Zoox, Inc.

Thomas Bernard Gacka

Collin MacGregor

Andreas Christian Reschka

Gerrit Bagschik

Leonardo Poubel Orenstein

Kshitij Agarwal

Julia A Wyatt

Nicolas Roman Hammer

Pengzhi Tian

Techniques for determining a rear end collision probability for a vehicle are discussed herein. The rear end collision probability can be determined based on data associated with the vehicle and an object proximate the vehicle, probability distribution data, and a vehicle maneuver value. The probability distribution data, which can be received, may represent a reaction time of the object and a maneuver value of the object. The rear end collision probability can be utilized to control the vehicle.

Rear end collision probability calculation

Techniques are discussed herein for generating and evaluating autonomous vehicle control systems. In examples, a driving scenario is determined and log data, e.g., generated from user-supported vehicles travelling in an environment, may be used to determine instances in which vehicles experience the driving scenario. Based on the log data, an exposure metric, a severity metric, and/or a controllability metric may be determined. A safety goal can be determined from the metric(s) and vehicle control parameters are determined to comport with the safety goal. The vehicle control parameters may be used to control an autonomous vehicle in the environment.

Safe vehicle operation using log data

Performance anomalies in autonomous vehicle can be difficult to identify, and the impact of such anomalies on systems within the autonomous vehicle may be difficult to understand. In examples, systems of the autonomous vehicle are modeled as nodes in a probabilistic graphical network. Probabilities of data generated at each of the nodes is determined. The probabilities are used to determine capabilities associated with higher level functions of the autonomous vehicle.

Methods and systems to assess vehicle capabilities

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Redwood City, CA, United States of America

Thomas Bernard Gacka