R-CARLA: 自動運転レース向けの交換可能なダイナミクスを備えた高忠実度センサシミュレーション
R-CARLA: High-Fidelity Sensor Simulations with Interchangeable Dynamics for Autonomous Racing
自動運転レース向けに、正確な車両ダイナミクスと高忠実度センサシミュレーションを両立させたCARLA拡張を提案し、実世界データからのデジタルツイン構築によりSim-to-Realギャップを車両動力学で42%、センサで82%削減した。
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著者: Maurice Brunner, Edoardo Ghignone, Nicolas Baumann, Michele Magno
分類: cs.RO
原文アブストラクト
Autonomous racing has emerged as a crucial testbed for autonomous driving algorithms, necessitating a simulation environment for both vehicle dynamics and sensor behavior. Striking the right balance between vehicle dynamics and sensor accuracy is crucial for pushing vehicles to their performance limits. However, autonomous racing developers often face a trade-off between accurate vehicle dynamics and high-fidelity sensor simulations. This paper introduces R-CARLA, an enhancement of the CARLA simulator that supports holistic full-stack testing, from perception to control, using a single system. By seamlessly integrating accurate vehicle dynamics with sensor simulations, opponents simulation as NPCs, and a pipeline for creating digital twins from real-world robotic data, R-CARLA empowers researchers to push the boundaries of autonomous racing development. Furthermore, it is developed using CARLA's rich suite of sensor simulations. Our results indicate that incorporating the proposed digital-twin framework into R-CARLA enables more realistic full-stack testing, demonstrating a significant reduction in the Sim-to-Real gap of car dynamics simulation by 42% and by 82% in the case of sensor simulation across various testing scenarios.
