The growing number of road accidents, especially in low-visibility conditions, highlights the urgent need for robust vehicle speed detection and collision avoidance systems. Traditional sensors such as cameras and LiDAR struggle in fog, rain, or night, limiting their effectiveness in real-world scenarios. This research presents a MIMO OFDM-based Integrated Sensing and Communication (ISAC) system enabled vehicle speed detection for collision avoidance in intelligent transportation systems. By leveraging uniform linear MIMO arrays and OFDM modulation, the system achieves high-resolution radar sensing and robust data communication simultaneously. Dynamic targets are identified and tracked using range-Doppler processing, while communication quality is assessed through QAM constellation analysis. The system demonstrates effective Doppler-based velocity estimation, precise target localization, and reliable data transmission under realistic multipath and cluttered conditions, highlighting ISAC's potential for real-time vehicular applications while enhancing network security through location-aware authentication and jamming resistance, and improving safety via real-time situational awareness. |
