Autonomous Vehicle Health Monitoring and Driver Safety System

Abstract

This project is to modernize vehicle maintenance processes and enhance driver safety through the integration of advanced autonomous and Internet of Things (IoT) technologies. Current vehicle service methods are impeded by manual inefficiencies, delayed maintenance, insufficient transparency, and the potential misuse of vehicles during servicing. These deficiencies not only jeopardize vehicle health but also pose significant risks to driver safety. To address these challenges, this initiative develops an advanced system that incorporates real-time monitoring, predictive analytics, and automated safety measures. The system employs embedded sensors to continuously assess critical vehicle parameters, including oil levels, engine conditions, and accident notifications. The utilization of the ESP32 enables connectivity with a mobile IoT application, and an auditory alert system, represented by a buzzer, serves to indicate faults and issues.The vehicle is equipped with a temperature sensor, vibration sensor, and fuel leakage sensor. In the event that any of these sensors are activated, the vehicle will automatically enter an off state. Subsequently, we will implement RFID technology for scanning and detection, which will facilitate the reactivation of the vehicle..

Introduction

Summary:

The project aims to revolutionize traditional vehicle maintenance and safety by integrating advanced autonomous systems, IoT connectivity, and sensor-based monitoring. Conventional vehicle servicing faces issues such as delayed fault detection, manual diagnostics, and potential misuse, risking vehicle performance and driver safety.

The proposed system uses an ESP32 microcontroller connected to multiple sensors (temperature, vibration, fuel leakage) and biometric monitors to continuously assess vehicle health and driver condition in real-time. Key features include automatic fault detection, vehicle ignition cut-off upon hazardous conditions, and secure restart enabled only via authorized RFID verification to prevent unauthorized use.

All sensor data is transmitted to a mobile IoT application for remote monitoring, real-time alerts, fault logging, and predictive maintenance. Biometric sensors enhance safety by detecting driver fatigue and distractions. The system thus provides proactive, transparent, and autonomous vehicle maintenance, improving safety, security, and operational efficiency while reducing costs and preventing accidents.

Conclusion

This paper effectively illustrates an innovative and sophisticated approach to vehicle maintenance and driver safety, utilizing the capabilities of Internet of Things (IoT) technology, sensor applications, and autonomous control systems. By integrating embedded sensors—including those for temperature, vibration, and fuel leakage—with RFID authentication and ESP32-based mobile connectivity, the system enables real-time fault detection, secure vehicle operation, and proactive maintenance notifications. The vehicle shutdown feature, when paired with RFID-based restart functionality, introduces a vital layer of safety and prevention against misuse, particularly in cases of significant faults. Furthermore, the incorporation of biometric monitoring to detect driver fatigue and distraction enhances overall safety by encouraging vigilant driving practices and mitigating accident risks. The mobile IoT application functions as a centralized platform for real-time updates, fault logs, and historical data analysis, thereby improving the transparency and efficiency of vehicle diagnostics. In summary, this system successfully bridges the divide between conventional vehicle maintenance methodologies and the advancing technological landscape of autonomous vehicles. It not only enhances operational performance and reduces operational costs but also instills confidence in the reliability and safety of smart transportation systems. This project establishes a robust foundation for the development of intelligent mobility and automobile health management solutions in the future..

References

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Copyright

Copyright © 2025 Nandhini B, Mathiyarasan N, Meyyanathan S, Ponparthipan M, Pradeep K S . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.