An End-to-End Autonomous Vehicle Trained Entirely in simulation that can Drive in the Real World

Non-Exclusively Licensed

A controller for an autonomous vehicle is trained using simulated paths on a roadway and simulated observations that are formed by transforming images previously acquired on similar paths on that roadway. Essentially an unlimited number of paths may be simulated, enabling optimization approaches including reinforcement learning to be applied to optimize the controller.

Researchers

Daniela Rus / Jacob Phillips / Julia Moseyko / Alexander Amini / Igor Gilitschenski / Sertac Karaman

Departments: Dept of Electrical Engineering & Computer Science, Computer Science & Artificial Intelligence Lab, Department of Aeronautics and Astronautics
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Computer Science: Networking & Signals / Industrial Engineering & Automation: Autonomous Systems

  • simulation-based training of an autonomous vehicle
    United States of America | Published application

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