In autonomous racing, reactive controllers eliminate the computational burden of the full See-Think-Act autonomy stack by directly mapping sensor inputs to control actions. This bypasses the need for explicit localization and trajectory planning. A widely adopted baseline in this category is the Follow-The-Gap method, which performs trajectory planning using LiDAR data. Building on FTG, the Delaunay Triangulation-based Racing algorithm introduces further enhancements. However, DTR's use of circumcircles for trajectory generation often results in insufficiently smooth paths, ultimately degrading performance. Additionally, the commonly used F1TENTH-simulator for autonomous racing competitions lacks support for 3D LiDAR perception, limiting its effectiveness in realistic testing. To address these challenges, this work proposes the MCTR algorithm. MCTR improves trajectory smoothness through the use of Curvature Corrected Moving Average and implements a digital twin system within the CARLA simulator to validate the algorithm's robustness under 3D LiDAR perception. The proposed algorithm has been thoroughly validated through both simulation and real-world vehicle experiments.

翻译：在自动驾驶赛车领域，反应式控制器通过直接将传感器输入映射为控制动作，避免了完整“感知-决策-执行”自主架构的计算负担，从而无需进行显式定位与轨迹规划。该类方法中广泛采用的基线是Follow-The-Gap方法，其利用激光雷达数据进行轨迹规划。在FTG基础上，基于Delaunay三角剖分的赛车算法引入了进一步改进。然而，DTR采用外接圆生成轨迹常导致路径平滑度不足，最终影响性能表现。此外，自动驾驶赛车竞赛常用的F1TENTH模拟器缺乏对三维激光雷达感知的支持，限制了其在真实场景测试中的有效性。为应对这些挑战，本研究提出MCTR算法。该算法通过曲率校正移动平均法提升轨迹平滑度，并在CARLA模拟器中构建数字孪生系统，以验证算法在三维激光雷达感知下的鲁棒性。所提算法已通过仿真与实车实验得到全面验证。