  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.
