用F1赛车数据训练模型，自动生成最优起步路线，提速求解效率。
Efficient Trajectory Optimization for Autonomous Racing via Formula-1 Data-Driven Initialization
- 基于真实F1数据构建赛道轨迹库，用神经网络从赛道结构预测专家级走线
- 在17条赛道上测试，求解速度提升超50%，最终圈速不变
- 适合需要快速生成高性能赛车路径的自动驾驶系统研发者
轨迹优化是高速自主赛车的核心环节。然而现有优化流程对初始路径敏感，使用中心线或最小曲率路径等启发式初始化时，常收敛缓慢或陷入次优局部解。为此，本文利用真实世界一级方程式赛车的驾驶行为作为先验信息，提出一种基于F1实测遥测数据的学习驱动初始化策略。我们首先通过重建与对齐噪声GPS数据，在17条赛道上构建了标准化参考线表示的多赛道轨迹数据集。在此基础上，设计了一个神经网络，仅根据局部赛道几何结构直接预测专家级的赛道偏移量，无需显式建模车辆动力学或受力。该预测轨迹作为最小时间最优控制求解器的初始种子。在全部17条赛道上的实验表明，该学习初始化方法显著加速求解器收敛，大幅降低计算耗时，同时保持最终优化圈速不变。
原文摘要 · Abstract (English)
Trajectory optimization is a central component of fast and efficient autonomous racing. However practical optimization pipelines remain highly sensitive to initialization and may converge slowly or to suboptimal local solutions when seeded with heuristic trajectories such as the centerline or minimum-curvature paths. To address this limitation, we leverage expert driving behavior as a initialization prior and propose a learning-informed initialization strategy based on real-world Formula~1 telemetry. To this end, we first construct a multi-track Formula~1 trajectory dataset by reconstructing and aligning noisy GPS telemetry to a standardized reference-line representation across 17 tracks. Building on this, we present a neural network that predicts an expert-like raceline offset directly from local track geometry, without explicitly modeling vehicle dynamics or forces. The predicted raceline is then used as an informed seed for a minimum-time optimal control solver. Experiments on all 17 tracks demonstrate that the learned initialization accelerates solver convergence and significantly reduces runtime compared to traditional geometric baselines, while preserving the final optimized lap time.
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