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.

翻译：道路静态障碍物的突然出现（即麋鹿测试）是自动驾驶避撞领域中一个广为人知的紧急场景。模型预测控制（MPC）长期以来被用于先进自动驾驶车辆的规划与控制。然而，由于MPC在此类危险场景中进行规避动作时的高计算需求，麋鹿测试等紧急情况下的实时自动避撞实现问题仍未得到解决。本文通过实验实现MPC在静态障碍物突然且意外出现后的运动规划，为实时避撞提供了新的见解。鉴于当前最先进的非线性MPC在实时提供可接受解方面的能力有限，我们提出了一种类人前馈规划器，用于在MPC优化问题因初始猜测质量差而不可行或无法找到合适解时提供辅助。我们引入了最大转向操纵的概念来设计该前馈规划器，以模拟检测到道路静态障碍物后的人类反应。使用FPEV2-Kanon电动汽车在不同速度和紧急程度下进行了实车实验。此外，通过与当前最先进的MPC运动规划器进行比较，我们验证了所提规划策略的有效性。