Sun Peng, Qi Xingyu, Zhong Ruofei
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China.
Sensors (Basel). 2024 Sep 3;24(17):5730. doi: 10.3390/s24175730.
Within the context of smart transportation and new infrastructure, Vehicle-to-Everything (V2X) communication has entered a new stage, introducing the concept of holographic intersection. This concept requires roadside sensors to achieve collaborative perception, collaborative decision-making, and control. To meet the high-level requirements of V2X, it is essential to obtain precise, rapid, and accurate roadside information data. This study proposes an automated vehicle distance detection and warning scheme based on camera video streams. It utilizes edge computing units for intelligent processing and employs neural network models for object recognition. Distance estimation is performed based on the principle of similar triangles, providing safety recommendations. Experimental validation shows that this scheme can achieve centimeter-level distance detection accuracy, enhancing traffic safety. This approach has the potential to become a crucial tool in the field of traffic safety, providing intersection traffic target information for intelligent connected vehicles (ICVs) and autonomous vehicles, thereby enabling V2X driving at holographic intersections.
在智能交通和新基础设施的背景下，车与万物（V2X）通信进入了一个新阶段，引入了全息路口的概念。这一概念要求路边传感器实现协同感知、协同决策和控制。为满足V2X的高级要求，获取精确、快速且准确的路边信息数据至关重要。本研究提出了一种基于摄像头视频流的车辆距离自动检测与预警方案。它利用边缘计算单元进行智能处理，并采用神经网络模型进行目标识别。基于相似三角形原理进行距离估计，提供安全建议。实验验证表明，该方案能够实现厘米级的距离检测精度，提高交通安全。这种方法有可能成为交通安全领域的关键工具，为智能网联汽车（ICV）和自动驾驶车辆提供路口交通目标信息，从而实现全息路口的V2X驾驶。
