用高斯过程和ADMM优化，让汽车更稳地自动漂移
GP-enhanced Autonomous Drifting Framework using ADMM-based iLQR
- 结合高斯过程补偿模型误差，提升动态适应性
- 轨迹跟踪横向误差降低38%，计算速度提高75%
- 适合需要实时控制的自动驾驶漂移场景
自主漂移是一项复杂挑战，因其高度非线性动力学特性及在不确定环境中对精确实时控制的需求。本文提出一种分层控制框架，用于自动驾驶车辆沿任意路径漂移，重点解决模型不准确和实时控制中的计算难题。该框架将高斯过程（GP）回归与基于交替方向乘子法（ADMM）的迭代线性二次调节器（iLQR）结合：GP回归有效补偿模型残差，提升动态条件下的精度；ADMM-based iLQR兼具iLQR的快速轨迹优化能力，并利用ADMM分解问题为更易处理的子问题。仿真结果表明，所提框架显著提升了漂移轨迹跟踪性能与计算效率，横向误差均方根（RMSE）降低38%，平均计算时间比内点优化器（IPOPT）减少75%。
原文摘要 · Abstract (English)
Autonomous drifting is a complex challenge due to the highly nonlinear dynamics and the need for precise real-time control, especially in uncertain environments. To address these limitations, this paper presents a hierarchical control framework for autonomous vehicles drifting along general paths, primarily focusing on addressing model inaccuracies and mitigating computational challenges in real-time control. The framework integrates Gaussian Process (GP) regression with an Alternating Direction Method of Multipliers (ADMM)-based iterative Linear Quadratic Regulator (iLQR). GP regression effectively compensates for model residuals, improving accuracy in dynamic conditions. ADMM-based iLQR not only combines the rapid trajectory optimization of iLQR but also utilizes ADMM's strength in decomposing the problem into simpler sub-problems. Simulation results demonstrate the effectiveness of the proposed framework, with significant improvements in both drift trajectory tracking and computational efficiency. Our approach resulted in a 38$\%$ reduction in RMSE lateral error and achieved an average computation time that is 75$\%$ lower than that of the Interior Point OPTimizer (IPOPT).
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