主办：陕西省汽车工程学会
ISSN 1671-7988  CN 61-1394/TH
创刊：1976年
汽车实用技术

汽车实用技术 ›› 2026, Vol. 51 ›› Issue (2): 27-34.DOI: 10.16638/j.cnki.1671-7988.2026.002.005

• 智能网联汽车 • 上一篇    

基于车联车通信的多传感器融合安全 预警系统研究

邓敏皓，饶欣，邱秀盛，李红英，张靖轩   

  1. 江西科技学院 智能工程学院
  • 发布日期:2026-01-26
  • 通讯作者: 邓敏皓
  • 作者简介:邓敏皓（2004－），男，研究方向为智能网联汽车
  • 基金资助:
    江西省高等学校大学生创新训练计划项目（202310846011）

Research on a Multi-Sensor Fusion Safety Early Warning System Based on Vehicle-to-Vehicle Communication

DENG Minhao, RAO Xin, QIU Xiusheng, LI Hongying, ZHANG Jingxuan   

  1. College of Intelligent Engineering, Jiangxi Institute of Technology
  • Published:2026-01-26
  • Contact: DENG Minhao

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摘要： 随着车联网技术的发展，针对车联网通信存在延迟丢包及单车传感器感知盲区问题， 文章提出一种融合车联车（V2V）通信、摄像头、激光雷达和毫米波雷达的多传感器安全预 警系统。通过改进数据融合算法，将多源传感器数据与 V2V 共享信息深度融合，提高目标检 测准确率和预警响应速度。分析表明，该系统有效缩减盲区影响，优化通信延迟对预警的影 响，具备较传统单车感知更强的适应性和可靠性。该研究为预警系统提供思路，对其应用和 发展具有参考意义。

关键词: V2V 通信；多传感器融合；智能安全预警；数据融合

Abstract: With the development of vehicle networking technology, issues such as communication delays, packet loss, and sensor blind spots in single vehicles still exist. This paper proposes a multi-sensor safety warning system integrating vehicle-to-vehicle (V2V) communication, cameras, LiDAR, and millimeter-wave radar to address communication delays and sensing blind spots in vehicular networks. An improved data fusion algorithm enables deep integration of multi-source sensor data with V2V shared information, enhancing target detection accuracy and warning response speed. The system effectively reduces blind spot impacts and mitigates communication delay effects, demonstrating superior adaptability compared to traditional single-vehicle perception. This research provides valuable insights for advanced warning system development.

Key words: V2V communication; multi-sensor fusion; intelligent safety warning; data fusion