改进赛车路径平滑性，用数字孪生验证3D激光感知效果
MCTR: Midpoint Corrected Triangulation for Autonomous Racing via Digital Twin Simulation in CARLA
- 基于中点修正三角剖分生成更平滑的赛车路径
- 在CARLA中实现3D激光感知数字孪生系统，提升测试真实性
- 结合仿真与实车实验，验证算法鲁棒性
在自动驾驶赛车中，反应式控制器通过直接将传感器输入映射到控制动作，避免了显式定位和轨迹规划的计算负担。主流基线方法为基于LiDAR数据的跟空隙（Follow-The-Gap）算法，其后发展出基于德劳内三角剖分的赛车算法（DTR），但该方法使用外接圆生成路径时，常导致路径不够平滑，影响性能。此外，广泛使用的F1TENTH模拟器缺乏对3D LiDAR感知的支持，限制了真实场景下的测试能力。为此，本文提出MCTR算法，通过曲率修正移动平均提升路径平滑性，并在CARLA模拟器中构建数字孪生系统，以支持3D LiDAR感知下的算法验证。该算法已通过仿真与实车实验充分验证。
原文摘要 · Abstract (English)
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.
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