Zoox, Inc.

Oytun Ulutan

Derek Xiang Ma

Sarah Tariq

Kratarth Goel

Yi Xu

Vasiliy Karasev

Xin Wang

Yi-Ting Lin

Subhasis Das

Kenneth Michael Siebert

Amir Ghaderi

William Harland Montgomery, Iv

Hang Ren

Shuangyu Li

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

Techniques are described herein for determining a yaw value for a traffic light head. The technique comprises obtaining sensor data via the sensor associated with the vehicle, the sensor data including a traffic light head. Based at least in part on the sensor data, a yaw value is determined for the traffic light head indicative of a degree that the traffic light head is yawed with respect to a line of sight of the sensor or the vehicle. Based at least in part on the yaw value, an extent that the traffic light head is associated with navigation of the vehicle is determined. The vehicle is then controlled based at least in part on the extent that the traffic light head is associated with the navigation of the vehicle.

Yawed traffic light determination

A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.

Machine-learned architecture for efficient object attribute and/or intention classification

There is provided methods, systems, and computer-readable media for determining intention of bicycles and other person-wide vehicles. A method comprises: receiving, from one or more sensors of an autonomous vehicle, sensor data relating to an external environment of the autonomous vehicle; detecting, based at least in part on the sensor data, a person-wide vehicle in the external environment proximate to the autonomous vehicle; determining, based at least in part on the sensor data and the person-wide vehicle, image data including the person-wide vehicle; inputting the image data to a machine-learned model; receiving, from the machine-learned model based on the image data, a future intention of the person-wide vehicle; and controlling the autonomous vehicle based at least in part on the future intention of the person-wide vehicle.

Determining intention of bicycles and other person-wide vehicles

Techniques for detecting attributes and/or gestures associated with pedestrians in an environment are described herein. The techniques may include receiving sensor data associated with a pedestrian in an environment of a vehicle and inputting the sensor data into a machine-learned model that is configured to determine a gesture and/or an attribute of the pedestrian. Based on the input data, an output may be received from the machine-learned model that indicates the gesture and/or the attribute of the pedestrian and the vehicle may be controlled based at least in part on the gesture and/or the attribute of the pedestrian. The techniques may also include training the machine-learned model to detect the attribute and/or the gesture of the pedestrian.

Pedestrian attribute and gesture detection

Machine-learned model training for pedestrian attribute and gesture detection

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Buena Park, CA, United States of America

Oytun Ulutan