RCMS: Risk-Aware Crash Mitigation System for Autonomous Vehicles

TL;DR

This paper introduces RCMS, a risk-aware crash mitigation system for autonomous vehicles that enhances existing motion planners by enabling evasive maneuvers and minimizing collision severity in high-risk scenarios.

Contribution

The paper presents a novel activation mechanism combining real-time and predictive risk assessments with a receding horizon optimization for trajectory planning.

Findings

Effective in simulation for high-risk scenarios

Smooth risk profile minimization

Seamless integration with existing motion planners

Abstract

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.