MCTR: デジタルツインシミュレーションを用いた自動運転レースのための中点補正三角測量
MCTR: Midpoint Corrected Triangulation for Autonomous Racing via Digital Twin Simulation in CARLA
LiDARデータから直接制御する自動運転レース向けに、軌道の滑らかさを改善するMCTRアルゴリズムを提案し、CARLAでのデジタルツイン検証と実車実験で有効性を示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Junhao Ye, Cheng Hu, Yiqin Wang, Weizhan Huang, Nicolas Baumann, Jie He, Meixun Qu, Lei Xie, Hongye Su
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
