Bridge to Real Environment with Hardware-in-the-loop for Wireless Artificial Intelligence Paradigms
This addresses the risk of wasted resources for researchers and engineers in vehicular networks by providing a bridge to real-world testing, though it is incremental as it builds on existing hardware-in-the-loop concepts.
The paper tackles the problem of unexpected outcomes when machine learning solutions for wireless vehicular networks are tested only in simulation, by developing a hardware-in-the-loop system to test AI, services, and LiDAR data in both simulated and real-world settings, enabling more reliable validation.
Nowadays, many machine learning (ML) solutions to improve the wireless standard IEEE802.11p for Vehicular Adhoc Network (VANET) are commonly evaluated in the simulated world. At the same time, this approach could be cost-effective compared to real-world testing due to the high cost of vehicles. There is a risk of unexpected outcomes when these solutions are implemented in the real world, potentially leading to wasted resources. To mitigate this challenge, the hardware-in-the-loop is the way to move forward as it enables the opportunity to test in the real world and simulated worlds together. Therefore, we have developed what we believe is the pioneering hardware-in-the-loop for testing artificial intelligence, multiple services, and HD map data (LiDAR), in both simulated and real-world settings.