Zoox, Inc.

Snap Inc.

Yi Xu

Sarah Tariq

Cooper Stokes Sloan

Xin Wang

Michael David Marr

James William Vaisey Philbin

Michael Ronald Cieslak

Chuang Wang

Zahra Ferdowsi

Kratarth Goel

Feng Tian

Vasiliy Karasev

Caleb Ogden

Oytun Ulutan

Xiaolin Shi

Hussein Mehanna

Aysegul Cansizoglu

Kai Zhenyu Wang

Pengfei Duan

Noureldin Ehab Hendy

William Harland Montgomery, Iv

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

Clustering a plurality of client devices running an application as a function of a data structure such that the plurality of client devices are each assigned a cluster. Client devices having similar performance metrics are assigned the same cluster. An operation of the application is modified as a function of the performance metrics of the client device. The modification of application operation is performed by turning certain features of the application on and off using a rule based on device cluster.

Device clustering

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

Techniques for detecting door states associated with vehicles in an environment are described herein. The techniques may include receiving sensor data associated with a first vehicle in an environment and inputting the sensor data into a machine-learned model that is configured to determine a state of a door of the first vehicle. Based on the input data, an output may be received from the machine-learned model that indicates the state of the door of the first vehicle. Based on the state of the door of the first vehicle, a trajectory of a second vehicle may be controlled. The techniques may also include training the machine-learned model to detect door states of vehicles.

Vehicle door state detection

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

Techniques relating to monitoring map consistency are described. In an example, a monitoring component associated with a vehicle can receive sensor data associated with an environment in which the vehicle is positioned. The monitoring component can generate, based at least in part on the sensor data, an estimated map of the environment, wherein the estimated map is encoded with policy information for driving within the environment. The monitoring component can then compare first information associated with a stored map of the environment with second information associated with the estimated map to determine whether the estimated map and the stored map are consistent. Component(s) associated with the vehicle can then control the object based at least in part on results of the comparing.

Map consistency checker

Machine-learned model training for pedestrian attribute and gesture detection

A sensor degradation detection and remediation system includes one or more sensors configured to collect image data from an environment. A combination of techniques may be used to detect degradations within regions of the image data captured by a sensor, including one or more of determining a level of the visual consistency between the associated image regions captured by different sensors, determining a level of opaqueness of the image regions, and/or measuring temporal movement of the image regions captured by a sensor over a period of time. Operations of a vehicle or other system may be controlled based at least in part on the detection of degradations of the image data captured by the sensors, including automated cleaning of a sensor surface, reducing a level of reliance on the image data received from the sensor, and/or changing a direction of travel of the vehicle.

Sensor degradation detection and remediation

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Pasadena, CA, United States of America

Yi Xu