Recently, advancements in vehicle-to-infrastructure communication technologies have elevated the significance of infrastructure-based roadside perception systems for cooperative driving. This paper delves into one of its most pivotal challenges: data insufficiency. The lacking of high-quality labeled roadside sensor data with high diversity leads to low robustness, and low transfer-ability of current roadside perception systems. In this paper, a novel solution is proposed to address this problem that creates synthesized training data using Augmented Reality. A Generative Adversarial Network is then applied to enhance the reality further, that produces a photo-realistic synthesized dataset that is capable of training or fine-tuning a roadside perception detector which is robust to different weather and lighting conditions. Our approach was rigorously tested at two key intersections in Michigan, USA: the Mcity intersection and the State St./Ellsworth Rd roundabout. The Mcity intersection is located within the Mcity test field, a controlled testing environment. In contrast, the State St./Ellsworth Rd intersection is a bustling roundabout notorious for its high traffic flow and a significant number of accidents annually. Experimental results demonstrate that detectors trained solely on synthesized data exhibit commendable performance across all conditions. Furthermore, when integrated with labeled data, the synthesized data can notably bolster the performance of pre-existing detectors, especially in adverse conditions.

翻译：近年来，车联网通信技术的进步提升了基于基础设施的路侧感知系统在协同驾驶中的重要性。本文聚焦于其关键挑战之一：数据不足。缺乏高质量、高多样性且带标注的路侧传感器数据，导致当前路侧感知系统鲁棒性低、迁移性差。本文提出了一种新解决方案，通过增强现实技术生成合成训练数据，并进一步应用生成对抗网络提升真实感，从而产生能够训练或微调路侧感知检测器的照片级真实合成数据集，使其对不同的天气和光照条件具有鲁棒性。我们的方法在美国密歇根州的两个关键路口进行了严格测试：Mcity试验场内的Mcity路口以及State St./Ellsworth Rd环岛（一个因高交通流量和每年大量事故而闻名的繁忙环岛）。实验结果表明，仅基于合成数据训练的检测器在所有条件下均表现出色。此外，当与标注数据结合时，合成数据能显著增强已有检测器的性能，尤其在恶劣条件下。