Uatc, LLC

Aurora Operations, Inc.

Shivam Gautam

Carlos Vallespi-Gonzalez

Brian C Becker

Nemanja Djuric

Abhishek Sen

Cole Christian Gulino

Gregory P Meyer

Abhishek Mohta

Carl Knox Wellington

Ankit Laddha

Joseph Lawrence Amato

Fang-Chieh Chou

Sida Zhang

Shreyash Pandey

Jake Scott Charland

Peter M Kingston

Chi-Kuei Liu

An autonomous platform can obtain sensor data descriptive of an actor in an environment of an autonomous vehicle and at least a portion of the environment of the autonomous vehicle that does not include the actor, the sensor data comprising at least one sweep of the environment of the autonomous vehicle; process the sensor data with a machine-learned perception model to generate a detection of the actor and one or more predicted future velocities; and determine a motion trajectory for the autonomous vehicle based at least in part on the detection and the one or more predicted future velocities.

Perception system for an autonomous vehicle

Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.

Multiple stage image based object detection and recognition

Various examples are directed to systems and methods for controlling an autonomous vehicle comprising a tractor and a trailer. For example, a system may determine that a line from a position of a first sensor on the autonomous vehicle to a position of a first actor in an environment of the autonomous vehicle intersects the trailer. The system may determine that the first actor is in a blind spot of the autonomous vehicle, generate a motion plan for the autonomous vehicle, and control the autonomous vehicle in accordance with the motion plan.

Autonomous vehicle blind spot management

Systems, methods, tangible non-transitory computer-readable media, and devices associated with object association and tracking are provided. Input data can be obtained. The input data can be indicative of a detected object within a surrounding environment of an autonomous vehicle and an initial object classification of the detected object at an initial time interval and object tracks at time intervals preceding the initial time interval. Association data can be generated based on the input data and a machine-learned model. The association data can indicate whether the detected object is associated with at least one of the object tracks. An object classification probability distribution can be determined based on the association data. The object classification probability distribution can indicate a probability that the detected object is associated with each respective object classification. The association data and the object classification probability distribution for the detected object can be outputted.

Association and tracking for autonomous devices

Systems, methods, tangible non-transitory computer-readable media, and devices for associating objects are provided. For example, the disclosed technology can receive sensor data associated with the detection of objects over time. An association dataset can be generated and can include information associated with object detections of the objects at a most recent time interval and object tracks of the objects at time intervals in the past. A subset of the association dataset including the object detections that satisfy some association subset criteria can be determined. Association scores for the object detections in the subset of the association dataset can be determined. Further, the object detections can be associated with the object tracks based on the association scores for each of the object detections in the subset of the association dataset that satisfy some association criteria.

Object association for autonomous vehicles

Systems and methods for detecting objects and predicting their motion are provided. In particular, a computing system can obtain a plurality of sensor sweeps. The computing system can determine movement data associated with movement of the autonomous vehicle. For each sensor sweep, the computing system can generate an image associated with the sensor sweep. The computing system can extract, using the respective image as input to one or more machine-learned models, feature data from the respective image. The computing system can transform the feature data into a coordinate frame associated with a next time step. The computing system can generate a fused image. The computing system can generate a final fused image. The computing system can predict, based, at least in part, on the final fused representation of the plurality of sensors sweeps from the plurality of sensor sweeps, movement associated with the feature data at one or more time steps in the future.

Systems and methods for object detection and motion prediction by fusing multiple sensor sweeps into a range view representation

Systems, device, and methods for trajectory association and tracking are provided. A method can include obtaining input data indicative of a respective trajectory for each of one or more first objects for a first time step and input data indicative of a respective trajectory for each of one or more second objects for a second time step subsequent to the first time step. The method can include generating, using a machine-learned model, a temporally-consistent trajectory for at least one of the one or more first objects or the one or more second objects based at least in part on the input data and determining a third predicted trajectory for the at least one of the one or more first objects or the one or more second objects for at least the second time step based at least in part on the temporally-consistent trajectory.

Object trajectory association and tracking

Systems, methods, tangible non-transitory computer-readable media, and devices for operating an autonomous vehicle are provided. For example, a vehicle computing system can receive sensor data including information based on sensor outputs associated with detection of objects in an environment by sensors of a vehicle. Inflection points can be determined based at least in part on the sensor data. The inflection points can correspond to portions of the objects that occlude detection of the environment beyond the portions of the objects. A set of polar coordinates can be determined for each of the one or more inflection points. The set of polar coordinates can include a distance from the sensors to a portion of the objects and an angle of the sensor with respect to the portion of the objects. Furthermore, sparse map data including the set of polar coordinates can be generated based on the set of polar coordinates.

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Pittsburgh, PA, United States of America

Shivam Gautam