mlsanityData to 14 Oct 2026

Risk-Aware Reinforcement Learning for Autonomous Driving: Improving Safety When Driving through Intersection

arXiv 2503.19690 ↗First posted 25 Mar 2025cs.ROcited by 2

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

also in 1 more
Abstract (arXiv)

Applying reinforcement learning to autonomous driving has garnered widespread attention. However, classical reinforcement learning methods optimize policies by maximizing expected rewards but lack sufficient safety considerations, often putting agents in hazardous situations. This paper proposes a risk-aware reinforcement learning approach for autonomous driving to improve the safety performance when crossing the intersection. Safe critics are constructed to evaluate driving risk and work in conjunction with the reward critic to update the actor. Based on this, a Lagrangian relaxation method and cyclic gradient iteration are combined to project actions into a feasible safe region. Furthermore, a Multi-hop and Multi-layer perception (MLP) mixed Attention Mechanism (MMAM) is incorporated into the actor-critic network, enabling the policy to adapt to dynamic traffic and overcome permutation sensitivity challenges. This allows the policy to focus more effectively on surrounding potential risks while enhancing the identification of passing opportunities. Simulation tests are conducted on different tasks at unsignalized intersections. The results show that the proposed approach effectively reduces collision rates and improves crossing efficiency in comparison to baseline algorithms. Additionally, our ablation experiments demonstrate the benefits of incorporating risk-awareness and MMAM into RL.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 18 of its 39 references that are in the map. First posted on arXiv 25 Mar 2025.

Builds on

18 of 39 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning20 Jun
  2. 2022How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies16 Mar

Other references · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2017Constrained Policy Optimizationcited by 983
  5. 2015Risk-Constrained Reinforcement Learning with Percentile Risk Criteriacited by 337
  6. 2020Responsive Safety in Reinforcement Learning by PID Lagrangian Methodscited by 316
  7. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 289
  8. 2018Safe Exploration in Continuous Action Spacescited by 247
  9. 2020Learning to Walk in the Real World with Minimal Human Effortcited by 111
  10. 2020Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learningcited by 103
  11. 2023OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Researchcited by 80
  12. 2022A General Survey on Attention Mechanisms in Deep Learningcited by 64
4 more
  1. 2020Numerically Stable Dynamic Bicycle Model for Discrete-time Controlcited by 15
  2. 2019Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Trafficcited by 5
  3. 2021Fixed-Dimensional and Permutation Invariant State Representation of Autonomous Drivingcited by 5
  4. 2022Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learningcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Uncertainty-Aware Safety-Critical Decision and Control for Autonomous Vehicles at Unsignalized Intersectionscited by 1
  2. 2026A Risk-Sensitive and Uncertainty-Aware Decision-Making and Control Framework for Safe and Robust Autonomous Drivingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving
  2. 2021Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning
  3. 2023Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving
  4. 2025Biased-Attention Guided Risk Prediction for Safe Decision-Making at Unsignalized Intersections
  5. 2021Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge
  6. 2026BAP-SRL: Bayesian Adaptive Priority Safe Reinforcement Learning for Vehicle Motion Planning at Mixed Traffic Intersections
  7. 2025Goal-conditioned Hierarchical Reinforcement Learning for Sample-efficient and Safe Autonomous Driving at Intersections
  8. 2025Human-aligned Safe Reinforcement Learning for Highway On-Ramp Merging in Dense Traffic