用缩小版车和AI数字孪生，低成本高保真测试自动驾驶控制器。
A Vehicle-in-the-Loop Simulator with AI-Powered Digital Twins for Testing Automated Driving Controllers
- 采用缩比车辆与AI驱动的数字孪生模型提升仿真精度。
- 相比传统方法节省空间与成本，仿真结果更贴近真实场景。
- 兼容现成软件，适合自动驾驶算法验证与智能交通研究。
模拟器是测试自动驾驶控制器的重要工具。车辆在环（ViL）测试与数字孪生（DT）技术广泛应用于控制器向实车部署的验证。然而，传统ViL依赖全尺寸车辆，占用空间大、成本高；基于物理模型的数字孪生存在因建模误差导致的真实感差距。本文提出一种集成缩比物理车辆与AI驱动数字孪生模型的综合实用模拟器，可显著降低测试空间与成本，同时通过AI模型保证仿真高保真度。该模拟器与现成软件及控制算法良好兼容，易于扩展。我们使用带形式化安全保证的过滤控制基准来展示其验证自动驾驶控制器的能力。实验表明，该模拟器在验证自动驾驶控制方案及智能交通系统方面具有巨大潜力。
原文摘要 · Abstract (English)
Simulators are useful tools for testing automated driving controllers. Vehicle-in-the-loop (ViL) tests and digital twins (DTs) are widely used simulation technologies to facilitate the smooth deployment of controllers to physical vehicles. However, conventional ViL tests rely on full-size vehicles, requiring large space and high expenses. Also, physical-model-based DT suffers from the reality gap caused by modeling imprecision. This paper develops a comprehensive and practical simulator for testing automated driving controllers enhanced by scaled physical cars and AI-powered DT models. The scaled cars allow for saving space and expenses of simulation tests. The AI-powered DT models ensure superior simulation fidelity. Moreover, the simulator integrates well with off-the-shelf software and control algorithms, making it easy to extend. We use a filtered control benchmark with formal safety guarantees to showcase the capability of the simulator in validating automated driving controllers. Experimental studies are performed to showcase the efficacy of the simulator, implying its great potential in validating control solutions for autonomous vehicles and intelligent traffic.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。