Zoox, Inc.

William Harland Montgomery, Iv

Yi Xu

Cooper Stokes Sloan

Oytun Ulutan

Hang Ren

Samir Joshi

Shengnan Liang

Kelly Leece Mcguire

Techniques for detecting and labeling construction zones in an environment are disclosed. Two-dimensional images may be evaluated to identify pixels that may be associated with a construction zone and labeled accordingly. Labels for corresponding pixels in a separate image or non-construction zone labels for the same pixels may be compared to the construction zone pixels and an output image pixel label may be determined based on one or more criteria. An output image can be provided for vehicle control and for other operations, such as top-down segmentation and trajectory determination. Output images and related data may also be used to train a model to perform construction zone detection and labeling.

Systems and methods for construction zone detection

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 detecting and/or classifying objects in a driving environment are described herein. A vehicle may receive sensor data and input the sensor data into a perception and/or localization component which may be trained to provide processed output data (e.g., bounding boxes, track(s), etc.). In such cases, the vehicle may generate a scene embedding based on the output data. Further, the vehicle may receive image data from an image capturing device on the vehicle. The vehicle may generate an image embedding associated with the image data and concatenate the image embedding with the scene embedding. The vehicle may input the concatenated embedding into a detection and/or classification model to detect and/or classify object(s) within the environment.

Detection and classification based on scene embeddings and image embeddings

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Burlingame, CA, United States of America

William Harland Montgomery, Iv