Chain Accident Avoidance and Multifunctional Vehicle Safety using V2V Communication

Road accidents, particularly chain collisions involving multiple vehicles, are among the most severe traffic hazards, often resulting in significant loss of life and property. Conventional safety systems are reactive in nature and fail to allow enough time for drivers and vehicles to respond effectively. To address this challenge, the proposed system introduces a two-module intelligent accident avoidance and multifunctional vehicle safety mechanism based on Vehicle-to-Vehicle (V2V) communication. The first module is a robot vehicle prototype built using an ESP32 controller integrated with multiple sensors for real-time monitoring of road and driver conditions. An ultrasonic sensor is used for obstacle detection, while an LDR senses high-beam glare from oncoming vehicles, enabling automatic dimming and dipping of headlights via a relay. An alcohol sensor monitors driver sobriety, and an ADXL345 accelerometer detects tilting or sudden impacts for accident recognition. Additionally, IR sensors detect blind spot objects to prevent unsafe lane changes. An H-Bridge motor driver controls the robot’s DC motors, ensuring safe stop-and-go movement based on sensor data. Whenever any abnormal condition is detected, the robot stops immediately and communicates hazard information to nearby vehicles using Zigbee-based V2V communication. Simultaneously, critical alerts are sent to registered users through Telegram notifications, ensuring rapid response. The second module is a vehicle unit built around an Arduino controller, equipped with Zigbee and an LCD display to receive transmitted safety information from the robot module. This module alerts the driver about possible hazards such as an accident, obstacle, or high-beam situation, thereby extending the safety mechanism across multiple vehicles. This dual-module design creates a cooperative safety network where vehicles exchange situational awareness in real time, thereby preventing chain collisions and enhancing overall road safety. The system not only mitigates the effects of delayed driver reaction but also automates critical responses, ensuring proactive accident avoidance. The proposed model demonstrates a low-cost, scalable, and practical solution for integrating V2V communication, IoT sensors, and intelligent control mechanisms to improve vehicular safety and reduce chain accident occurrences.