Dodging the Moose: Experimental Insights in Real-Life Automated Collision Avoidance

TL;DR

This paper explores real-time collision avoidance in emergency scenarios like the moose test by combining MPC with a human-like feed-forward planner, validated through real-world experiments on an electric vehicle.

Contribution

It introduces a hybrid planning approach that enhances real-time performance in collision avoidance by integrating a feed-forward planner with MPC, addressing computational limitations.

Findings

The combined approach improves real-time response in emergency scenarios.

Experimental results show better performance than standalone MPC.

The method effectively mimics human reactions in static obstacle avoidance.

Abstract

The sudden appearance of a static obstacle on the road, i.e. the moose test, is a well-known emergency scenario in collision avoidance for automated driving. Model Predictive Control (MPC) has long been employed for planning and control of automated vehicles in the state of the art. However, real-time implementation of automated collision avoidance in emergency scenarios such as the moose test remains unaddressed due to the high computational demand of MPC for evasive action in such hazardous scenarios. This paper offers new insights into real-time collision avoidance via the experimental imple- mentation of MPC for motion planning after a sudden and unexpected appearance of a static obstacle. As the state-of-the-art nonlinear MPC shows limited capability to provide an acceptable solution in real-time, we propose a human-like feed-forward planner to assist when the MPC optimization problem is either infeasible or unable to find a suitable solution due to the poor quality of its initial guess. We introduce the concept of maximum steering maneuver to design the feed-forward planner and mimic a human-like reaction after detecting the static obstacle on the road. Real-life experiments are conducted across various speeds and level of emergency using FPEV2-Kanon electric vehicle. Moreover, we demonstrate the effectiveness of our planning strategy via comparison with the state-of- the-art MPC motion planner.