Gm Cruise Holdings LLC

Gm Global Technolgoy Operations, Inc.

Daniel Chen

Eric Lujan

Shiva Ghose

Curt Harrington

Ryan Holben

Ted Nitz

Christopher Daily-Diamond

Mason Swofford

Christophe Philippona

Benjamin Greer

Francesco Capodieci

Jeremy Allan

Yu-Cheng Lin

Sohrab Haghighat

An autonomous vehicle (AV) is described herein. The AV is configured to identify an occluded region where a portion of a field of view of a sensor is occluded by an object. The AV is further configured to hypothesize that an object exists in the occluded region and is moving in the occluded region. The AV is still further configured to perform a driving maneuver based upon the hypothesized object existing in the occluded region.

Autonomous vehicle maneuvering based upon risk associated with occluded regions

Described herein are technologies relating to controlling an autonomous vehicle based upon a computed lane boundary. The computed lane boundary defines a travel path for the autonomous vehicle when the autonomous vehicle turns across a lane of traffic at an intersection of two or more roadways. The autonomous vehicle navigates turn based upon the computed lane boundary, which allows the autonomous vehicle to enter the intersection without crossing into a lane of oncoming traffic.

Controlling an autonomous vehicle based upon computed lane boundaries

Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving, by a processor, a lane plan; determining, by a processor, from the lane plan an upcoming turn; determining, by a processor, if the upcoming turn is at least one of an unprotected left turn, an inside right turn, and an inside left turn; when at least one of an unprotected left turn, an inside right turn, and an inside left turn is determined, modifying, by a processor, a lane boundary; and controlling the vehicle based on the modified lane boundary.

Relaxable turn boundaries for autonomous vehicles

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San Francisco, CA, United States of America

Daniel Chen