Design of vehicle network reverse information feedback system based on 5G communication
In recent years, with the continuous increase in global vehicle ownership, reversing safety has become a significant challenge in road traffic safety [1]. According to statistics, the proportion of traffic accidents caused by reversing has been rising year by year. Traditional reversing assistance technologies, such as rear-view mirror observation and reversing cameras, have limitations in complex environments, including large blind spots and delayed information, which fail to adequately meet the growing safety demands. This control system adopts a deeply integrated design of software and hardware. The hardware part uses a high-performance microcontroller as the core control unit, paired with an ultrasonic sensor array, audio-visual alarm devices, and a braking execution module. The sensor is responsible for collecting distance data, while the buzzer issues graded warnings based on preset thresholds. When a dangerous distance is reached, the relay responds quickly to intervene in the vehicle's braking system. On the software side, the system integrates advanced distance measurement algorithms and user interaction interfaces. The former ensures the accuracy of distance calculations through data optimization, while the latter allows users to customize alarm thresholds, braking trigger conditions, and other parameters on their mobile devices, with real-time display of the vehicle surroundings. Practical applications have shown that this system is easy to operate and responds swiftly, providing comprehensive safety protection during reversing through multimodal alerts and intelligent braking mechanisms.
Authors
- Wenjia Chen (ORCID: https://orcid.org/0000-0001-8201-7145)
- Ye Li (ORCID: https://orcid.org/0000-0002-2379-5497)
- Xiaoyan Li (ORCID: https://orcid.org/0000-0002-7454-7821)
- Jizhong Zhao (ORCID: https://orcid.org/0000-0003-1037-9619)
Institutions
- Dalian University of Technology (CN)
Publication Details
- Published
- 2025-09-09
- DOI
- https://doi.org/10.1117/12.3073951
- Primary Topic
- Traffic Prediction and Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00