Uatc, LLC

Aurora Operations, Inc.

Kenneth James Jensen

Eyal Cohen

Mark Calleija

Brian M Neil

Randol W Aikin

Edward Henry Lim

Soren Juelsgaard

Brian Neil

Mike Carter

Randol Aikin

Vehicles according to at least some embodiments of the disclosure include a sensor, and a computing device comprising at least one hardware processing unit. The computing device is programmed to perform operations comprising capturing an image with the sensor, identifying an object in the image, and in response to an accuracy of the identification meeting a first criterion, pre-loading a braking system of the autonomous vehicle. In some aspects, the computing device may predict that an object not currently within a path of the vehicle has a probability of entering the path of the vehicle that meets a second criterion. When the probability of entering the path meets the second criterion, some of the disclosed embodiments may pre-load the braking system.

Brake preload system for autonomous vehicles

Various examples are directed to systems and methods for operating a vehicle comprising a tractor and a trailer attached for pulling behind the tractor. A center-of-mass system may determine a mass of the trailer and a tractor understeer. The center-of-mass system may determine the tractor understeer using steering input data describing a steering angle of the tractor and yaw data describing a yaw of the tractor. The center-of-mass system may determine a load center of mass using the tractor understeer and a mass of the trailer. The center-of-mass system may further determine that the load center of mass transgresses a center-of-mass threshold and send an alert message indicating that the load transgresses the load center-of-mass threshold.

Determining vehicle load center of mass

Systems and methods for compensating for acceleration-related sensor mismatch of an autonomous vehicle are provided. A computing system for compensating for autonomous vehicle acceleration-related sensor mismatch can include one or more processors and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the computing system to perform operations. The operations can include obtaining data indicative of an acceleration mismatch between a first portion and a second portion of an autonomous vehicle. The operations can further include determining a sensor compensation action based at least in part on the data indicative of the acceleration mismatch. The operations can further include implementing the sensor compensation action for the autonomous vehicle.

Autonomous vehicle sensor compensation by monitoring acceleration

Disclosed are systems, methods, and computer-readable storage media to control a vehicle. In one aspect, a method includes capturing point-cloud data representative of a surrounding of an autonomous vehicle with one or more LIDAR sensors, identifying a point in the point cloud data as a non-matching point in response to the point having no corresponding point in a map used to determine a position of the autonomous vehicle, determining whether the non-matching point is to be used in a determination of an overlap score based on one or more comparisons of the point cloud data and the map, determining the overlap score in response to the determining whether the non-matching point is to be used in the determination of the overlap score, determining a position of the autonomous vehicle based on the overlap score and the map, and controlling the autonomous vehicle based on the position.

Systems, devices, and methods for autonomous vehicle localization

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Alameda, CA, United States of America

Kenneth James Jensen