Recently, with the rapid development in vehicle-to-infrastructure communication technologies, the infrastructure-based, roadside perception system for cooperative driving has become a rising field. This paper focuses on one of the most critical challenges - the data-insufficiency problem. 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 approach is proposed to address this problem by creating synthesized training data using Augmented Reality and Generative Adversarial Network. This method creates synthesized dataset that is capable of training or fine-tuning a roadside perception detector which is robust to different weather and lighting conditions, or to adapt a new deployment location. We validate our approach at two intersections: Mcity intersection and State St/Ellsworth Rd roundabout. Our experiments show that (1) the detector can achieve good performance in all conditions when trained on synthesized data only, and (2) the performance of an existing detector trained with labeled data can be enhanced by synthesized data in harsh conditions.

翻译：近年来，随着车路协同通信技术的快速发展，基于基础设施的路侧感知系统在协同驾驶领域逐渐兴起。本文聚焦于其核心挑战之一——数据不足问题。高质量、高多样性的标注路侧传感器数据的缺乏，导致当前路侧感知系统的鲁棒性和可迁移性较低。本文提出了一种新颖方法，通过使用增强现实和生成对抗网络创建合成训练数据来解决该问题。该方法可生成合成数据集，用于训练或微调能够适应不同天气和光照条件，或适应新部署位置的鲁棒路侧感知检测器。我们在两个交叉路口（Mcity交叉口和State St/Ellsworth Rd环岛）验证了所提方法。实验表明：（1）仅基于合成数据训练的检测器能在所有条件下取得良好性能；（2）现有基于标注数据训练的检测器在恶劣条件下可通过合成数据进一步提升性能。