mlsanityData to 7 Oct 2026

Don't Get Yourself into Trouble! Risk-aware Decision-Making for Autonomous Vehicles

arXiv 2106.04625 ↗First posted 8 Jun 2021cs.AI cs.ROcited by 2

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

Abstract (arXiv)

Risk is traditionally described as the expected likelihood of an undesirable outcome, such as collisions for autonomous vehicles. Accurately predicting risk or potentially risky situations is critical for the safe operation of autonomous vehicles. In our previous work, we showed that risk could be characterized by two components: 1) the probability of an undesirable outcome and 2) an estimate of how undesirable the outcome is (loss). This paper is an extension to our previous work. In this paper, using our trained deep reinforcement learning model for navigating around crowds, we developed a risk-based decision-making framework for the autonomous vehicle that integrates the high-level risk-based path planning with the reinforcement learning-based low-level control. We evaluated our method in a high-fidelity simulation such as CARLA. This work can improve safety by allowing an autonomous vehicle to one day avoid and react to risky situations.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 7 of its 25 references that are in the map. First posted on arXiv 8 Jun 2021.

Builds on

7 of 25 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network1 May

Other references · 6

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  3. 2015Prioritized Experience Replaycited by 1,070
  4. 2017Deep Predictive Models for Collision Risk Assessment in Autonomous Drivingcited by 15
  5. 2018Occlusion-Aware Risk Assessment for Autonomous Driving in Urban Environmentscited by 13
  6. 2020The Pedestrian Patterns Datasetcited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1.

2 papers.

  1. 2023Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learningcited by 3
  2. 2022MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies
  2. 2021Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge
  3. 2025Risk-Aware Reinforcement Learning for Autonomous Driving: Improving Safety When Driving through Intersection
  4. 2025Collision Risk Estimation via Loss Prediction in End-to-End Autonomous Driving
  5. 2025Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving
  6. 2026A Risk-Sensitive and Uncertainty-Aware Decision-Making and Control Framework for Safe and Robust Autonomous Driving
  7. 2021Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving
  8. 2025Uncertainty-Aware Safety-Critical Decision and Control for Autonomous Vehicles at Unsignalized Intersections