# RCMS: Risk-Aware Crash Mitigation System for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2309.12531 · HTML: https://mlsanity.com/paper/2309.12531/

## 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 30 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## 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)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network](https://mlsanity.com/paper/1909.05665/) (2019)
- [SLAS: Speed and Lane Advisory System for Highway Navigation](https://mlsanity.com/paper/2303.00861/) (2023)
- [Emergency Collision Avoidance and Mitigation Using Model Predictive Control and Artificial Potential Function](https://mlsanity.com/paper/2211.06574/) (2022)
- [Predicting Parameters for Modeling Traffic Participants](https://mlsanity.com/paper/2301.10893/) (2023)

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

- [Predictive Planner for Autonomous Driving with Consistency Models](https://mlsanity.com/paper/2502.08033/) (2025)
- [Frenet Corridor Planner: An Optimal Local Path Planning Framework for Autonomous Driving](https://mlsanity.com/paper/2505.03695/) (2025)

## Abstract (arXiv)

We propose a risk-aware crash mitigation system (RCMS), to augment any existing motion planner (MP), that enables an autonomous vehicle to perform evasive maneuvers in high-risk situations and minimize the severity of collision if a crash is inevitable. In order to facilitate a smooth transition between RCMS and MP, we develop a novel activation mechanism that combines instantaneous as well as predictive collision risk evaluation strategies in a unified hysteresis-band approach. For trajectory planning, we deploy a modular receding horizon optimization-based approach that minimizes a smooth situational risk profile, while adhering to the physical road limits as well as vehicular actuator limits. We demonstrate the performance of our approach in a simulation environment.