In this paper, we demonstrate a proof of concept for characterizing vehicular behavior using only the roadside cameras of the ITS system. The essential advantage of this method is that it can be implemented in the roadside infrastructure transparently and inexpensively and can have a global view of each vehicle's behavior without any involvement of or awareness by the individual vehicles or drivers. By using a setup that includes programmatically controlled robot cars (to simulate different types of vehicular behaviors) and an external video camera set up to capture and analyze the vehicular behavior, we show that the driver classification based on the external video analytics yields accuracies that are within 1-2\% of the accuracies of direct vehicle-based characterization. We also show that the residual errors primarily relate to gaps in correct object identification and tracking and thus can be further reduced with a more sophisticated setup. The characterization can be used to enhance both the safety and performance of the traffic flow, particularly in the mixed manual and automated vehicle scenarios that are expected to be common soon.

翻译：本文中，我们提出了一种仅利用智能交通系统（ITS）路侧摄像头来表征车辆行为的概念验证方法。该方法的核心优势在于，能够以透明且低成本的方式部署于路侧基础设施中，并在无需任何个体车辆或驾驶员参与及感知的情况下，全局观测每辆车的驾驶行为。通过采用包含程序化控制的机器人车辆（用于模拟不同类型的车辆行为）以及外部视频摄像头（用于捕获并分析车辆行为）的实验装置，我们证实，基于外部视频分析的驾驶员分类精度与直接基于车辆自身表征的精度相差仅在1%-2%以内。我们还证明，残余误差主要源于对目标对象的正确识别与跟踪缺失，因此可通过更精密的装置进一步降低误差。该表征方法可用于提升交通流的安全性与性能，尤其适用于即将普遍存在的有人驾驶与自动驾驶混合交通场景。