Gm Cruise Holdings LLC

Tesla, Inc.

Nimish Patil

Geoffrey Louis Chi-Johnston

Laura Athena Freeman

Vishal Suresh Vaingankar

Antony Joseph

Alex Green

Described herein are technologies relating to computing a likelihood of an operation-influencing event with respect to an autonomous vehicle at a geographic location. The likelihood of the operation-influencing event is computed based upon a prediction of a value that indicates whether, through a causal process, the operation-influencing event is expected to occur. The causal process is identified by means of a model, which relates spatiotemporal factors and the operation-influencing events.

Identifying a route for an autonomous vehicle between an origin and destination location

A system for monitoring stress cycles includes memory storing a base value and an inflection value of a stress cycle and one or more processors coupled to the memory. The one or more processors are configured to receive a series of stress values from a stress sensor. For each stress value in the series of stress values, the one or more processors are configured to perform operations including performing a first comparison between the stress value and a previous stress value in the series of stress values, detecting an inflection in the series of stress values based on the first comparison, updating the base value and the inflection value in response to detecting the inflection, performing a second comparison between the stress value and the base value, determining whether the stress cycle is complete based on the second comparison, and recording the stress cycle in response to determining that the stress cycle is complete.

System and method for monitoring stress cycles

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Pleasanton, CA, United States of America

Nimish Patil