IoT based black box for vehicles

• Main Authors: Aldholay, Mohammed Hussein Hassan, Susiapan, Yvette Shaan-Li, Abdul Rahim, Ruzairi, Abdul Manaf, Mohamad Shukri, Ahmad, Nasarudin, Mohammad, Elmy Johana, Shamsuddin, Abu Ubaidah
• Other Authors: nil, nil
• Format: Book Section
• Language: English
• Published: Penerbit UTHM 2020
• Subjects: TA165 Engineering instruments, meters, etc. Industrial instrumentation
• Online Access: http://eprints.uthm.edu.my/3112/1/Ch08%20IOT%20BASED%20BLACK%20BOX%20FOR%20VEHICLES.pdf; http://eprints.uthm.edu.my/3112/
• Institution: Universiti Tun Hussein Onn Malaysia

Transportation is considered one of the most basic requirements for the infrastructure in the 21st century in order to save time and effort. One of the main problems in transportation is the ambiguity of time delay because of traffic jams, or a fixed transportation schedule like the bus or train system. Therefore, individuals prefer to own and drive their own private vehicle. Unfortunately, having more private vehicles on the road contributes to a higher daily accident rate. This project aims to provide a solution to reduce the incidences of traffic accidents being reported late, thus causing delay in possible life-saving efforts which then results in more fatalities. The research covers the design of a black box for vehicles with the same concept as a black box placed on an aircraft. The black box in this project will be able to detect if an accident has happened by using the relevant sensors to collect the information from the vehicle. These data include detecting hard impacts on the body of the vehicle, vehicle speed as well as orientation, and then communicating with the cloud. Once the data collected from the sensors reaches the cloud, the coinciding information from all sensors will be processed, and an appropriate alert will be sent out. Sensors can be falsely triggered and so the system does not immediately trigger the alert system to send out a notification based on the data received from just a single sensor. Instead, it evaluates the event using coinciding data received from all sensors to verify the probability of an accident.