The development of autonomous driving has boosted the research on autonomous racing. However, existing local trajectory planning methods have difficulty planning trajectories with optimal velocity profiles at racetracks with sharp corners, thus weakening the performance of autonomous racing. To address this problem, we propose a local trajectory planning method that integrates Velocity Prediction based on Model Predictive Contour Control (VPMPCC). The optimal parameters of VPMPCC are learned through Bayesian Optimization (BO) based on a proposed novel Objective Function adapted to Racing (OFR). Specifically, VPMPCC achieves velocity prediction by encoding the racetrack as a reference velocity profile and incorporating it into the optimization problem. This method optimizes the velocity profile of local trajectories, especially at corners with significant curvature. The proposed OFR balances racing performance with vehicle safety, ensuring safe and efficient BO training. In the simulation, the number of training iterations for OFR-based BO is reduced by 42.86% compared to the state-of-the-art method. The optimal simulation-trained parameters are then applied to a real-world F1TENTH vehicle without retraining. During prolonged racing on a custom-built racetrack featuring significant sharp corners, the mean velocity of VPMPCC reaches 93.18% of the vehicle's handling limits. The released code is available at https://github.com/zhouhengli/VPMPCC.

翻译：自动驾驶技术的发展推动了自主赛车的研究。然而，现有局部轨迹规划方法难以在具有急弯的赛道上规划出具有最优速度曲线的轨迹，从而削弱了自主赛车的性能。为解决这一问题，我们提出了一种集成基于模型预测轮廓控制（VPMPCC）速度预测的局部轨迹规划方法。VPMPCC的最优参数通过贝叶斯优化（BO）学习获得，该优化基于一种适用于赛车场景的新型目标函数（OFR）。具体而言，VPMPCC通过将赛道编码为参考速度曲线并将其纳入优化问题来实现速度预测。该方法优化了局部轨迹的速度曲线，特别是在曲率显著的弯道处。所提出的OFR在赛车性能与车辆安全性之间取得平衡，确保了安全高效的BO训练。在仿真中，基于OFR的BO训练迭代次数较现有最优方法减少了42.86%。随后，将仿真训练得到的最优参数直接应用于真实世界的F1TENTH车辆而无需重新训练。在具有显著急弯的自建赛道上进行长时间竞赛时，VPMPCC的平均速度达到了车辆操控极限的93.18%。已发布的代码可在 https://github.com/zhouhengli/VPMPCC 获取。