MCTR: Midpoint Corrected Triangulation for Autonomous Racing via Digital Twin Simulation in CARLA

2025-08-18

AI Summary

This work addresses two key limitations: (1) insufficient trajectory smoothness in Delaunay triangulation–based path planning for autonomous racing, and (2) the absence of realistic 3D LiDAR perception modeling in the F1TENTH simulator. To this end, we propose a high-fidelity digital twin–enabled verification framework. Methodologically: (1) we enhance Delaunay triangulation with midpoint correction and curvature-aware moving-average filtering to significantly improve trajectory continuity and trackability; (2) we construct a photorealistic digital twin racetrack in CARLA, fully integrated with physics-accurate 3D LiDAR point cloud simulation, enabling closed-loop perception–planning–control evaluation. Experiments demonstrate that our approach reduces path curvature by 32% compared to DTR, increases average lap speed by 11.5%, and exhibits superior robustness and cross-domain transferability on both simulation and physical testbeds.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

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.

Problem

• Improve trajectory smoothness in autonomous racing algorithms
• Address limitations of 3D LiDAR perception in simulators
• Enhance robustness of reactive controllers via digital twin

Innovation

• MCTR algorithm enhances trajectory smoothness
• Uses Curvature Corrected Moving Average
• Implements digital twin in CARLA simulator