Uatc, LLC

Aurora Operations, Inc.

Uber Technologies, Inc.

Velo Ai, Inc.

Galen Clark Haynes

Micol Marchetti-Bowick

Tian Lan

Alexander David Styler

David McAllister Bradley

Nemanja Djuric

Tzu-Kuo Huang

Neil Traft

Sai Bhargav Yalamanchi

Charles R Hogg, Iii

Michael Lee Phillips

Anthony Joseph Stentz

Jeff Schneider

Eric Chen Deng

Skanda Shirdhar

Vladan Radosavljevic

Tsung-Han Lin

Sumit Kumar

Yuhang Ma

Huahua Wang

Hersh Mehta

Eric B Werner

Albert John Biglan

Yasmine Straka Concilio

Poornima Kaniarasu

Skanda Shridhar

Tara Lynn Stentz

Ian Dewancker

Zhengdi Shen

Anh Tuan Hoang

Yiming Gu

Systems and methods for generating performance metrics for autonomous vehicle systems are provided. The performance metrics include two complementary metrics that evaluate a machine-learning object prediction model relative to a number of potential trajectories of an autonomous vehicle. The performance metrics include an avoidance metric that quantifies a probability that a region occupied by a real-world or simulated object is reached by the autonomous vehicle, the region is not blocked by another object, and the region is not blocked by a prediction output by the machine-learning object prediction model. The performance metrics also include an availability metric that quantifies a probability that a simulated or real-world object is not located within a region, the region is not blocked by another simulated or real-world object, and the autonomous vehicle is unnecessarily blocked by the prediction output before the autonomous vehicle reaches the particular footprint.

Metrics for evaluating autonomous vehicle performance

Systems and methods for determining object motion and controlling autonomous vehicles are provided. In one example embodiment, a computing system includes processor(s) and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the processor(s) cause the computing system to perform operations. The operations include obtaining data associated with a first object and one or more second objects within a surrounding environment of an autonomous vehicle. The operations include determining an interaction between the first object and the one or more second objects based at least in part on the data. The operations include determining one or more predicted trajectories of the first object within the surrounding environment based at least in part on the interaction between the first object and the one or more second objects. The operations include outputting data indicative of the one or more predicted trajectories of the first object.

Object interaction prediction systems and methods for autonomous vehicles

Systems and methods for predicting interactions between objects and predicting a trajectory of an object are presented herein. A system can obtain object data associated with a first object and a second object. The object data can have position data and velocity data for the first object and the second object. Additionally, the system can process the obtained object data to generate a hybrid graph using a graph generator. The hybrid graph can have a first node indicative of the first object and a second node indicative of the second object. Moreover, the system can process, using an interaction prediction model, the generated hybrid graph to predict an interaction type between the first node and the second node. Furthermore, the system can process, using a graph neural network model, the predicted interaction type between the first node and the second node to predict a trajectory of the first object.

Systems and methods for interaction-based trajectory prediction

Systems and methods for detecting a surprise or unexpected movement of an actor with respect to an autonomous vehicle are provided. An example computer-implemented method can include, for a first compute cycle, obtaining motion forecast data based on first sensor data collected with respect to an actor relative to an autonomous vehicle; and determining, based on the motion forecast data, failsafe region data representing an unexpected path or area where a likelihood of the actor following the unexpected path or entering the unexpected area is below a threshold. For a second compute cycle after the first compute cycle, the method can include obtaining second sensor data; determining, based on the second sensor data and the failsafe region data, that the actor has followed the unexpected path or entered the unexpected area; and in response to such determination, determining a deviation for controlling a movement of the autonomous vehicle.

Systems and methods for detecting surprise movements of an actor with respect to an autonomous vehicle

In some examples, a mobility safety system includes a real-time data capturing component to capture data relating to conditions within a traffic environment using at least one of a camera, a radar sensor, a LiDAR sensor, a proximity sensor, an inertial measurement unit (IMU), and a global positioning system (GPS), at least one trained model to generate a risk estimation related to a mobility platform within the traffic environment, the at least one trained model being trained using at least one of supervised, unsupervised and semi-supervised learning, a risk estimation component to generate the risk estimation, and an alert activation component to generate a first alert directed at an operator of the mobility platform based on the risk estimation.

Artificially intelligent mobility safety system

Systems, methods, tangible non-transitory computer-readable media, and devices associated with trajectory prediction are provided. For example, trajectory data and goal path data can be accessed. The trajectory data can be associated with an object's predicted trajectory. The predicted trajectory can include waypoints associated with waypoint position uncertainty distributions that can be based on an expectation maximization technique. The goal path data can be associated with a goal path and include locations the object is predicted to travel. Solution waypoints for the object can be determined based on application of optimization techniques to the waypoints and waypoint position uncertainty distributions. The optimization techniques can include operations to maximize the probability of each of the solution waypoints. Stitched trajectory data can be generated based on the solution waypoints. The stitched trajectory data can be associated with portions of the solution waypoints and the goal path.

Trajectory prediction for autonomous devices

Generally, the present disclosure is directed to systems and methods for streaming processing within one or more systems of an autonomy computing system. When an update for a particular object or region of interest is received by a given system, the system can control transmission of data associated with the update as well as a determination of other aspects by the given system. For example, the system can determine based on a received update for a particular aspect and a priority classification and/or interaction classification determined for that aspect whether data associated with the update should be transmitted to a subsequent system before waiting for other updates to arrive.

Systems and methods for streaming processing for autonomous vehicles

Systems and methods for determining object prioritization and predicting future object locations for an autonomous vehicle are provided. A method can include obtaining, by a computing system comprising one or more processors, state data descriptive of at least a current or past state of a plurality of objects that are perceived by an autonomous vehicle. The method can further include determining, by the computing system, a priority classification for each object in the plurality of objects based at least in part on the respective state data for each object. The method can further include determining, by the computing system, an order at which the computing system determines a predicted future state for each object based at least in part on the priority classification for each object and determining, by the computing system, the predicted future state for each object based at least in part on the determined order.

Systems and methods for prioritizing object prediction for autonomous vehicles

Systems and methods for motion planning by a vehicle computing system of an autonomous vehicle are provided. The vehicle computing system can input sensor data to a machine-learned system including one or more machine-learned models. The computing system can obtain, as an output of the machine-learned model(s), motion prediction(s) associated with object(s) detected by the system. The system can convert a shape of the object(s) into a probability of occupancy by convolving an occupied area of the object(s) with a continuous uncertainty associated with the object(s). The system can determine a probability of future occupancy of a plurality of locations in the environment at future times based at least in part on the motion prediction(s) and the probability of occupancy of the object(s). The system can provide the motion prediction(s) and the probability of future occupancy of the plurality of locations to a motion planning system of the autonomous vehicle.

Full uncertainty for motion planning in autonomous vehicles

An autonomous vehicle can obtain state data associated with an object in an environment, obtain map data including information associated with spatial relationships between at least a subset of lanes of a road network, and determine a set of candidate paths that the object may follow in the environment based at least in part on the spatial relationships between at least two lanes of the road network. Each candidate path can include a respective set of spatial cells. The autonomous vehicle can determine, for each candidate path, a predicted occupancy for each spatial cell of the respective set of spatial cells of such candidate path during at least a portion of a prediction time horizon. The autonomous vehicle can generate prediction data associated with the object based at least in part on the predicted occupancy for each spatial cell of the respective set of spatial cells for at least one candidate path.

Goal-directed occupancy prediction for autonomous driving

Systems, devices, products, apparatuses, and/or methods for generating a driving path for an autonomous vehicle on a roadway by determining one or more prior probability distributions of one or more motion paths for one or more objects that have previously moved in a geographic location and/or for controlling travel of an autonomous vehicle on a roadway by predicting movement of a detected object according to one or more prior probability distributions of one or more motion paths for one or more objects that have previously moved in a geographic location.

All mover priors

Systems, methods, tangible non-transitory computer-readable media, and devices associated with vehicle control based on risk-based interactions are provided. For example, vehicle data and perception data can be accessed. The vehicle data can include the speed of an autonomous vehicle in an environment. The perception data can include location information and classification information associated with an object in the environment. A scenario exposure can be determined based on the vehicle data and perception data. Prediction data including predicted trajectories of the object can be accessed. Expected speed data can be determined based on hypothetical speeds and hypothetical distances between the vehicle and the object. A speed profile that satisfies a threshold criteria can be determining based on the scenario exposure, the prediction data, and the expected speed data, over a distance. A motion plan to control the autonomous vehicle can be generated based on the speed profile.

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Pittsburgh, PA, United States of America

Galen Clark Haynes