Collision Probability Distribution Estimation via Temporal Difference Learning

• URL: http://arxiv.org/abs/2407.20000v1
• Date: Mon, 29 Jul 2024 13:32:42 GMT
• Title: Collision Probability Distribution Estimation via Temporal Difference Learning
• Authors: Thomas Steinecker, Thorsten Luettel, Mirko Maehlisch,
• Abstract summary: We introduce CollisionPro, a pioneering framework designed to estimate cumulative collision probability distributions. We formulate our framework within the context of reinforcement learning to pave the way for safety-aware agents. A comprehensive examination of our framework is conducted using a realistic autonomous driving simulator.
• Score: 0.46085106405479537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce CollisionPro, a pioneering framework designed to estimate cumulative collision probability distributions using temporal difference learning, specifically tailored to applications in robotics, with a particular emphasis on autonomous driving. This approach addresses the demand for explainable artificial intelligence (XAI) and seeks to overcome limitations imposed by model-based approaches and conservative constraints. We formulate our framework within the context of reinforcement learning to pave the way for safety-aware agents. Nevertheless, we assert that our approach could prove beneficial in various contexts, including a safety alert system or analytical purposes. A comprehensive examination of our framework is conducted using a realistic autonomous driving simulator, illustrating its high sample efficiency and reliable prediction capabilities for previously unseen collision events. The source code is publicly available.

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