Waymo LLC

Google Inc.

Uber Technologies, Inc.

Uatc, LLC

Donald Jason Burnette

David Ian Ferguson

Nathaniel Fairfield

Dmitri A Dolgov

Andreas Wendel

Jens-Steffen Ralf Gutmann

Andrew Hughes Chatham

Bryan John Nagy

Eric Michael Perko

Lisa Christine Weitekamp

Adam Henry Polk Milstein

Jiajun Zhu

Daniel Trawick Egnor

Michael Steven Montemerlo

Matthew Paul McNaughton

Aspects of the disclosure relate to determining whether a vehicle should continue through an intersection. For example, the one or more of the vehicle's computers may identify a time when the traffic signal light will turn from yellow to red. The one or more computers may also estimate a location of a vehicle at the time when the traffic signal light will turn from yellow to red. A starting point of the intersection may be identified. Based on whether the estimated location of the vehicle is at least a threshold distance past the starting point at the time when the traffic signal light will turn from yellow to red, the computers can determine whether the vehicle should continue through the intersection.

Traffic signal response for autonomous vehicles

Aspects of the present disclosure relate to differentiating between active and inactive construction zones. In one example, this may include identifying a construction object associated with a construction zone. The identified construction object may be used to map the area of the construction zone. Detailed map information may then be used to classify the activity of the construction zone. The area of the construction zone and the classification may be added to the detailed map information. Subsequent to adding the construction zone and the classification to the detailed map information, the construction object (or another construction object) may be identified. The location of the construction object may be used to identify the construction zone and classification from the detailed map information. The classification of the classification may be used to operate a vehicle having an autonomous mode.

Mapping active and inactive construction zones for autonomous driving

A method for receiving autonomous vehicle (AV) driving path data associated with a driving path in a roadway of a geographic location. The driving path data associated with a trajectory for an AV in a roadway and trajectory points in a trajectory of the driving path in the roadway for determining at least one feature of the roadway positioned a lateral distance from a first trajectory of the one or more trajectories of the driving path of an AV based on the map data. The method includes receiving map data associated with a map of a geographic location, determining a driving path for an AV in a roadway, generating driving path information based on a trajectory point in a trajectory of the driving path, and providing driving path data associated with the driving path to an AV for controlling the AV on the roadway.

Mapped driving paths for autonomous vehicle

A vehicle can include throttle, braking, and steering systems. The vehicle can further include a computing system that obtains, from one or more sensors, data representing one or more of a velocity or an acceleration of the vehicle. The computing system can further determine an estimated weight of the vehicle based on the one or more of the velocity or the acceleration of the vehicle, and autonomously operate the throttle, braking, and steering systems of the vehicle based on the estimated weight of the vehicle.

Vehicle controls based on the measured weight of freight

A computing system determines an estimated weight of a vehicle by measuring kinematic data of the vehicle, including at least one of a velocity or an acceleration of the vehicle. The computing system processes the data to determine an estimated weight of the vehicle. Based on the estimated weight of the vehicle, the computing system can autonomously operate the throttle, braking, and steering systems of the vehicle.

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Mountain View, CA, United States of America

Donald Jason Burnette