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arXiv · 1904.11483

Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments

Maxime Bouton·Alireza Nakhaei·Kikuo Fujimura·Mykel J. Kochenderfer
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Abstract

Navigating urban environments represents a complex task for automated vehicles. They must reach their goal safely and efficiently while considering a multitude of traffic participants. We propose a modular decision making algorithm to autonomously navigate intersections, addressing challenges of existing rule-based and reinforcement learning (RL) approaches. We first present a safe RL algorithm relying on a model-checker to ensure safety guarantees. To make the decision strategy robust to perception errors and occlusions, we introduce a belief update technique using a learning based approach. Finally, we use a scene decomposition approach to scale our algorithm to environments with multiple traffic participants. We empirically demonstrate that our algorithm outperforms rule-based methods and reinforcement learning techniques on a complex intersection scenario.

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Maxime Bouton, Alireza Nakhaei, Kikuo Fujimura, Mykel J. Kochenderfer. 2019-04-25. Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments. https://arxiv.org/abs/1904.11483

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