Path planning for autonomous vehicles with minimal collision severity

TL;DR

This paper introduces a novel path planning method for autonomous vehicles that minimizes collision severity by combining a severity map with a two-level optimal control approach, especially useful in unavoidable collision scenarios.

Contribution

It presents a new two-level optimal control algorithm that prioritizes collision severity reduction and minimal steering effort, advancing autonomous vehicle safety planning.

Findings

Effective in identifying paths with minimal collision severity.

Demonstrates the impact of collision severity ratings on vehicle behavior.

Validated through numerical simulations with promising results.

Abstract

This paper proposes a path planning algorithm for autonomous vehicles, evaluating collision severity with respect to both static and dynamic obstacles. A collision severity map is generated from ratings, quantifying the severity of collisions. A two-level optimal control problem is designed. At the first level, the objective is to identify paths with the lowest collision severity. Subsequently, at the second level, among the paths with lowest collision severity, the one requiring the minimum steering effort is determined. Finally, numerical simulations were conducted using the optimal control software OCPID-DAE1. The study focuses on scenarios where collisions are unavoidable. Results demonstrate the effectiveness and significance of this approach in finding a path with minimum collision severity for autonomous vehicles. Furthermore, this paper illustrates how the ratings for collision severity influence the behaviour of the automated vehicle.