Driver assistance collision warning system using a LIDAR sensor with kinematics and perception algorithms

Abstract

Road accidents remained a significant global concern, causing loss of life and economic damage. To mitigate this issue, the automotive industry has increasingly invested in Advanced Driver Assistance Systems to enhance vehicle safety. This research presented a Driver Assistance Collision Warning System that incorporated kinematics and perception algorithms to improve collision prevention. The system utilized a LIDAR sensor to capture real-time data regarding the distance to the vehicle in front of it. This data was integrated with an Arduino microcontroller to compute the relative speed and time of collision. Upon detecting a collision risk, the system triggered a warning mechanism, which included an audible alert provided by a buzzer and a visual warning displayed on the head-up display. The system integrated kinematics algorithms, which processed sensor-read values to generate real-time decisions utilizing a specific threshold time to collision, and perception algorithms relied on Fuzzy Logic to handle uncertainty and improve accuracy. Validation was conducted through integration, system, and acceptance testing, demonstrating reliable synchronization of algorithms and accurate operation in real-world environments. The results showed that the system achieved a collision risk detection accuracy of ±5 cm within five different environmental factors. These findings confirmed the system's potential as a reliable solution for real-world collision prevention.