Design and fabrication of heavy vehicle rollover warning device by IoT based monitoring

Mohammad Rozaimin, Mohammad Ammar (2024) Design and fabrication of heavy vehicle rollover warning device by IoT based monitoring. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Heavy vehicle rollovers are a significant concern, contributing to a high number of fatal accidents worldwide. Statistics show that rollover incidents account for a substantial portion of traffic accidents, often resulting in severe injuries and fatalities. This project addresses this critical issue by designing and fabricating a heavy vehicle rollover warning device using an Internet of Things (IoT)-based monitoring system. The primary objectives are to develop a rollover warning system specifically for heavy-duty trucks and to integrate the IoT system for real-time monitoring. The research methodology involves a two-pronged approach. The first component utilizes Software-in-the-Loop (SIL) simulation, employing TruckSim and MATLAB/Simulink to create and optimize a Modified Odenthal Rollover Index (MORI) algorithm for accurately predicting rollover risks. The second component involves Hardware-in-the-Loop (HIL) implementation, where the MORI algorithm is integrated with an ESP32 microcontroller, sensors, and a Blynk app to develop a functional rollover warning device. Real-time monitoring using the Blynk IoT platform ensures that critical safety alerts are promptly communicated to drivers. The results from the SIL simulations demonstrate that the MORI algorithm provides earlier warnings, specifically 1.13 seconds sooner than the original Odenthal rollover index, allowing sufficient time for drivers to implement corrective actions. Additionally, the integration of the MORI algorithm in MATLAB/Simulink with real-time monitoring via the Blynk app showed excellent synchronization, confirming the reliability and practicality of the IoT-based monitoring system. The system consistently achieved identical RSF threshold crossing times in simulation and real-time monitoring, ensuring robust and accurate rollover detection under various loading and speed conditions. This comprehensive approach highlights the potential of combining advanced rollover detection algorithms with IoT technologies to enhance road safety, reduce accidents, and protect lives in the heavy vehicle industry.

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