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.
@article{arxiv.2309.12531,
title = {RCMS: Risk-Aware Crash Mitigation System for Autonomous Vehicles},
author = {Faizan M. Tariq and David Isele and John S. Baras and Sangjae Bae},
journal= {arXiv preprint arXiv:2309.12531},
year = {2023}
}
Comments
Presented at the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC) 2023, Bilbao, Bizkaia, Spain