Emergent Autonomous Drifting for Collision Avoidance in Real-World Winter Driving Scenarios

2026-07-21

AI Summary

This study addresses the limitations of conventional Electronic Stability Control (ESC) systems in mitigating collision risks during high-speed winter driving on sudden low-friction surfaces such as ice. The authors propose a nonlinear Model Predictive Control (MPC) framework endowed with autonomous drifting capability, which for the first time validates controlled drift as an active safety strategy in complex, real-accident-based icy scenarios—including oncoming vehicle intrusion and lane departure. Departing from the traditional stability-first paradigm, the approach dynamically balances controllability and stability. Experimental results demonstrate that the proposed controller significantly reduces lane departure error compared to ESC, particularly at high speeds, by executing controlled drift maneuvers to avoid obstacles. Monte Carlo simulations further confirm the robustness and superiority of the method under stochastic disturbances.

Abstract

Real-world collision avoidance is a core motivation for studying the dynamics and control of high sideslip drifting in vehicles, yet the practical benefit of such maneuvers has so far primarily been tested in scenarios explicitly engineered to require drifting. In this work, we explore the question of if and when drifting may be optimal for safety in real-world winter driving conditions. We present a drift-capable nonlinear model predictive control (MPC) system designed to handle scenarios grounded in crash fatality data and deploy the controller in a high fidelity simulator across road departure and oncoming vehicle collision avoidance scenarios. The controller naturally initiates and sustains drifting maneuvers to stay on the road when hitting a patch of ice on the rear axle and to avoid an oncoming vehicle that has slid into its lane. Comparisons with a benchmark electronic stability control (ESC) system demonstrate how a drift-capable controller can trade off stability for controllability to precisely maneuver through dangerous winter driving scenarios. A Monte Carlo study over random ice patches further shows that the drift-capable controller achieves lower median lane error than ESC across several speeds, while revealing that drifting emerges predominantly at higher speeds.

Problem

• collision avoidance
• autonomous drifting
• winter driving
• vehicle dynamics
• safety

Innovation

• drift-capable MPC
• collision avoidance
• winter driving
• autonomous drifting
• nonlinear model predictive control