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

arXiv: https://arxiv.org/abs/2106.04625 · HTML: https://mlsanity.com/paper/2106.04625/

## 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 7 of its 25 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 7 of 7)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Deep Predictive Models for Collision Risk Assessment in Autonomous Driving](https://mlsanity.com/paper/1711.10453/) (2017)
- [Occlusion-Aware Risk Assessment for Autonomous Driving in Urban Environments](https://mlsanity.com/paper/1809.04629/) (2018)
- [Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network](https://mlsanity.com/paper/2105.00153/) (2021)
- [The Pedestrian Patterns Dataset](https://mlsanity.com/paper/2001.01816/) (2020)

## Built on by (most cited first, 2 of 2)

- [Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning](https://mlsanity.com/paper/2401.08661/) (2023)
- [MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation](https://mlsanity.com/paper/2209.09079/) (2022)

## 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.