Aidil Redza Khan Mohamed Amir Khan creator author. text theses my 2021 monographic eng xxi, 214 pages : illustrations (some color) ; 30 cm. + 1 CD ROM Rising number of road accidents have been a common issue that needs to be given attention where most of it causes fatal injury and death. 30% of accidents are involving rear-to-end crashes meanwhile more than 900,000 cases in a year are related to rearblind-spots. Even though safety improvements have been upgraded such as introduction of Assistance Driving Assistance System (ADAS), yet the numbers are still on its endangering path. To solve this issue, Vehicle Ad-hoc Networks (VANET) system is invented to ensure a safer environment for drivers and pedestrians. Vehicle-toInfrastructure (V2I) and Vehicle-to-Vehicle (V2V) Communication System is one of the technologies created under VANET . This dissertation presented the new V2V and V2I system that is applicable to avoid collisions with development of On-Board Unit (OBU) and Roadside Unit (RSU) prototype using Internet of Things (IoT) technology. Single-Board Computers (SBC) is integrated with sensors such as GPS, LiDAR and ultrasonic for OBU while DHT22, CO gas sensor, PM sensor and rain sensor for RSU. Both OBU and RSU connected to internet via 4G module integrated on the SBC which also function as Apache-MySQL-PHP (AMP) server. Location Tracker, Forward Collision Warning (FCW) and Blind Spot Warning (BSW) application is embedded into OBU located in a vehicle known as a Subject Vehicle (SV). All testing involved with obstacle vehicle known as Host Vehicle (HV) executed at Universiti Malaysia Pahang (UMP) Pekan campus. Finding shows that OBU‟s location is as accurate as 0.0124% in latitude while 0.0084% in longitude in real-time at 60 km/h. Such GPS accuracy allow FCW application to generate alert at CP of 80% to the driver. FCW developed is tested at different speed of SV and HV and findings shows that alert is generated at a safe distance and sufficient time for the driver to react. Throughout the field testing, the new TTC has been successfully formulated and verified where the real time distance has been subtracted to 1 meter over current speed. Collision percentage (CP) of 80% is still generated even though the average lagging time (LT) delay of SBC is recorded at 1.3 seconds. The new formulated TTC and CP proven that the driver has ample time to respond to the generated alert, e.g., for the case of HV is at 0 km/h and SV is at 60 km/h, alert is generated at CP of 84.04% with TTC recorded at 2.4s, which is almost aligned with recommendation of International Organizations of Standardization 2013 stating 2.6s is the best time for driver to react. Even though there was a slight delay with the alerts, with consideration of 1m safe distance and 1.3s LT, driver was able to pull off a safe braking after the alert to slow down SV thus to avoid collision from happening. For BSW application, promising results by having 1 second delay in detecting blinded HV at the constant span of 40km/h speed limit between SV and HV which is an enabler to the safe lane changing operation. The presence of host vehicle (HV) or any obstacles is detected in the blinded area of SV. In contrast to OBU, RSU is developed to monitor the weather which in turn influenced the road conditions and eventually lead to the traffic status monitoring. The RSU‟s sensors are sensitively detected the haze, rain, temperature and humidity accurately. Therefore, this system is potentially to produce Variable Speed Limit (VSL) based on the environment conditions. Speed Limit information from the RSU can be accessed through the OBU inside the vehicles using internet from the 4G technology. Implementation of IoT technology has proven to assist the drivers in avoiding collisions thuspotential to reduce the road accidents. specialized Aidil Redza Khan Mohamed Amir Khan College of Engineering Thesis (Master of Science) -- Universiti Malaysia Pahang – 2021 Includes bibliographical references College of Engineering Dissertations Universities and colleges Dissertations Theses THE0009355(Local) UMP 220428 20251125110042.0 eng