mlsanityData to 14 Oct 2026

Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency

arXiv 2606.14609 ↗First posted 12 Jun 2026cs.ROcited by 0

On the map: End-to-end autonomous driving → Reinforcement learning for driving

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Abstract (arXiv)

Deep reinforcement learning (DRL) offers a compelling route to decision-making for advanced autonomous vehicles (AVs), yet its trial-and-error nature makes it difficult to guarantee safety during training and to achieve both safety and efficiency at deployment. We propose a unified safe reinforcement learning (SRL) framework that integrates safe distance (SD), reward machines (RM), and mixture-of-experts (MoE), termed MoE-RM-SRL. For deployment, SD and RM jointly shape a rule-aware reward that encodes highway traffic regulations and stage-wise objectives, enabling safe and reliable behavior without sacrificing efficiency. For training, we introduce a sparsely gated MoE layer comprising up to 11 deep Q-networks (DQNs); an SD-based gating rule activates a minimal set of experts for lane-keeping and lane-changing, mitigating the instability, discontinuities, and impulsive transients commonly induced by switching between heterogeneous controllers (e.g., MPC/rule-based modules and learned policies). We implement the proposed architecture in CARLA and integrate it with a 6-DoF driver-in-the-loop virtual-reality (DiL-VR) platform. Experiments in stochastic two-lane traffic show that MoE-RM-SRL substantially improves safety and efficiency over state-of-the-art baselines, and the framework naturally extends to multi-lane driving as well as on-ramp merging and exiting scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 14 of its 43 references that are in the map. First posted on arXiv 12 Jun 2026.

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14 of 43 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Scenario-Based Hierarchical Reinforcement Learning for Automated Driving Decision Making28 Jun
  2. 2024A Safe and Efficient Self-evolving Algorithm for Decision-making and Control of Autonomous Driving Systems22 Aug
  3. 2023Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving31 Oct

Other references · 11

  1. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  2. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  3. 2017Constrained Policy Optimizationcited by 983
  4. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  5. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 490
  6. 2020Reward Machines: Exploiting Reward Function Structure in Reinforcement Learningcited by 191
  7. 2020Safe Reinforcement Learning in Constrained Markov Decision Processescited by 115
  8. 2021Constraints Penalized Q-learning for Safe Offline Reinforcement Learningcited by 91
  9. 2021Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangiancited by 15
  10. 2024Reward Machines for Deep RL in Noisy and Uncertain Environmentscited by 11
  11. 2022Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machinescited by 10

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  1. 2024Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning
  2. 2023Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach
  3. 2021Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving
  4. 2025LA-RL: Language Action-guided Reinforcement Learning with Safety Guarantees for Autonomous Highway Driving
  5. 2022Self-Awareness Safety of Deep Reinforcement Learning in Road Traffic Junction Driving
  6. 2025Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving
  7. 2021Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancement
  8. 2021Runtime Safety Assurance for Learning-enabled Control of Autonomous Driving Vehicles