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

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.

💡 Research Summary

The paper addresses the “moose test,” a critical emergency scenario in which a static obstacle suddenly appears on the road, demanding an immediate evasive maneuver from an automated vehicle. While Model Predictive Control (MPC) is widely used for planning and control because of its ability to handle constraints and to re‑optimize in a receding‑horizon fashion, the authors point out that in such high‑urgency situations the computational burden of a nonlinear MPC (NLP‑based) often prevents a solution from being found within the strict real‑time budget. Warm‑starting, a common technique to accelerate convergence, becomes ineffective because the previous solution is no longer a good initial guess when the obstacle appears abruptly.

To overcome these limitations, the authors propose a hybrid planning architecture that combines a conventional MPC planner with a human‑inspired feed‑forward planner, called the Maximum Steering Feed‑forward (MSF) planner. The MSF planner models the extreme human reaction of “steer as hard as possible without braking” by pre‑computing a maximum‑steering maneuver (y_{\max}(t, v_x)). This maneuver is derived from the vehicle’s kinematic equations under the constraints of maximum steering angle (\delta_{\max}) and maximum steering rate (\dot\delta_{\max}). The lateral steering index (\nu = w , y_{\max}(\tau, v_x)) is then calculated from the obstacle width (w) and the remaining time‑to‑collision (\tau).

The MPC planner, on the other hand, uses a safety barrier function (y_{\text{safe}}(k)) based on a sigmoid formulation. This barrier ensures that the vehicle’s centre of gravity stays at least half the obstacle width away from the danger zone a few seconds before reaching the obstacle’s longitudinal position. Both planners output desired longitudinal and lateral accelerations (