Park Jungme, Kavathekar Vaibhavi, Bhuduri Shubhang, Amin Mohammad Hasan, Devaraj Sriram Sanjeev
College of Engineering, Kettering University, Flint, MI 48504, USA.
Sensors (Basel). 2025 Sep 5;25(17):5544. doi: 10.3390/s25175544.
With the recent 5G communication technology deployment, Cellular Vehicle-to-Everything (C-V2X) significantly enhances road safety by enabling real-time exchange of critical traffic information among vehicles, pedestrians, infrastructure, and networks. However, further research is required to address real-time application latency and communication reliability challenges. This paper explores integrating cutting-edge C-V2X technology with environmental perception systems to enhance safety at intersections and crosswalks. We propose a multi-module architecture combining C-V2X with state-of-the-art perception technologies, GPS mapping methods, and the client-server module to develop a co-operative perception system for collision avoidance. The proposed system includes the following: (1) a hardware setup for C-V2X communication; (2) an advanced object detection module leveraging Deep Neural Networks (DNNs); (3) a client-server-based co-operative object detection framework to overcome computational limitations of edge computing devices; and (4) a module for mapping GPS coordinates of detected objects, enabling accurate and actionable GPS data for collision avoidance-even for detected objects not equipped with C-V2X devices. The proposed system was evaluated through real-time experiments at the GMMRC testing track at Kettering University. Results demonstrate that the proposed system enhances safety by broadcasting critical obstacle information with an average latency of 9.24 milliseconds, allowing for rapid situational awareness. Furthermore, the proposed system accurately provides GPS coordinates for detected obstacles, which is essential for effective collision avoidance. The technology integration in the proposed system offers high data rates, low latency, and reliable communication, which are key features that make it highly suitable for C-V2X-based applications.
随着近期5G通信技术的部署，蜂窝车联网（C-V2X）通过实现车辆、行人、基础设施和网络之间关键交通信息的实时交换，显著提高了道路安全性。然而，仍需要进一步研究来应对实时应用延迟和通信可靠性挑战。本文探讨将前沿的C-V2X技术与环境感知系统相结合，以提高十字路口和人行横道的安全性。我们提出了一种多模块架构，将C-V2X与最先进的感知技术、GPS地图绘制方法以及客户端-服务器模块相结合，以开发用于避免碰撞的协同感知系统。所提出的系统包括以下部分：（1）用于C-V2X通信的硬件设置；（2）利用深度神经网络（DNN）的先进目标检测模块；（3）基于客户端-服务器的协同目标检测框架，以克服边缘计算设备的计算限制；（4）一个用于映射检测到的物体的GPS坐标的模块，即使对于未配备C-V2X设备的检测到的物体，也能提供准确且可用于避免碰撞的GPS数据。所提出的系统在凯特林大学的GMMRC测试轨道上通过实时实验进行了评估。结果表明，所提出的系统通过以平均9.24毫秒的延迟广播关键障碍物信息来提高安全性，从而实现快速的态势感知。此外，所提出的系统准确地为检测到的障碍物提供GPS坐标，这对于有效的碰撞避免至关重要。所提出的系统中的技术集成提供了高数据速率、低延迟和可靠的通信，这些关键特性使其非常适合基于C-V2X的应用。
