Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge. A number of authors have attempted to address this issue, but there are few publicly-available tools to adequately explore the trade-offs between functionality, scalability, and safety. We thus present WiseMove, a software framework to investigate safe deep reinforcement learning in the context of motion planning for autonomous driving. WiseMove adopts a modular learning architecture that suits our current research questions and can be adapted to new technologies and new questions. We present the details of WiseMove, demonstrate its use on a common traffic scenario, and describe how we use it in our ongoing safe learning research.
@article{arxiv.1902.04118,
title = {WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving},
author = {Jaeyoung Lee and Aravind Balakrishnan and Ashish Gaurav and Krzysztof Czarnecki and Sean Sedwards},
journal= {arXiv preprint arXiv:1902.04118},
year = {2021}
}