Zoox, Inc.

Noureldin Ehab Hendy

Chuang Wang

Cooper Stokes Sloan

James William Vaisey Philbin

Feng Tian

Yi Xu

Nicholas George Charchut

Li Yon Tan

Sarah Tariq

Xiang Gao

William Harland Montgomery, Iv

Andrew James Feit

Eric Carl Daria Wiener

Yihang Zhang

A vehicle safety system of an autonomous vehicle may determine predicted velocity vectors for a potential collision, and use the velocity vectors to determine a trajectory for the autonomous vehicle to traverse the environment. The vehicle safety system may analyze sensor data to determine a likelihood of a potential collision with a dynamic object in the environment. Predicted velocity vectors may be determined for the autonomous vehicle and the dynamic object at a time and/or location associated with the potential collision. The predicted velocity vectors may be used to determine a point of impact, relative angle, and/or relative velocity between the autonomous vehicle and dynamic object at the potential collision. The likelihood of the potential collision and the predicted velocity vectors may be used to determine a trajectory for the autonomous vehicle, which may include following a current trajectory or transitioning to one or more contingent trajectories.

Collision avoidance and mitigation in autonomous vehicles using predicted trajectories

Techniques for fusing sensor data generated by different sensor modalities to improve object detections and object predictions determined by low-level systems of a vehicle. The techniques may include determining feature maps based on sensor data generated by different sensor modalities associated with a vehicle. In some examples, the feature maps may include at least a first feature map indicative of a location of an object in an environment of the vehicle and a second feature map indicative of elevation information associated with the object. The techniques may also include inputting the first feature map and the second feature map into a machine-learned model associated with the low-level system of the vehicle. In some examples, an output may be received from the machine-learned model that includes an occupancy grid, and the occupancy grid may exclude representation(s) associated with over-drivable object(s) and/or under-drivable object(s) that may be disposed in the environment.

Sensor fusion for object detection

Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle safety system can implement one or more models to output data indicating an intersection probability between the object and a portion of the vehicle in the future. The model may employ a blur filter to modify a representation of an object usable to determine the intersection probability. A vehicle can be controlled in an environment based at least in part on the intersection probability.

Normalized object occupancy determination

Techniques for verifying a reliability of map data are discussed herein. In some examples, map data can be used by a vehicle, such as an autonomous vehicle, to traverse an environment. Sensor data (e.g., image data, lidar data, etc.) can be received from a sensor associated with a vehicle and may be used to generate an estimated map and confidence values associated with the estimated map. When the sensor data is image data, images data from multiple perspectives or different time instances may be combined to generate the estimated map. The estimated map may be compared to a stored map or to a proposed vehicle trajectory or corridor to determine a reliability of the stored map data.

Verifying reliability of data used for autonomous driving

A collision avoidance system may validate, reject, or replace a trajectory generated to control a vehicle. The collision avoidance system may comprise a secondary perception component that may receive sensor data, receive and/or determine a corridor associated with operation of a vehicle, classify a portion of the sensor data associated with the corridor as either ground or an object, determine a position and/or velocity of at least the nearest object, determine a threshold distance associated with the vehicle, and control the vehicle based at least in part on the position and/or velocity of the nearest object and the threshold distance.

Collision avoidance perception system

Techniques for analyzing driving scenarios are discussed herein. For example, techniques may include determining a level of exposure associated with scenarios, searching for similar scenarios, and generating new additional scenarios. A driving scenario may be represented as top-down multi-channel data. The top-down multi-channel data may be provided as input to a neural network trained to output a prediction of future events. A multi-dimensional vector representing the scenario can be received as an intermediate output from the neural network and may be stored to represent the scenario. Multi-dimensional vectors representing different scenarios may be stored in a multi-dimensional space, and similar scenarios may be identified by proximity searching of the multi-dimensional space.

Top-down scenario exposure modeling

Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle safety system can implement a model to output data indicating an intersection probability between the object and a portion of the vehicle in the future. The model may employ a rear collision filter, a distance filter, and a time to stop filter to determine whether a predicted collision may be a false positive, in which case the techniques may include refraining from reporting such predicted collision to other another vehicle computing device to control the vehicle.

Velocity regression safety system

A collision avoidance system may validate, reject, or replace a trajectory generated to control a vehicle. The collision avoidance system may comprise a secondary perception component that may comprise one or more machine learned models, each of which may be trained to output one or more occupancy maps based at least in part on sensor data of different types. The occupancy maps may include a prediction of whether at least a portion of an environment is occupied at a future time by any one of multiple object types. Occupancy maps associated with a same time may be aggregated into a data structure that may be used to validate, reject, or replace the trajectory.

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West Lafayette, IN, United States of America

Noureldin Ehab Hendy