# Risk Assessment and Planning with Bidirectional Reachability for Autonomous Driving

arXiv: https://arxiv.org/abs/1909.08059 · HTML: https://mlsanity.com/paper/1909.08059/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Safe Occlusion-aware Autonomous Driving via Game-Theoretic Active Perception](https://mlsanity.com/paper/2105.08169/) (2021)
- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (2024)
- [Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety](https://mlsanity.com/paper/2402.01507/) (2024)
- [Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving](https://mlsanity.com/paper/2102.03053/) (2021)

## Abstract (arXiv)

Knowing and predicting dangerous factors within a scene are two key components during autonomous driving, especially in a crowded urban environment. To navigate safely in environments, risk assessment is needed to quantify and associate the risk of taking a certain action. Risk assessment and planning is usually done by first tracking and predicting trajectories of other agents, such as vehicles and pedestrians, and then choosing an action to avoid collision in the future. However, few existing risk assessment algorithms handle occlusion and other sensory limitations effectively. This paper explores the possibility of efficient risk assessment under occlusion via both forward and backward reachability. The proposed algorithm can not only identify where the risk-induced factors are, but also be used for motion planning by executing low-level commands, such as throttle. The proposed method is evaluated on various four-way highly occluded intersections with up to five other vehicles in the scene. Compared with other risk assessment algorithms, the proposed method shows better efficiency, meaning that the ego vehicle reaches the goal at a higher speed. In addition, it also lowers the median collision rate by 7.5x.