Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation

Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation

In this work, we present a data-driven simulation and training engine capable of learning end-to-end autonomous vehicle control policies using only sparse rewards. By leveraging real, human-collected trajectories through an environment, we render novel training data that allows virtual agents to dri...

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Bibliographic Details
Main Authors: Amini, Alexander A, Gilitschenski, Igor, Phillips, Jacob, Moseyko, Julia, Banerjee, Rohan, Karaman, Sertac, Rus, Daniela L
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
Online Access:https://hdl.handle.net/1721.1/130456

Internet

https://hdl.handle.net/1721.1/130456

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