R-CARLA提升赛车仿真精度，实现车体与传感器的高保真同步模拟。
R-CARLA: High-Fidelity Sensor Simulations with Interchangeable Dynamics for Autonomous Racing
- 融合真实车辆动力学与高保真传感器模拟，支持端到端测试
- 数字孪生框架使仿真与现实差距缩小42%（动力学）和82%（传感器）
- 适合自动驾驶赛车研发者进行真实场景下的全链路验证
自主赛车已成为自动驾驶算法的重要测试平台，亟需兼顾车辆动力学与传感器行为的仿真环境。现有系统常在动力学精度与传感器保真度之间权衡。本文提出R-CARLA，作为CARLA的增强版本，支持从感知到控制的全栈测试。通过无缝集成精确车辆动力学、传感器模拟、对手车辆（NPC）仿真，以及基于真实机器人数据构建数字孪生的流程，研究人员可突破自主赛车开发边界。该系统利用CARLA丰富的传感器模拟功能。实验表明，引入数字孪生框架后，车辆动力学仿真与现实的差距减少42%，传感器仿真差距减少82%，在多种测试场景中均表现显著。
原文摘要 · Abstract (English)
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.
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