# Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Driving

arXiv: https://arxiv.org/abs/1910.12905 · HTML: https://mlsanity.com/paper/1910.12905/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 21 of 21)

- [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://mlsanity.com/paper/2107.12045/) (2021)
- [A Survey on Reinforcement Learning in Aviation Applications](https://mlsanity.com/paper/2211.02147/) (2022)
- [Uncovering Instabilities in Variational-Quantum Deep Q-Networks](https://mlsanity.com/paper/2202.05195/) (2022)
- [An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients](https://mlsanity.com/paper/2305.00224/) (2023)
- [Risk-Aware Reinforcement Learning through Optimal Transport Theory](https://mlsanity.com/paper/2309.06239/) (2023)
- [Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2307.01316/) (2023)
- [SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning](https://mlsanity.com/paper/2104.06506/) (2021)
- [Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule](https://mlsanity.com/paper/2404.15751/) (2024)
- [Optimal Transport-Assisted Risk-Sensitive Q-Learning](https://mlsanity.com/paper/2406.11774/) (2024)
- [A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems](https://mlsanity.com/paper/2205.04590/) (2022)
- [PEARL: Preconditioner Enhancement through Actor-critic Reinforcement Learning](https://mlsanity.com/paper/2501.10750/) (2025)
- [Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement](https://mlsanity.com/paper/2412.10822/) (2024)
- [A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective](https://mlsanity.com/paper/2503.23650/) (2025)
- [Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages](https://mlsanity.com/paper/2103.09726/) (2021)
- [Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulator](https://mlsanity.com/paper/2211.10237/) (2022)
- [Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learning](https://mlsanity.com/paper/2305.00576/) (2023)
- [Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning](https://mlsanity.com/paper/2402.15893/) (2024)
- [Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey](https://mlsanity.com/paper/2501.01886/) (2025)
- [Towards Theoretical Understanding of Data-Driven Policy Refinement](https://mlsanity.com/paper/2305.06796/) (2023)
- [Demystifying the Physics of Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Making](https://mlsanity.com/paper/2403.11432/) (2024)
- [A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments](https://mlsanity.com/paper/2408.00997/) (2024)

## Abstract (arXiv)

In this paper, we present a safe deep reinforcement learning system for automated driving. The proposed framework leverages merits of both rule-based and learning-based approaches for safety assurance. Our safety system consists of two modules namely handcrafted safety and dynamically-learned safety. The handcrafted safety module is a heuristic safety rule based on common driving practice that ensure a minimum relative gap to a traffic vehicle. On the other hand, the dynamically-learned safety module is a data-driven safety rule that learns safety patterns from driving data. Specifically, the dynamically-leaned safety module incorporates a model lookahead beyond the immediate reward of reinforcement learning to predict safety longer into the future. If one of the future states leads to a near-miss or collision, then a negative reward will be assigned to the reward function to avoid collision and accelerate the learning process. We demonstrate the capability of the proposed framework in a simulation environment with varying traffic density. Our results show the superior capabilities of the policy enhanced with dynamically-learned safety module.