Collision Probability Distribution Estimation via Temporal Difference Learning

TL;DR

CollisionPro is a reinforcement learning framework that estimates collision probability distributions in autonomous driving, offering explainability, high sample efficiency, and reliable predictions for unseen events.

Contribution

It introduces CollisionPro, a novel RL-based method for collision probability estimation, addressing limitations of traditional model-based approaches and enhancing safety in autonomous systems.

Findings

High sample efficiency demonstrated in simulations

Reliable prediction of unseen collision events

Potential applications in safety alert systems

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.