提出高效安全的超车轨迹规划方法，提升自动驾驶赛车超车成功率与实时性。
FSDP: Fast and Safe Data-Driven Overtaking Trajectory Planning for Head-to-Head Autonomous Racing Competitions
- 用稀疏高斯预测对手行为，结合双层优化生成轨迹
- 超车成功率提升8.93%，计算时间减少74.04%
- 适合资源受限平台的实时超车决策，尤其适用于竞速场景
在自动驾驶赛车中生成超车轨迹极具挑战，需满足车辆动力学约束、保证安全性并实现实时运行。本文提出快速且安全的数据驱动规划器，引入稀疏高斯预测以提升对手预测的效率与精度。采用双层二次规划框架生成超车轨迹：第一层通过多项式拟合生成粗略轨迹，提取参考状态与控制输入；第二层在Frenet坐标系下构建模型预测控制优化问题，生成满足运动学可行性与安全性的轨迹。在F1TENTH平台的实验表明，本方法优于现有最优方案，超车成功率提升8.93%，可支持最大对手速度，实现更平滑的自身轨迹，计算时间相比Predictive Spliner方法减少74.04%。代码已开源。
原文摘要 · Abstract (English)
Generating overtaking trajectories in autonomous racing is a challenging task, as the trajectory must satisfy the vehicle's dynamics and ensure safety and real-time performance running on resource-constrained hardware. This work proposes the Fast and Safe Data-Driven Planner to address this challenge. Sparse Gaussian predictions are introduced to improve both the computational efficiency and accuracy of opponent predictions. Furthermore, the proposed approach employs a bi-level quadratic programming framework to generate an overtaking trajectory leveraging the opponent predictions. The first level uses polynomial fitting to generate a rough trajectory, from which reference states and control inputs are derived for the second level. The second level formulates a model predictive control optimization problem in the Frenet frame, generating a trajectory that satisfies both kinematic feasibility and safety. Experimental results on the F1TENTH platform show that our method outperforms the State-of-the-Art, achieving an 8.93% higher overtaking success rate, allowing the maximum opponent speed, ensuring a smoother ego trajectory, and reducing 74.04% computational time compared to the Predictive Spliner method. The code is available at: https://github.com/ZJU-DDRX/FSDP.
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