Safe Reinforcement Learning for Autonomous Lane Changing Using Set-Based Prediction
Year: 2020
Type: conference-paper
Abstract: Machine learning approaches often lack safety guarantees, which are often a key requirement in real-world tasks. This paper addresses the lack of safety guarantees by extending reinforcement learning with a safety layer that restricts the action space to the subspace of safe actions. We demonstrate the proposed approach using lane changing in autonomous driving. To distinguish safe actions from unsafe ones, we compare planned motions with the set of possible occupancies of traffic participants generated by set-based predictions. In situations where no safe action exists, a verified failsafe controller is executed. We used real-world highway traffic data to train and test the proposed approach. The evaluation result shows that the proposed approach trains agents that do not cause collisions during training and deployment.
Authors: Hanna Krasowski, Xiao Wang, Matthias Althoff
Institution: Technical University of Munich
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3115737827