Vehicle-to-Vehicle (V2V) communication is a crucial component of Intelligent Transportation Systems (ITS) that enables real-time data exchange among vehicles to improve road safety and traffic efficiency. This paper presents a Wi-Fi-enabled vehicle to vehicle communication system using ESP32 modules for scalable deployment. Conventional vehicle safety systems rely heavily on driver perception and are limited by line-of-sight visibility and delayed human response, increasing accident risks. The proposed system integrates Ultrasonic and Motion Processing Unit (MPU6050) sensors to detect hazards such as obstacles, sudden braking, and abnormal environmental conditions. The processed data is transmitted wirelessly using Wi-Fi to nearby vehicles, enabling early warning alerts. The receiving vehicle providesbuzzer notifications to alert the driver. The system improves situational awareness, reduces reaction time, and minimizes collision risks. It supports future applications in smart cities and autonomous vehicles.
Introduction
The paper addresses the increasing risk of road accidents caused by human error and limited situational awareness in modern traffic environments. To improve safety, it proposes a real-time Vehicle-to-Vehicle (V2V) communication system that allows vehicles to exchange critical data such as speed, distance, and hazard alerts, enabling drivers to react proactively.
Existing V2V approaches use technologies like DSRC, Wi-Fi, Bluetooth, and sensors such as ultrasonic and gas detectors. While these methods improve detection and communication, they are limited by short range, lack of scalability, and insufficient real-time integration.
The proposed system introduces a Wi-Fi-based V2V communication framework using ESP32 microcontrollers to enable direct vehicle communication without external infrastructure. Vehicles are equipped with sensors (ultrasonic, gas, vibration) to detect obstacles, environmental risks, and sudden impacts. When hazards are detected, the system sends real-time alerts to nearby vehicles.
On the receiver side, the system processes incoming data and triggers visual (LCD) and audio (buzzer) alerts, and can activate automatic braking in critical situations. This improves driver awareness, reduces reaction time, and enhances road safety in dynamic traffic conditions.
Conclusion
The proposed WiFi-enabled Vehicle-to-Vehicle (V2V) communication system improves road safety through real-time transmission of critical parameters such as speed and distance. The integration of sensors with the ESP32 microcontroller ensures accurate detection and efficient communication between vehicles.
At the receiver side, automatic braking along with buzzer indication and LCD display enables immediate response to critical conditions, reducing the risk of collisions. Overall, the system provides an effective and reliable solution for enhancing vehicular safety and responsiveness.
References
[1] J. Smith et al., “Survey of V2V Communication Protocols,” IEEE Communications Surveys & Tutorials, 2018.
[2] R. Jones and L. Wang, “Ultrasonic Sensors for Vehicular Proximity Detection,” IEEE Sensors Journal, 2019.
[3] X. Chen et al., “Effectiveness of Auditory Alerts in Driver Assistance Systems,” IEEE Trans. Intelligent Transportation Systems, 2020.
[4] H. Kim and S. Lee, “I2C Communication in Embedded Systems,” IEEE Embedded Systems Letters, 2017.
[5] J. B. Kenney, “Dedicated Short-Range Communications (DSRC) Standards in the United States,” Proceedings of the IEEE, 2011.
[6] A. Bazzi et al., “On the Performance of IEEE 802.11p and LTE-V2V for the Cooperative Awareness of Connected Vehicles,” IEEE Trans. Vehicular Technology, 2017.
[7] A. Ali et al., “Cooperative Resource Management for C-V2X Communications,” IEEE Access, 2022.
[8] H. Hartenstein and K. Laberteaux, VANET: Vehicular Applications and Inter-Networking Technologies, Wiley, 2010.
[9] K. Abboud, H. Omar, and W. Zhuang, “Interworking of DSRC and Cellular Network Technologies for V2X Communications,” IEEE Trans. Vehicular Technology, 2016.
[10] Y. Zhang et al., “Machine Learning in Intelligent Vehicular Networks,” IEEE Communications Magazine, 2021.
[11] S. Lee and J. Park, “Human-Machine Interaction in Intelligent Vehicles,” IEEE Trans. Human-Machine Systems, 2018.
[12] Q. Wang et al., “Sensor Fusion for Intelligent Vehicles,” IEEE Sensors Journal, 2020.