Learning an Explainable Trajectory Generator Using the Automaton Generative Network

A method of generating an output trajectory of an ego vehicle is described. The method includes extracting high-level features from a bird-view image of a traffic environment of the ego vehicle. The method also includes generating, using an automaton generative network, an automaton including an automaton state distribution describing a behavior of the ego vehicle in the traffic environment according to the high-level features. The method further includes generating the output trajectory of the ego vehicle according to extracted bird-view features of the bird-view image and the automaton state distribution describing the behavior of the ego vehicle in the traffic environment.

Researchers

Daniela Rus / Xiao Li / Cristian-Ioan Vasile / Igor Gilitschenski / Minoru Araki / Sertac Karaman / Guy Rosman

Departments: Dept of Electrical Engineering & Computer Science, Department of Aeronautics and Astronautics
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Computer Science: Networking & Signals
Impact Areas: Advanced Materials

  • method for learning an explainable trajectory generator using an automaton generative network
    United States of America | Granted | 12,024,203

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