  This paper presents a safe reinforcement learning system for automated
driving that benefits from multimodal future trajectory predictions. We propose
a safety system that consists of two safety components: a heuristic safety and
a learning-based safety. The heuristic safety module is based on common driving
rules. On the other hand, the learning-based safety module is a data-driven
safety rule that learns safety patterns from driving data. Specifically, it
utilizes mixture density recurrent neural networks (MD-RNN) for multimodal
future trajectory predictions to accelerate the learning progress. Our
simulation results demonstrate that the proposed safety system outperforms
previously reported results in terms of average reward and number of
collisions.
