将物理机器人与高保真仿真结合,打造自动驾驶安全验证新平台。
A Mixed-Reality Testbed for Autonomous Vehicles

- 物理机器人与虚拟环境实时联动,支持复杂场景测试
- 集成感知、规划与在线学习控制器,实现安全闭环控制
- 适合自动驾驶多智能体系统与安全算法研究
我们提出一种混合现实、硬件在环(HIL)的自动驾驶车辆测试平台,无缝融合物理移动机器人测试台与高保真仿真环境。虚拟仿真可生成多样且危及安全的驾驶场景,用于验证先进的感知、规划与控制算法;通过在逼真的虚拟环境中部署配备多模态传感器的物理机器人,进一步提升验证严谨性。测试平台还支持基于无线通信的车辆联网,结合物理机器人与虚拟代理,可容纳大量智能体,适用于连通式自动驾驶车辆(CAVs)等多智能体系统研究。最后,我们提出一种结合感知、规划与基于控制屏障函数(CBFs)的新型在线学习控制器的安全保障框架。实验验证了该框架的关键功能与平台整体价值,有效弥合了仿真与真实硬件部署之间的鸿沟。
原文摘要 · Abstract (English)
We propose a mixed-reality, hardware-in-the-loop (HIL) testbed for autonomous vehicles that seamlessly integrates a physical testbed of mobile robots with a high-fidelity simulation environment. The virtual simulation enables the creation of diverse, safety-critical driving scenarios to validate state-of-the-art perception, planning, and control algorithms, while augmenting simulations with physical robots equipped with multimodal sensors in photorealistic virtual environments further facilitating rigorous validation. Our testbed also features vehicular connectivity using wireless communication and can accommodate a large number of agents through the combination of physical robots and virtual simulated agents, supporting research on multi-agent systems including Connected and Autonomous Vehicles (CAVs). Finally, we present a safety-guaranteed framework combining perception, planning and a novel online learning-based controller using Control Barrier Functions (CBFs) for CAVs. Experiments using the proposed framework are used to validate and demonstrate the key functionalities and the overall utility of the testbed to bridge the gap between simulation and real-world hardware deployment.
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