# Safe Reinforcement Learning on Autonomous Vehicles

arXiv: https://arxiv.org/abs/1910.00399 · HTML: https://mlsanity.com/paper/1910.00399/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

## Built on by (most cited first, 30 of 50)

- [A Review of Safe Reinforcement Learning: Methods, Theory and Applications](https://mlsanity.com/paper/2205.10330/) (2022)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [CAT: Closed-loop Adversarial Training for Safe End-to-End Driving](https://mlsanity.com/paper/2310.12432/) (2023)
- [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://mlsanity.com/paper/2107.12045/) (2021)
- [Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopes](https://mlsanity.com/paper/2210.10691/) (2022)
- [Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning](https://mlsanity.com/paper/2310.03718/) (2023)
- [State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2207.04361/) (2022)
- [Learning Adversarial MDPs with Stochastic Hard Constraints](https://mlsanity.com/paper/2403.03672/) (2024)
- [Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach](https://mlsanity.com/paper/2302.00171/) (2023)
- [Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback](https://mlsanity.com/paper/2307.02842/) (2023)
- [GUARD: A Safe Reinforcement Learning Benchmark](https://mlsanity.com/paper/2305.13681/) (2023)
- [A Survey of Safe Reinforcement Learning and Constrained MDPs: A Technical Survey on Single-Agent and Multi-Agent Safety](https://mlsanity.com/paper/2505.17342/) (2025)
- [Optimal Strong Regret and Violation in Constrained MDPs via Policy Optimization](https://mlsanity.com/paper/2410.02275/) (2024)
- [CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Models](https://mlsanity.com/paper/2502.15119/) (2025)
- [A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles](https://mlsanity.com/paper/2105.14218/) (2021)
- [Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning](https://mlsanity.com/paper/2107.07316/) (2021)
- [Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2201.06539/) (2022)
- [Gradient Shaping for Multi-Constraint Safe Reinforcement Learning](https://mlsanity.com/paper/2312.15127/) (2023)
- [A Best-of-Both-Worlds Algorithm for Constrained MDPs with Long-Term Constraints](https://mlsanity.com/paper/2304.14326/) (2023)
- [Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic](https://mlsanity.com/paper/2202.09644/) (2022)
- [SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving](https://mlsanity.com/paper/2206.08528/) (2022)
- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (2024)
- [Provably Efficient CVaR RL in Low-rank MDPs](https://mlsanity.com/paper/2311.11965/) (2023)
- [An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints](https://mlsanity.com/paper/2505.21841/) (2025)
- [Learning Constrained Markov Decision Processes With Non-stationary Rewards and Constraints](https://mlsanity.com/paper/2405.14372/) (2024)
- [Safe Deep Q-Network for Autonomous Vehicles at Unsignalized Intersection](https://mlsanity.com/paper/2106.04561/) (2021)
- [Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning](https://mlsanity.com/paper/2401.00629/) (2024)
- [Safety Optimized Reinforcement Learning via Multi-Objective Policy Optimization](https://mlsanity.com/paper/2402.15197/) (2024)
- [Critic as Lyapunov function (CALF): a model-free, stability-ensuring agent](https://mlsanity.com/paper/2409.09869/) (2024)
- [A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems](https://mlsanity.com/paper/2205.04590/) (2022)

## Abstract (arXiv)

There have been numerous advances in reinforcement learning, but the typically unconstrained exploration of the learning process prevents the adoption of these methods in many safety critical applications. Recent work in safe reinforcement learning uses idealized models to achieve their guarantees, but these models do not easily accommodate the stochasticity or high-dimensionality of real world systems. We investigate how prediction provides a general and intuitive framework to constraint exploration, and show how it can be used to safely learn intersection handling behaviors on an autonomous vehicle.