Gm Global Technolgoy Operations, Inc.

Aashish N Patel

Rajan Bhattacharyya

Amir M Rahimi

Hyukseong Kwon

Srinivas Nedunuri

Embodiments include methods, systems, and computer readable storage medium for a method for providing path-planning guidance by resolving multiple behavioral predictions associated with operating a vehicle is disclosed. The method includes installing a vehicle system into a vehicle, wherein the vehicle system provides path planning guidance based on training data using and fused hypotheses and/or decisions associated with the training data. The method further includes determining, by a processor, a location of the vehicle on a map containing a road network, and determining, by the processor, whether one or more agents exist within a predetermined range of the vehicle. The method further includes selecting, by the processor, an output trajectory to traverse the road network based on the location of the vehicle on the map and the existence of one or more agents. The method further includes controlling, by the processor, operation of the vehicle using the output trajectory.

Path-planning fusion for a vehicle

A method, autonomous vehicle and system for operating an autonomous vehicle. A sensor obtains data of an agent. A processor determines a measure of complexity of the environment in which the autonomous vehicle is operating from the sensor data, selects a control scheme for operating the autonomous vehicle based on the determined complexity, and operates the autonomous vehicle using the selected control scheme.

Situational complexity quantification for autonomous systems

Embodiments include methods, systems and computer readable storage medium for a method for collision avoidance by a vehicle is disclosed. The method includes installing a vehicle system into a vehicle, wherein the vehicle system provides collision avoidance guidance based on training data using movement information from one or more agents and behaviors associated with one or more individuals associated with the one or more agents or the vehicle. The method further includes detecting, by a processor, a collision course between the vehicle and the one or more mobile agents and/or one or more stationary agents. The method further includes calculating, by the processor, one or more decisions that avoid a collision in response to detecting a collision course. The method further includes selecting, by the processor, a decision from the one or more decisions and controlling, by the processor, operation of the vehicle based on the selected decision.

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Los Angeles, CA, United States of America

Aashish N Patel