Understanding and classifying vehicle driving behavior is critical for the development of intelligent transportation systems (ITS) and road safety analytics. In this paper, we propose a novel frequency-domain descriptor called the Spectral Curvature Signature (SCS), which characterizes the temporal dynamics of vehicle trajectory curvature through spectral and fractal features. The proposed framework processes raw (x, y) trajectories using a Savitzky–Golay filter, computes curvature over time, and applies a Fourier Transform to extract a set of compact, interpretable descriptors, including spectral energy, centroid frequency, Hurst exponent, and Band Energy Ratios (BER). We evaluate the effectiveness of SCS on both synthetic trajectories generated from a kinematic model and real-world data from the US Highway 101 Dataset (NGSIM). After applying random undersampling to address label imbalance, the classification model achieves an accuracy of 99.8%, precision of 99.9%, recall of 99.7%, and an F1-score of 99.8% in distinguishing between safe and unsafe driving behaviors using a Random Forest classifier. Feature analysis reveals spectral energy as the most discriminative metric, while the Hurst exponent offers limited utility due to the short duration of observed sequences. The method also demonstrates robustness to data noise and class imbalance. Overall, SCS provides a principled, interpretable, and low-dimensional alternative to traditional time-domain motion descriptors, with strong potential for integration into large-scale traffic monitoring and driver behavior analytics systems.