Gm Cruise Holdings LLC

Hooman Barekatain

Nestor Grace

Lucio Otavio Marchioro Rech

Abhishek Jain

Saranya Konala

Ryan Zelen

Kyle Liang

Diego Plascencia-Vega

Dogan Gidon

Srishti Iyer Koduvayur

Katelyn Mary Wolfenberger

Weichao Kong

Danyan Qian

Systems, methods, and computer-readable media are provided for analyzing a traffic infrastructure element, determining reflectivity information of the traffic infrastructure element based on the analyzing of the traffic infrastructure element, comparing the reflectivity information of the traffic infrastructure element with semantic map information of the traffic infrastructure element, and providing instructions to an autonomous vehicle based on the comparing of the reflectivity information of the traffic infrastructure element with the semantic map information of the traffic infrastructure element.

Utilization of reflectivity to determine changes to traffic infrastructure elements

A method includes determining that an object has fallen to a surface of interest in an interior cabin of a vehicle during a transportation service provided using the vehicle; tracking a location of the object on the surface of interest using at least one interior sensor; updating tracking information for the object, the tracking information including the tracked location of the object and a time at which the location was tracked; inferring a continued existence of the object after the object has moved out of a view of the at least one interior sensor and tagging the object as obscured from view in the tracking information for the object; identifying a user associated with the transportation service; and alerting the identified user to retrieve the object.

Lost object tracking system

Various technologies described herein pertain to autonomous vehicle consumption of real-time public transportation data to guide curb access and usage. An autonomous vehicle receives a trip request for a ride specifying a requested pullover location. The autonomous vehicle receives public transportation data specifying an expected arrival time of a public transportation vehicle at a reserved zone within proximity of the requested pullover location. The autonomous vehicle evaluates availability of the reserved zone during an expected occupancy time of the reserved zone by the autonomous vehicle based on the expected arrival time of the public transportation vehicle at the reserved zone. The autonomous vehicle selects an actual pullover location for the ride in the autonomous vehicle based on the availability of the reserved zone during the expected occupancy time. The autonomous vehicle stops at the actual pullover location for the ride in the autonomous vehicle.

Autonomous vehicle consumption of real-time public transportation data to guide curb access and usage

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San Francisco, CA, United States of America

Hooman Barekatain