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

Leila Gharavi; Simone Baldi; Yuki Hosomi; Tona Sato; Bart De Schutter; Binh-Minh Nguyen; Hiroshi Fujimoto

eess.SYFeb 19, 2026

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

Authors: Leila Gharavi, Simone Baldi, Yuki Hosomi, Tona Sato, Bart De Schutter, Binh-Minh Nguyen, Hiroshi Fujimoto

Organizations: Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands. · School of Mathematics, Southeast University, Nanjing, China. · Department of Advanced Energy, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, 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.

Figures & tables

Figure 1: Elements of the collision avoidance problem after detecting a static obstacle

Figure 2: Single-track vehicle model


Var.	Definition	Units
$x_{\rm{ego}}$	Global longitudinal position of the CoG	m
$y_{\rm{ego}}$	Global lateral position of the CoG	m
$x_{\rm{obs}}$	Global longitudinal position of the obstacle	m
$y_{\rm{obs}}$	Global lateral position of the obstacle	m
$v_{X}$	Global longitudinal velocity of the CoG	m/s

Table I : List of variables and parameters

Panel of Figure 3 — Figure 3: The proposed control architecture

Panel of Figure 4 — Figure 4: Maximum steering maneuver ( 8 ) for different longitudinal velocities.

Panel of Figure 5 — Figure 5: Sigmoid barrier function examples

Figure 6: Experimental vehicle system configuration.


Parameter	Value
Max iteration	10
Max function evaluation	100
Constraint tolerance	1e-3
Optimality tolerance	1e-3
Step tolerance	1e-3

Table II : Optimization implementation parameters.

Panel of Figure 7 — Figure 7: Experimental assessment of the $\tau$ influence: vehicle trajectory for the desired velocity $v_{x}=5$ m/s.

Panel of Figure 8 — Figure 8: Experimental assessment of the $\nu$ influence: vehicle trajectory for the desired velocity $v_{x}=6$ m/s.

Panel of Figure 9 — Figure 9: Experimental assessment of the hazard: vehicle trajectory for the desired velocity $v_{x}=7$ m/s.

Panel of Figure 10 — Figure 10: Comparing the MPC planner in [ 21 ] and our proposed combined planning strategy

Collision Avoidance

Collision avoidance develops methods for predicting and preventing unsafe encounters among autonomous agents, vehicles, robots, and obstacles.

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A Convex Obstacle Avoidance Formulation

Ricardo Tapia, Iman Soltani

Laboratory for AI, Robotics and Automation, University of California at Davis, Davis, CA 95616 USA

Collision Avoidance Nonlinear MPC Autonomous Driving Model Predictive Control Dynamic Obstacle Avoidance

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Autonomous driving requires reliable collision avoidance in dynamic environments. Nonlinear Model Predictive Controllers (NMPCs) are suitable for this task, but struggle in time-critical scenarios requiring high frequency. To meet this demand, optimization problems are often simplified via linearization, narrowing the horizon window, or reduced temporal nodes, each compromising accuracy or reliability. This work presents the first general convex obstacle avoidance formulation, enabled by a novel approach to integrating logic. This facilitates the incorporation of an obstacle avoidance formulation into convex MPC schemes, enabling a convex optimization framework with substantially improved computational efficiency relative to conventional nonconvex methods. A key property of the formulation is that obstacle avoidance remains effective even when obstacles lie outside the prediction horizon, allowing shorter horizons for real-time deployment. In scenarios where nonconvex formulations are unavoidable, the proposed method meets or exceeds the performance of representative nonconvex alternatives. The method is evaluated in autonomous vehicle applications, where system dynamics are highly nonlinear.

Abstract

Collision Avoidance

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