Toyota Motor Engineering & Manufacturing North America, Inc.

Jianyu Su

Rui Guo

Roger D Melen

Kyungtae Han

Ziran Wang

Systems and methods of cooperative autonomous driving which maximize objectives of a global traffic environment are provided. In particular, a cooperative controller may control multiple connected vehicles within a traffic environment. In certain embodiments, the cooperative controller may assign each connected vehicle a traffic role, based in part on their situation within the traffic environment. In some embodiments, this traffic role may also be based on a 'priority level' for the connected vehicle which corresponds to a desired travel time. Once traffic roles have been assigned, the cooperative controller may control each connected vehicle according to a driving policy associated with its assigned traffic role.

Cooperative artificial intelligent assisted driving

Systems and methods for providing driving recommendations are disclosed herein. One embodiment receives, at an ego vehicle, first vehicle data and first encoded information from one or more other vehicles; constructs, from the first vehicle data, graph data representing how the ego vehicle and the one or more other vehicles are spatially related; inputs the graph data, the first vehicle data, second vehicle data pertaining to the ego vehicle, and the first encoded information to a graph convolutional network that outputs second encoded information; inputs the second encoded information and previously stored encoded information to a recurrent neural network that outputs a set of parameters to a mixture model; predicts acceleration of the one or more other vehicles using the mixture model; and generates a driving recommendation for the ego vehicle based, at least in part, on the predicted acceleration of the one or more other vehicles.

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Charlottesville, VA, United States of America

Jianyu Su