IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management

Curbing road accidents have always been one of the utmost priority of nations worldwide. In Malaysia, the Traffic Investigation and Enforcement Department reported that Malaysia's total number of road accidents have increased from 373,071 to 533,875 in the last decade. One of the significant causes of road accidents is the driver's behaviors. However, to regulate drivers' behavior by the enforcement team or fleet operators is challenging, especially for heavy vehicles. In our research, we have proposed the Internet of Things (IoT) scalability framework and its' emerging technologies to monitor and alert driver's behavioral and driving patterns to reduce road accidents. To prove this work, we have implemented a lane tracking, and iris detection algorithm, to monitor and alert the driver's behavior when the vehicle sways away from the lane, and to detect if the driver is feeling drowsy. We implemented electronic devices such as cameras, a global positioning system module, a global system communication module, and a microcontroller as the hardware for an intelligent system in the vehicle. We also applied face recognition for person identification using the same in-vehicle camera and recorded the working duration for authentication and operation health monitoring. With the GPS module, we monitored and alerted against permissible vehicle's speed accordingly. We integrated IoT on the system for the fleet centre to monitor and alert the driver's behavioral activities in real-time through the user access portal. We have validated it successfully on Malaysian roads. The outcome of this pilot project ensures the safety of drivers, public road users, and passengers. The impact of this framework leads to a new regulation by the government agencies towards merit and demerit system, real-time fleet monitoring of intelligent transportation systems, and socio-economy such as cheaper health premiums. The big data can be used to predict the driver's behavioral in the future.