Uatc, LLC

Uber Technologies, Inc.

Andrew Raymond Sturges

Richard Brian Donnelly

Bryan John Nagy

Jeff Schneider

Xiaodong Zhang

Yifang Liu

Alexander Edward Chao

Collin Christopher Otis

Sri Kanajan

David McAllister Bradley

Dima Kislovskiy

An autonomous vehicle includes one or more sensors for detecting an object in an environment surrounding the autonomous vehicle and a vehicle computing system comprising one or more processors receiving canonical route data associated with at least one canonical route, and controlling travel of the autonomous vehicle based on sensor data from the one or more sensors and the canonical route data associated with the at least one canonical route. The at least one canonical route comprises at least one roadway connected with another roadway in a plurality of roadways in a geographic location that satisfies at least one route optimization function derived based on trip data associated with one or more traversals of the plurality of roadways in a geographic location by one or more autonomous vehicles.

Determining autonomous vehicle routes

Techniques are provided for determining where to position vehicles for trip optimization or where to map roads for use by autonomous or semi-autonomous vehicles. The techniques include identifying, from historical trip data, common pickup and drop-off points within a geographical area where respective geohashes are used as nodes in the geographical area. A number of trips between respective nodes in the geographical area within a predetermined time frame define edges between respective nodes in the geographic area. The nodes and edges for the geographic area are processed to score each node to identify most active nodes within the geographic area as potential pickup/drop-off zones. The top k potential pickup-drop-off zones are evaluated for suitability as a pickup/drop-off zone, and lane IDs, suitable pickup/drop-off zones, and/or trip lists derived from the historical trip data are provided for use in positioning vehicles or mapping roads.

Autonomous vehicle positioning for trip optimization

An on-demand transportation management system can collect historical data of harmful events of human-driven vehicles (HDVs) operating throughout a given region. For each road segment of the given region, the system can determine a fractional risk value for the HDVs, and based on the fractional risk value for each road segment, the system can route drivers within the given region along lowest risk route options.

Generalized risk routing for human drivers

A method for determining a canonical route includes receiving trip data associated with one or more traversals of a plurality of roadways in a geographic location by one or more autonomous vehicles. The method includes generating at least one canonical route based on the trip data, wherein the at least one canonical route includes at least one roadway connected with another roadway in the plurality of roadways. The method includes providing canonical route data associated with the at least one canonical route to an autonomous vehicle for controlling travel of the autonomous vehicle on the at least one canonical route.

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San Francisco, CA, United States of America

Andrew Raymond Sturges