Zhou, Bei (Zhejiang University), Li, Zhouheng (Zhejiang University), Xie, Lei (Zhejiang University), Su, Hongye (Zhejiang University), Betz, Johannes (Technical University of Munich)

A Learning-Based Planning and Control Framework for Inertia Drift Vehicles

Scheduled for presentation during the Regular Session "S26c-Motion Planning, Trajectory Optimization, and Control for Autonomous Vehicles" (TH-LA-T26), Thursday, November 20, 2025, 16:00−16:20, Broadbeach 1&2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Inertia drift is a transitional maneuver between two sustained drift stages in opposite directions, which provides valuable insights for navigating consecutive sharp corners for autonomous racing. However, this can be a challenging scenario for the drift controller to handle rapid transitions between opposing sideslip angles while maintaining accurate path tracking. Moreover, accurate drift control depends on a high-fidelity vehicle model to derive drift equilibrium points and predict vehicle states, but this is often compromised by the strongly coupled longitudinal-lateral drift dynamics and unpredictable environmental variations. To address these challenges, this paper proposes a learning-based planning and control framework utilizing Bayesian optimization (BO), which develops a planning logic to ensure a smooth transition and minimal velocity loss between inertia and sustained drift phases. BO is further employed to learn a performance-driven control policy that mitigates modeling errors for enhanced system performance.Simulation results on an 8-shape reference path demonstrate that the proposed framework can achieve smooth and stable inertia drift through sharp corners.