Enhancing FMCW radar accuracy for adaptive cruise control using CFAR algorithms in adverse weather

Abstract Adaptive cruise control (ACC) relies on accurate range and relative-velocity measurements of the preceding vehicle, which frequency-modulated continuous-wave (FMCW) radar provides. Under adverse conditions, however, clutter from rain, fog, and insect swarms degrades these measurements. This study presents a 77 GHz FMCW radar framework for ACC that integrates physically motivated models of rain, fog, and insect clutter with a two-dimensional cell-averaging constant false alarm rate (CA-CFAR) detector. By adapting its threshold to the local clutter level, the detector suppresses clutter-induced peaks and enables more reliable identification of the target response. Simulations up to 200 m show that all three clutter types increase the estimation errors with clutter intensity and distance, and that CFAR processing consistently reduces them. At 83 m under 25 mm/h rain, the range error decreases from 0.50 m to 0.24 m; at 63 m, the velocity error decreases from 4.7% to 2.3%; and at 200 m in dense fog, the range error decreases from 11.25% to 6.20%. The framework detects three targets up to 200 m and resolves velocities as low as 0.0083 m/s, supporting reliable ACC operation in adverse weather.