A Mixed-Reality Testbed for Autonomous Vehicles
For researchers developing autonomous vehicle algorithms, this testbed provides a more realistic validation environment that combines physical and simulated elements, but the contribution is incremental as it combines existing technologies.
The paper proposes a mixed-reality, hardware-in-the-loop testbed for autonomous vehicles that integrates physical robots with high-fidelity simulation to validate perception, planning, and control algorithms, and demonstrates a safety-guaranteed framework using Control Barrier Functions. The testbed supports multi-agent systems and connected autonomous vehicles, bridging simulation and real-world deployment.
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.