DRIVER ASSISTANCE COLLISION WARNING SYSTEM USING LIDAR SENSOR WITH KINEMATICS AND PERCEPTION ALGORITHMS

Abstract

Road accidents remain a significant global concern, causing loss of lives and economic losses. To address this issue, the automotive industry has been increasingly investing in advanced driver assistance systems (ADAS) to enhance vehicle safety. This research presents a Driver Assistance Collision Warning System that incorporates a kinematics and perception algorithms approach to improve collision prevention. The driver assistance collision warning system utilizes a LIDAR sensor as its input to capture realtime data regarding the distance to next vehicle, the data is then integrated with an Arduino microcontroller to compute the relative speed and Time-to-Collision (TTC). In response to detected collision risk, the system triggers a warning mechanism using an audible warning provide by a buzzer and a visual warning generated by LEDs connected to a head-up display. The kinematics algorithm utilizes a specific threshold time-to-collision (TTC) value to determine the state of emergency collision risk. The perception algorithm utilizes a fuzzy logic system to predict the collision risk. As a result, the system could perform well as an integrated system, as the algorithms can operate harmoniously without any interference against each other. In the real-world environment, the system operates reliably since it has successfully passed the acceptance tests.