth tracks are depicted using a trajectory of 3D boxes. The ground truth tracks are sequentially ordered based on a timestamp.

First ground truth track1115, second ground truth track1125, and Third ground truth track1135can be compared to the track segment. The comparison between the trajectory boxes of the track segment against the trajectory boxes of the ground truth tracks can be based on the steps of ground truth track matching flowchart1000. Training sample generator912determines that second ground truth track1125is the matching ground truth track using the ground truth track matching flowchart1000. Additionally, training sample generator912determines that second ground truth track1125is the matching ground truth track based on timestamps, trajectory features satisfying a distance threshold, and/or a number of proximate boxes between the track segment and the ground truth track. In an embodiment, the trajectory boxes for each of the ground truth tracks are aligned against the track segment to determine the best fit.

Now referring toFIG.12, illustrated is a flowchart of a process for track segment cleaning using a machine learning model. In some embodiments, one or more of the steps described with respect to process1200are performed (e.g., completely, partially, and/or the like) by the vehicle scenario mining dataflow. Additionally, or alternatively, in some embodiments, one or more steps described with respect to process1200are performed (e.g., completely, partially, and/or the like) by another device or group of devices separate from or including the vehicle scenario mining dataflow.

At1202, a first track segment and a second track segment are detected. The track segments are representative of tracked objects exterior to the vehicle that are spatially monitored relative to a position of the vehicle over a period of time. For example, a bicycle detected over a first time period is the first track segment and a bicycle detected over a second time period is the second track segment.

At1204, the first track segment and the second track segment are determined to be representative of an identical object. The first track segment and the second track segment are determined to be candidates for combination into a single track segment. Features can be extracted from the first track segment and the second track segment to be evaluated by machine learning model820. Machine learning model820performs analyses and determines whether the first track segment and the second track segment satisfy score thresholds indicating that the first track segment and the second track segment are to be combined. For example, machine learning model820determines that the bicycle from the first track segment and the bicycle from the second track segment represent the same object.

At1206, the first track segment and the second track segment are combined to form a single track segment. The first track segment and the second track segment are combined by concatenating the features of the first track segment with the features of the second track segment into a single set of features. For example, the features of the bicycle tracked by the first track segment are concatenated with the features of the bicycle tracked by the second track segment to form a single track segment that includes a single set of features.

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.