Real-Time Inter-Vehicle Distance Estimation and Multi-Vehicle Detection Using FMCW Radar with Corner Reflectors and Reflective Metal Sheets

This dataset supports the research study titled "Blind-Spot Vehicle Distance Estimation Using High-Frequency FMCW Radar and Passive Metal Reflectors", conducted since April 2025. The study proposes a novel method for estimating inter-vehicle distances when preceding vehicles are located in blind-spot environments. The method combines a 24 GHz high-frequency FMCW radar with passive metal sheets and triangular corner reflectors to enhance radar signal reflection and detection capabilities. Specifically, the system exploits the radiation directivity of the radar, the boundary reflection characteristics of passive metal sheets, and the high radar cross-section (RCS) gain of corner reflectors. By analyzing the reflected signals based on the transmission angle and discrete Fourier transform (DFT) results, the proposed method enables the detection of multiple preceding vehicles even under non-line-of-sight (NLOS) conditions where conventional sensors such as cameras, LiDAR, or standard millimeter-wave radars fail. Experimental validation was performed using three vehicles, including the radar-equipped one, with variable inter-vehicle distances and radar transmission angles. The method achieved an average distance estimation error of approximately 0.36 m under blind-spot conditions. This research demonstrates that accurate vehicle distance estimation can be achieved even in NLOS environments, contributing to the advancement of environmental perception technologies for next-generation mobility systems. Furthermore, the proposed approach has potential applications in improving road safety by preventing chain-reaction collisions, particularly those caused by delayed human responses or operational errors among elderly drivers.