# Safe Reinforcement Learning with Mixture Density Network: A Case Study in Autonomous Highway Driving

arXiv: https://arxiv.org/abs/2007.01698 · HTML: https://mlsanity.com/paper/2007.01698/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Structure in Deep Reinforcement Learning: A Survey and Open Problems](https://mlsanity.com/paper/2306.16021/) (2023)
- [A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms](https://mlsanity.com/paper/2201.12819/) (2022)

## Abstract (arXiv)

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.