IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management - Rudrusamy, Bhuvendhraa; Teoh, Hock Chye; Pang, Jia Yew; Lee, Tou Hong; Chai, Sung Choong (International Journal of Integrated Engineering, 2023)
Curbing road accidents has always been one of the utmost priorities in every country. In Malaysia, Traffic Investigation and Enforcement Department reported that Malaysiaâ€™s total number of road accidents has increased from 373,071 to 533,875 in the last decade. One of the significant causes of road accidents is driverâ€™s behaviours. However, driversâ€™ behaviour was challenging to regulate by the enforcement team or fleet operators, especially heavy vehicles. We proposed adopting the Internet of Things (IoT) and itsâ€™ emerging technologies to monitor and alert driverâ€™s behavioural and driving patterns in reducing road accidents. In this work, we proposed a lane tracking and iris detection algorithm to monitor and alert the driverâ€™s behaviour when the vehicle sways away from the lane and the driver feeling drowsy, respectively. We implemented electronic devices such as cameras, a global positioning system module, a global system communication module, and a microcontroller as an intelligent transportation system in the vehicle. We implemented face recognition for person identification using the same in-vehicle camera and recorded the working duration for authentication and operation health monitoring, respectively. With the GPS module, we monitored and alerted against permissible vehicleâ€™s speed accordingly. We integrated IoT on the system for the fleet centre to monitor and alert the driverâ€™s behavioural activities in real-time through the user access portal. We validated it successfully on Malaysian roads.  The outcome of this pilot project benefits 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 driverâ€™s behavioural in the future.