Waymo LLC

Christopher Bowen

David Smith

Nathaniel Fairfield

Benjamin Pitzer

Austin Daniel Abrams

David W Smith

Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling routing of an autonomous vehicles (AV) by identifying routes from a first location to a second location, identifying a target efficiency value of autonomous driving along a respective route, determining, in view of historical data for the respective route and using one or more randomized conditions, a confidence level associated with the target efficiency value, selecting, based on the target efficiency values and the associated confidence level, a preferred route, and causing the AV to select the first route for travel from the first location to the second location.

Route optimization for autonomous driving systems

A control command is provided to a vehicle control module that identifies one or more vehicle actions to be performed by the vehicle control module to control a position of an autonomous vehicle (AV) with respect to an external environment. Whether one or more conditions pertaining to the position of the AV with respect to the external environment are satisfied is determined. Responsive to determining that the one or more conditions pertaining to the position of the AV with respect to the external environment are satisfied, a first instruction is provide to the vehicle control module that permits the vehicle control module to deviate from the one or more vehicle actions identified in the control command and to perform an energy saving action with respect to the AV.

Energy saving actions for autonomous driving systems

The technology relates to route planning and performing driving operations in autonomous vehicles, such as cargo trucks, articulating buses, as well as other vehicles. A detailed kinematic model of the vehicle in evaluated in conjunction with roadgraph and other information to determine whether a route or driving operation is feasible for the vehicle. This can include evaluating a hierarchical set of driving rules and whether current driving conditions impact any of the rules. Driving trajectories and cost can be evaluated when pre-planning a route for the vehicle to follow. This can include determining an ideal trajectory for the vehicle to take a particular driving action. Pre-planned routes may be shared with a fleet of vehicles, and can be modified based on information obtained by different vehicles of the fleet.

Kinematic model for autonomous truck routing

The technology relates to determining whether a vehicle operating in an autonomous driving mode is experiencing an anomalous condition, for instance due to a loss of tire pressure, a mechanical failure, or a shift or loss of cargo. The actual current pose of the vehicle is compared to an expected pose of the vehicle, where the expected pose is based on a model of the vehicle. If a pose discrepancy is identified, the anomalous condition is determined from information associated with the pose discrepancy. The vehicle is then able to take corrective action based on the nature of the anomalous condition. The corrective action may include making a real-time driving change, modifying a planned route, alerting a remote operations center, or communicating with one or more other vehicles.

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Mountain View, CA, United States of America

Christopher Bowen