Collision Probability Distribution Estimation via Temporal Difference Learning
<|reference_start|>Collision Probability Distribution Estimation via Temporal Difference Learning: 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.<|reference_end|>
@article{steinecker2024collision,
    title={Collision Probability Distribution Estimation via Temporal Difference
  Learning},
    author={Thomas Steinecker, Thorsten Luettel and Mirko Maehlisch},
    journal={arXiv preprint arXiv:2407.20000},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20000},
    primaryClass={cs.RO cs.LG}
}