慣性ドリフト車両のための学習ベース計画・制御フレームワーク
A Learning-based Planning and Control Framework for Inertia Drift Vehicles
ベイズ最適化を用いて、慣性ドリフト中の滑らかな遷移と速度損失の最小化を図る計画・制御手法を提案し、8の字経路でのシミュレーションで有効性を示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Bei Zhou, Zhouheng Li, Lei Xie, Hongye Su, Johannes Betz
分類: cs.RO
原文アブストラクト
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.
