Waymo LLC

Peter Strohm

William Grossman

Choon Ping Chng

Vijaysai Patnaik

Cheng-Han Wu

Ganesh Balachandran

A method includes identifying route data including a threshold arrival time for a route for an autonomous vehicle (AV) and calculating, based on the route data and a fuel-efficient speed value for each segment of the route, an estimated arrival time. Responsive to the estimated arrival time not meeting the threshold arrival time, the method includes identifying at least a subset of segments that each represent a candidate for speed increase, computing, for each segment in the subset and based on the fuel economy data, a correlation metric that indicates a correlation between a change in fuel economy and a change in speed for a corresponding segment in the subset, and increasing, for at least one segment from the subset and based on a respective correlation metric, a fuel-efficient speed value of the corresponding segment from the subset to provide a speed profile reflecting the increased fuel-efficient speed value.

Fuel-economy optimization for autonomous driving systems

The technology relates to cargo vehicles. National, regional and/or local regulations set requirements for operating cargo vehicles, including how to distribute and secure cargo, and how often the cargo should be inspected during a trip. However, such regulations have been focused on traditional human-driven vehicles. Aspects of the technology address various issues involved with securement and inspection of cargo before a trip, as well as monitoring during the trip so that corrective action may be taken as warranted. For instance, imagery and other sensor information may be used to enable proper securement of cargo before starting a trip. Onboard sensors along the vehicle monitor the cargo and securement devices/systems during the trip to identify issues as they arise. Such information is used by the onboard autonomous driving system (or a human driver) to take corrective action depending on the nature of the issue.

Cargo inspection, monitoring and securement in self-driving trucks

A system includes multiple microphone arrays positioned at different locations on a roof of an autonomous vehicle. Each microphone array includes two or more microphones. Internal clocks of each microphone array are synchronized by a processor and used to generate timestamps indicating when microphones capture a sound. Based on the timestamps, the processor is configured to localize a source of the sound.

External microphone arrays for sound source localization

Systems and methods for automated vehicle sensor calibration and verification are provided. One example method involves monitoring a vehicle using one or more external sensors of a vehicle calibration facility. The sensor data may be indicative of a relative position of the vehicle in the vehicle calibration facility. The method also involves causing the vehicle to navigate in an autonomous driving mode, based on the sensor data, from a current position of the vehicle to a first calibration position in the vehicle calibration facility. The method also involves causing a first sensor of the vehicle to perform a first calibration measurement while the vehicle is at the first calibration position. The method also involves calibrating the first sensor based on at least the first calibration measurement.

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Stanford, CA, United States of America

Peter Strohm