Multi-level decision framework collision avoidance algorithm in emergency scenarios

TL;DR

This paper proposes a multi-level decision framework combining emergency steering and braking to improve collision avoidance in complex, dynamic environments for autonomous vehicles, enhancing safety in emergency scenarios.

Contribution

It introduces a novel multi-level decision-making obstacle avoidance framework that integrates steering and braking, addressing environment complexity and vehicle stability issues.

Findings

Effective obstacle avoidance in complex traffic scenarios

Improved vehicle stability during emergency maneuvers

Simulation confirms robustness of the decision framework

Abstract

With the rapid development of autonomous driving, the attention of academia has increasingly focused on the development of anti-collision systems in emergency scenarios, which have a crucial impact on driving safety. While numerous anti-collision strategies have emerged in recent years, most of them only consider steering or braking. The dynamic and complex nature of the driving environment presents a challenge to developing robust collision avoidance algorithms in emergency scenarios. To address the complex, dynamic obstacle scene and improve lateral maneuverability, this paper establishes a multi-level decision-making obstacle avoidance framework that employs the safe distance model and integrates emergency steering and emergency braking to complete the obstacle avoidance process. This approach helps avoid the high-risk situation of vehicle instability that can result from the separation of steering and braking actions. In the emergency steering algorithm, we define the collision hazard moment and propose a multi-constraint dynamic collision avoidance planning method that considers the driving area. Simulation results demonstrate that the decision-making collision avoidance logic can be applied to dynamic collision avoidance scenarios in complex traffic situations, effectively completing the obstacle avoidance task in emergency scenarios and improving the safety of autonomous driving.