This paper presents a global trajectory optimization framework for minimizing lap time in autonomous racing under uncertain vehicle dynamics. Optimizing the trajectory over the full racing horizon is computationally expensive, and tracking such a trajectory in the real world hardly assures global optimality due to uncertain dynamics. Yet, existing work mostly focuses on dynamics learning at the tracking level, without updating the trajectory itself to account for the learned dynamics. To address these challenges, we propose a track-centric approach that directly learns and optimizes the full-horizon trajectory. We first represent trajectories through a track-agnostic parametric space in light of the wavelet transform. This space is then efficiently explored using Bayesian optimization, where the lap time of each candidate is evaluated by running simulations with the learned dynamics. This optimization is embedded in an iterative learning framework, where the optimized trajectory is deployed to collect real-world data for updating the dynamics, progressively refining the trajectory over the iterations. The effectiveness of the proposed framework is validated through simulations and real-world experiments, demonstrating lap time improvement of up to 20.7% over a nominal baseline and consistently outperforming state-of-the-art methods.

翻译：本文提出了一种全局轨迹优化框架，用于在不确定车辆动力学条件下最小化自主赛车的单圈时间。在全赛道范围内优化轨迹计算成本高昂，且由于动力学不确定性，在实际环境中跟踪此类轨迹难以保证全局最优性。然而，现有研究主要集中于跟踪层面的动力学学习，未能通过更新轨迹本身来适应学习到的动力学特性。为解决这些挑战，我们提出一种以赛道为中心的方法，直接学习并优化全时段轨迹。我们首先基于小波变换，通过与赛道无关的参数空间表示轨迹。随后利用贝叶斯优化高效探索该参数空间，其中每个候选轨迹的单圈时间通过使用学习到的动力学模型进行仿真评估。该优化过程嵌入迭代学习框架：将优化后的轨迹部署于实际数据采集以更新动力学模型，通过迭代逐步优化轨迹。仿真与实车实验验证了所提框架的有效性，相较于基准方法实现了最高20.7%的单圈时间提升，且持续优于现有先进方法。