Title: Dodging the Moose: Experimental Insights in Real-Life Automated Collision Avoidance
ArXiv ID: 2602.17512
Date: 2026-02-19
Authors: ** - Leila Gharavi – Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands - Bart De Schutter – Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands - Simone Baldi – School of Mathematics, Southeast University, Nanjing, China - Yuki Hosomi – Department of Advanced Energy, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan - Tona Sato – Department of Advanced Energy, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan - Binh‑Minh Nguyen – Department of Advanced Energy, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan - Hiroshi Fujimoto – Department of Advanced Energy, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan **
📝 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.
💡 Deep Analysis
📄 Full Content
M OTION planning in automated driving has been extensively researched during the past years. Avoiding a collision in hazardous scenarios is particularly challenging on the operational and stability levels [1], i.e. planning a safe trajectory for the ego vehicle and tracking it during an emergency scenario.
Leila Gharavi and Bart De Schutter are with Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands.
Simone Baldi is with the School of Mathematics, Southeast University, Nanjing, China.
Yuki Hosomi, Tona Sato, Binh-Minh Nguyen and Hiroshi Fujimoto are with the Department of Advanced Energy, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan.
Experiment videos are accessible on YouTube: https://youtu.be/
GQk1n9bXKJU?si=IMxbQAHhFramVYBR globally-planned trajectory object distance lo c a ll y-pla nned trajectory object width Fig. 1: Elements of the collision avoidance problem after detecting a static obstacle Among the various testing scenarios [2], one example of an emergency situation is the sudden appearance of a static obstacle on the road, which can also be considered as the extreme case of leading vehicle deceleration in [3]. Figure 1 shows a schematic view of influential elements for the planning problem in such scenarios: the distance to the object -often reflected in time-to-collision or distance-to-collision threat measures in the literature [4] -and the width of the obstacle. For instance, shorter distance to the obstacle with a larger width can both contribute to the criticality of the situation as the width determines the necessary lateral displacement for collision avoidance. Understanding the importance of a wider width is closely linked to the current states of the vehicle as the required braking or steering actions for achieving a specific lateral displacement depend on factors like the current vehicle speed or sideslip angle.
MPC has become increasingly popular in the field of automated collision avoidance thanks to its straightforward handling of constraints and its capacity to dynamically adjust to environmental changes by solving the control optimization problem in a receding horizon manner [5]. In the current state of the art, trajectory planning and vehicle control are commonly addressed through one of two architectural frameworks: hierarchical [6]- [12] or integrated [13]- [18].
A hierarchical architecture offers greater flexibility in defining control problems and enables faster responses for real-time implementation, owing to the differing frequency and performance requirements at each level. However, the reference trajectory provided by the planner may not be attainable for the real plant. This is a critical issue in emergency cases where the feasible area for collision avoidance is limited and the vehicle is operating at its handling limits. Integrated planning and control circumvents this issue by treating both problems within a single optimization problem.
While integrated MPC design, in particular when used with Electronic Power Steering (EPS), allows the handling of two optimization problems combined, it is essential to highlight a key distinction between the two architectures: in a hierarchical architecture, addressing the planning and control optimization problems can occur at different frequencies (e.g. planning at 5-10Hz and control at 50-100Hz). Conversely, an integrated architecture demands solving the integrated optimization problem at the control frequency, albeit with a higher computational demand compared to the control optimization problem.
An important source of computational complexity stems from the fact that the integrated MPC optimization problem is nonlinear, which requires employing computationally-demanding NonLinear Programming (NLP) algorithms to find the -locally -optimal solution. In [16], Gaussian safe envelopes used in the integrated MPC problem are obtained via Gaussian processes regression; this formulation allows for efficient solution of the integrated MPC problem by Quadratic Programming (QP). Other examples of QP solution of the MPC optimization problem include [17] where constraining the decision space to the linear tire force range leads to a quadratic formulation of the problem, and [19] where the weights of the simplified quadratic problem are adapted online for improved control performance.
In [15], two models are serially cascaded to handle the two problems simultaneously, hence facilitating realtime solution of the optimization problem by NLP and a warm-start strategy. However, this strategy is limited in finding the optimal solution if a static obstacle suddenly appears on the road. Moreover, physics-based and local convexification [11], [20] or explicit sub-optimal solution [13] have helped the real-time realization of integrated planning and control for normal driving. Yet, achieving real-time solutions in emergency scenarios remains a primary bottleneck of