Gm Cruise Holdings LLC

Curt Harrington

Christopher Daily-Diamond

Ted Nitz

Eric Lujan

Shiva Ghose

Ryan Holben

Daniel Chen

Mason Swofford

Christophe Philippona

Benjamin Greer

Francesco Capodieci

Christopher Charles Ward

Kunal Mehta

Devina Jain

Edward Henry Forscher

Wei Lu

Nikola John Linn Noxon

Kelan Fitzgerald Stoy

William Anthony Silva

Nikola Noxon

Michael John Mehallow

A method is described and includes acquiring sensor data produced by sensors of a plurality of vehicles traversing an area including a location of a user, wherein the vehicles traversing the area comprise a subset of a fleet of vehicles for providing rideshare services; processing the acquired sensor data to determine a category of the user; selecting a vehicle from the fleet of vehicles based on the category of the user, wherein the selected vehicle comprises at least one accommodation corresponding to the category of the user; and dispatching the selected vehicle to a pick-up location designated by the user.

Rideshare service fleet optimization using vehicle sensor data

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

Systems and techniques are provided for vehicle safety technologies. An example method can include determining, based on sensor data captured by an autonomous vehicle, a first position of a different vehicle and a second position of one or more objects; determining, based on the first position of the different vehicle and the second position of the one or more objects, a risk of a collision between the different vehicle and the one or more objects; and based on a determination that the risk of the collision exceeds a threshold, outputting an alert representing a warning of the collision between the different vehicle and the one or more objects, the alert being directed to at least one of the different vehicle and the one or more objects.

Predictive threat warning system

Various technologies described herein pertain to reserving seats in an autonomous vehicle. The autonomous vehicle includes a plurality of seats that can be occupied by passengers when riding in the autonomous vehicle. The autonomous vehicle is configured to assign a particular seat from the plurality of seats in the autonomous vehicle to a passenger based on information pertaining to the passenger and information pertaining to a ride-sharing trip. The autonomous vehicle is to transport at least the passenger and a differing passenger during at least a portion of the ride-sharing trip. Moreover, the passenger and the differing passenger independently request rides in the autonomous vehicle. The autonomous vehicle is further configured to output, to the passenger, an indication of the particular seat assigned to the passenger.

Autonomous vehicle seat reservation

An autonomous vehicle is configured to estimate a change in direction of a vehicle that is on a roadway and is proximate to the autonomous vehicle. The autonomous vehicle has a mechanical system, one or more sensors that generate one or more sensor signals, and a computing system in communication with the mechanical system and the one or more sensors. The autonomous vehicle is configured to detect an imminent lane change by another vehicle based on at least one of a computed angle between a wheel of the other vehicle and a longitudinal direction of travel of the other vehicle, a degree of misalignment between the wheel of the other vehicle and a body of the other vehicle, and/or an eccentricity of the wheel of the other vehicle. The mechanical system of the autonomous vehicle is controlled by the computing system based upon the detected imminent lane change.

Controlling an autonomous vehicle based upon a predicted imminent lane change

An autonomous vehicle is described, wherein the autonomous vehicle is configured to estimate a change in direction of a vehicle that is on a roadway and is proximate to the autonomous vehicle. The autonomous vehicle has a mechanical system, one or more sensors that generate one or more sensor signals, and a computing system in communication with the mechanical system and the one or more sensors. The autonomous vehicle is configured to detect an imminent lane change by another vehicle based on at least one of a computed angle between a wheel of the other vehicle and a longitudinal direction of travel of the other vehicle, a degree of misalignment between the wheel of the other vehicle and a body of the other vehicle, and/or an eccentricity of the wheel of the other vehicle. The mechanical system of the autonomous vehicle is controlled by the computing system based upon the detected imminent lane change.

An autonomous vehicle (AV) described herein is configured to receive a pull over location specified by a passenger, and is further configured to refine the pull over location based upon one or more factors, where the factors include computer-readable content from a profile of the passenger, sensor data output by sensor systems of the AV, observed or predicted weather conditions, observed or predicted traffic, and/or observations recently generated by other AVs that belong to the same fleet as the AV.

Updating a pick-up or drop-off location for a passenger of an autonomous vehicle

Growing community of inventors

San Francisco, CA, United States of America

Curt Harrington