  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.
