# Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning

arXiv: https://arxiv.org/abs/2004.04450 · HTML: https://mlsanity.com/paper/2004.04450/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 20 of 20)

- [Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation](https://mlsanity.com/paper/2104.03111/) (2021)
- [Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook](https://mlsanity.com/paper/2107.01110/) (2021)
- [Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning](https://mlsanity.com/paper/2107.07316/) (2021)
- [LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty](https://mlsanity.com/paper/2209.11422/) (2022)
- [Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving](https://mlsanity.com/paper/2210.08885/) (2022)
- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (2024)
- [SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments](https://mlsanity.com/paper/2204.01922/) (2022)
- [Recursive Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model](https://mlsanity.com/paper/2506.00286/) (2025)
- [Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning](https://mlsanity.com/paper/2102.06765/) (2021)
- [I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames](https://mlsanity.com/paper/2109.09807/) (2021)
- [Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving](https://mlsanity.com/paper/2301.00650/) (2022)
- [Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2306.11465/) (2023)
- [High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging](https://mlsanity.com/paper/2107.07413/) (2021)
- [Trust-aware Control for Intelligent Transportation Systems](https://mlsanity.com/paper/2111.04248/) (2021)
- [Integrating occlusion awareness in urban motion prediction for enhanced autonomous vehicle navigation](https://mlsanity.com/paper/2406.19798/) (2024)
- [On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents](https://mlsanity.com/paper/2605.21763/) (2026)
- [MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation](https://mlsanity.com/paper/2209.09079/) (2022)
- [An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios](https://mlsanity.com/paper/2412.08562/) (2024)
- [A Noise-Robust Elicit-to-Optimize Framework for Distortion Riskmetrics via Inverse Reinforcement Learning](https://mlsanity.com/paper/2607.14373/) (2026)
- [Near-Optimal Sample Complexity for Recursive Entropic Risk Reinforcement Learning with a Generative Model](https://mlsanity.com/paper/2610.06931/) (2026)

## Abstract (arXiv)

Reinforcement learning is nowadays a popular framework for solving different decision making problems in automated driving. However, there are still some remaining crucial challenges that need to be addressed for providing more reliable policies. In this paper, we propose a generic risk-aware DQN approach in order to learn high level actions for driving through unsignalized occluded intersections. The proposed state representation provides lane based information which allows to be used for multi-lane scenarios. Moreover, we propose a risk based reward function which punishes risky situations instead of only collision failures. Such rewarding approach helps to incorporate risk prediction into our deep Q network and learn more reliable policies which are safer in challenging situations. The efficiency of the proposed approach is compared with a DQN learned with conventional collision based rewarding scheme and also with a rule-based intersection navigation policy. Evaluation results show that the proposed approach outperforms both of these methods. It provides safer actions than collision-aware DQN approach and is less overcautious than the rule-based policy.