The connectivity between vehicles, infrastructure, and other traffic participants brings a new dimension to automotive safety applications. Soon all the newly produced cars will have Vehicle to Everything (V2X) communication modems alongside the existing Advanced Driver Assistant Systems (ADAS). It is essential to identify the different sensor measurements for the same targets (Data Association) to use connectivity reliably as a safety feature alongside the standard ADAS functionality. Considering the camera is the most common sensor available for ADAS systems, in this paper, we present an experimental implementation of a Mahalanobis distance-based data association algorithm between the camera and the Vehicle to Vehicle (V2V) communication sensors. The implemented algorithm has low computational complexity and the capability of running in real-time. One can use the presented algorithm for sensor fusion algorithms or higher-level decision-making applications in ADAS modules.

中文翻译：

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感知和V2V通信传感器之间的数据关联

车辆，基础设施和其他交通参与者之间的连通性为汽车安全应用带来了新的维度。很快，所有新生产的汽车将与现有的高级驾驶员辅助系统（ADAS）一起配备“车辆到万物（V2X）”通信调制解调器。必须确定同一目标（数据关联）的不同传感器测量值，才能可靠地将连接性与安全性一起用作标准ADAS功能。考虑到摄像头是可用于ADAS系统的最常见传感器，在本文中，我们介绍了摄像头与“车辆对车辆（V2V）”通信传感器之间基于Mahalanobis距离的数据关联算法的实验实现。所实现的算法具有较低的计算复杂度和实时运行的能力。可以将提出的算法用于ADAS模块中的传感器融合算法或更高级别的决策应用程序。