Abstract
The widespread application of autonomous driving technology has significantly advanced the field of autonomous racing. Model Predictive Contouring Control (MPCC) is a highly effective local trajectory planning method for autonomous racing. However, the traditional MPCC method struggles with racetracks that have significant curvature changes, limiting the performance of the vehicle during autonomous racing. To address this issue, we propose a curvature-integrated MPCC (CiMPCC) local trajectory planning method for autonomous racing. This method optimizes the velocity of the local trajectory based on the curvature of the racetrack centerline. The specific implementation involves mapping the curvature of the racetrack centerline to a reference velocity profile, which is then incorporated into the cost function for optimizing the velocity of the local trajectory. This reference velocity profile is created by normalizing and mapping the curvature of the racetrack centerline, thereby ensuring efficient and performance-oriented local trajectory planning in racetracks with significant curvature. The proposed CiMPCC method has been experimented on a self-built 1:10 scale F1TENTH racing vehicle deployed with ROS platform. The experimental results demonstrate that the proposed method achieves outstanding results on a challenging racetrack with sharp curvature, improving the overall lap time by 11.4%-12.5% compared to other autonomous racing trajectory planning methods. Our code is available at this https URL.
Abstract (translated)
自主驾驶技术的广泛应用极大地推动了自动驾驶赛车领域的发展。模型预测轮廓控制（Model Predictive Contouring Control，MPCC）是一种非常有效的局部路径规划方法，在自动驾驶赛车中尤为突出。然而，传统的 MPCC 方法在处理赛道曲率变化显著的情况下表现不佳，这限制了车辆在自主赛车中的性能。为了解决这个问题，我们提出了一种基于曲率的 MPCC（Curvature-integrated Model Predictive Contouring Control, CiMPCC）局部路径规划方法用于自动驾驶赛车。该方法根据赛道中心线的曲率优化局部轨迹的速度。 具体实现包括将赛道中心线的曲率映射到参考速度曲线，然后将其纳入成本函数中以优化局部轨迹的速度。通过归一化和映射赛道中心线的曲率来创建这一参考速度曲线，从而确保在曲率显著变化的赛道上进行高效且性能导向的局部路径规划。 我们在一个自制的 1:10 比例 F1TENTH 赛车（搭载 ROS 平台）上对所提出的 CiMPCC 方法进行了实验。实验结果表明，在一条具有急转弯挑战性的赛道中，该方法取得了卓越的成绩，相较于其他自动驾驶赛车路径规划方法，整体圈速提高了 11.4% 至 12.5%。 我们的代码可在以下网址获取：[此链接](https://this https URL)（请注意，实际提供有效网址）。
URL
https://arxiv.org/abs/2502.03695
