{"ID":2896930,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.05748","arxiv_id":"2507.05748","title":"A Learning-based Planning and Control Framework for Inertia Drift Vehicles","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.","short_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 a...","url_abs":"https://arxiv.org/abs/2507.05748","url_pdf":"https://arxiv.org/pdf/2507.05748v1","authors":"[\"Bei Zhou\",\"Zhouheng Li\",\"Lei Xie\",\"Hongye Su\",\"Johannes Betz\"]","published":"2025-07-08T07:49:00Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}