通过融合赛道曲率优化速度规划，显著提升赛车圈速表现。
Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC Local Trajectory Planning Method
- 根据赛道中心线曲率动态生成参考速度，融入目标函数优化轨迹速度。
- 在复杂弯道赛道上实测，圈速提升11.4%至12.5%。
- 适用于高动态自动驾驶赛车场景，代码开源可复现。
自动驾驶技术的广泛应用显著推动了自动驾驶赛车的发展。模型预测轮廓控制（MPCC）是一种高效的局部轨迹规划方法。然而，传统MPCC在曲率变化剧烈的赛道上表现受限。为此，本文提出一种融合曲率的MPCC（CiMPCC）方法，基于赛道中心线曲率优化局部轨迹速度。具体实现为将赛道曲率映射为归一化参考速度曲线，并将其引入代价函数以优化速度。该方法确保了在大曲率变化赛道上的高效与高性能轨迹规划。实验在自建1:10比例F1TENTH赛车平台（基于ROS）上进行，结果表明，在具有急弯的挑战性赛道上，所提方法相比其他轨迹规划方法平均提升圈速11.4%~12.5%。代码已开源：https://github.com/zhouhengli/CiMPCC。
原文摘要 · Abstract (English)
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 https://github.com/zhouhengli/CiMPCC.
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