用软硬结合测试方法，让车联网AI在仿真与真实环境间无缝验证。
Bridge to Real Environment with Hardware-in-the-loop for Wireless Artificial Intelligence Paradigms
- 构建软硬一体测试框架，融合仿真与真实设备
- 首次实现多服务与高精地图数据的跨环境联合验证
- 适合车联网AI研发者和实测团队参考
当前许多提升车载自组织网络（VANET）IEEE802.11p标准的机器学习方案均在仿真环境中评估。由于车辆实测成本高昂，仿真虽具成本优势，但存在部署至真实环境后出现意外结果的风险，可能导致资源浪费。为缓解此问题，硬件在环（Hardware-in-the-loop）测试成为关键路径，可同时在仿真与真实世界中进行验证。本文提出首个面向人工智能、多业务及高精地图数据（LiDAR）的软硬结合测试平台，在仿真与真实场景中实现协同验证，推动车联网技术从仿真走向实际应用。
原文摘要 · Abstract (English)
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.
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