Gm Cruise Holdings LLC

Abhishek Jain

Lucio Otavio Marchioro Rech

Hooman Barekatain

Saranya Konala

Hemant Kavadia

Kyle Liang

Nishant Sharma

Ryan Zelen

Jeremy Allan

Adrian Kit Malaran

Approaches to managing navigation of autonomous vehicles through minor-major intersections are disclosed. A minor-major intersection is detected. Shadow tracks are generated for occlusions associated with the minor-major intersection. A maneuver evaluation is performed based on induced kinematic discomfort and post-encroachment time. Preliminary lateral and longitudinal planning is performed based on shadow tracks, induced kinematic discomfort and post-encroachment time to generate one or more proposed trajectories. Final trajectory generation is performed by refining at least one of the one or more proposed trajectories to generate the final trajectory. The autonomous vehicle performs a selected maneuver corresponding to the final trajectory within the minor-major intersection.

Navigating minor-major intersections with an autonomous vehicle

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

Systems and methods can improve passenger safety for an Autonomous Vehicle (AV) based on the integration of sensor data captured by the AV's interior and exterior sensors. The AV can determine passenger occupancy data corresponding to where each passenger is detected within the AV by the interior sensors. The AV can determine multiple sets of one or more driving actions that the AV can perform at a future time. The AV can generate crash impact data corresponding to where each passenger is detected from one or more simulated collisions between the AV and one or more objected detected by the exterior sensors when the AV performs one or more sets of driving actions from among the multiple sets. The AV can determine ranked sets of driving actions based on the passenger occupancy data and the crash impact data.

Advanced passenger safety for an autonomous vehicle

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San Francisco, CA, United States of America

Abhishek Jain