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WiseMove: A Framework to Investigate Safe Deep Reinforcement Learning for Autonomous Driving

  • Jaeyoung Lee
  • Aravind Balakrishnan
  • Ashish Gaurav
  • Krzysztof CzarneckiEmail author
  • Sean Sedwards
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11785)

Abstract

WiseMove is a platform to investigate safe deep reinforcement learning (DRL) in the context of motion planning for autonomous driving. It adopts a modular architecture that mirrors our autonomous vehicle software stack and can interleave learned and programmed components. Our initial investigation focuses on a state-of-the-art DRL approach from the literature, to quantify its safety and scalability in simulation, and thus evaluate its potential use on our vehicle.

Notes

Acknowledgment

This work is supported by the Japanese Science and Technology agency (JST) ERATO project JPMJER1603: HASUO Metamathematics for Systems Design, and by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant: Model-Based Synthesis and Safety Assurance of Intelligent Controllers for Autonomous Vehicles.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jaeyoung Lee
    • 1
  • Aravind Balakrishnan
    • 1
  • Ashish Gaurav
    • 1
  • Krzysztof Czarnecki
    • 1
    Email author
  • Sean Sedwards
    • 1
  1. 1.University of WaterlooWaterlooCanada

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