Honda Motor Co., Ltd.

Manish Saroya

Kikuo Fujimura

David Francis Isele

Sangjae Bae

Alexandre Miranda Anon

Alexandre Miranda Añon

Markus Schiffer

Systems and methods for training a neural network for generating a reasoning statement are provided. In one embodiment, a method includes receiving raw data regarding a raw simulation for an ego agent. The raw simulation includes at least one dynamic object. The method also includes generating a noisy simulation by applying a first input noise of a plurality of input noises to the raw simulation. The method further includes predicting one or more object trajectories for the at least one dynamic object. The method yet further includes generating an ego trajectory for the ego agent in the noisy simulation based on the one or more object trajectories. The method includes causing the ego agent to execute the ego trajectory.

Systems and methods for including simulation noise in path planning

A method for generating operable driving areas for an autonomous driving vehicle based on a path trajectory of the autonomous driving vehicle is provided. The method may form a space time (ST) graph indicating a distance of travel along the path trajectory with respect to time of the autonomous driving vehicle and path trajectories of devices intersecting with the path trajectory of the autonomous driving vehicle. The method may segment the ST graph into cells, wherein viable cells represent discretized viable unoccupied spaces in the ST graph. The method may find passage ways for the autonomous driving vehicle based on the viable cells. The method may select a desired passage way using quadratic programming (QP) optimization when multiple passage ways are found.

Multi-profile quadratic programming (MPQP) for optimal gap selection and speed planning of autonomous driving

A method for generating operable driving areas for an autonomous driving vehicle based on a path trajectory of the autonomous driving vehicle is provided. The method may form a space time (ST) graph indicating a distance of travel along the path trajectory with respect to time of the autonomous driving vehicle and path trajectories of devices intersecting with the path trajectory of the autonomous driving vehicle, wherein the path trajectory of each device is based on current and historical data for each device. The method may segment the ST graph into cells, wherein viable cells represent discretized viable unoccupied spaces in the ST graph. The method may find passage ways for the autonomous driving vehicle based on the viable cells. The method may select a desired passage way using quadratic programming (QP) optimization when multiple passage ways are found.

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Santa Clara, CA, United States of America

Manish Saroya