Spectrum Quantification-Based Safety Efficiency Evaluation of Autonomous Vehicle Under Random Cut-in Scenarios

Continuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence. While the UN R157 Regulation evaluates automated lane-keeping system (ALKS) performance baselines through safe collision plots (SCPs) in various scenario clusters, quantifying the specific ALKS safety efficiency remains challenging. We propose a spectrum quantification approach to evaluate the safety efficiency of autonomous vehicles in cut-in scenarios. First, we collected speed-distance data under different cut-in scenarios and extracted essential spectral features to indicate the vehicle motion parameters during the cut-in process. Second, by utilizing Fourier analysis, a spectral analysis model was built to quantify and analyze the vehicle motion characteristics, providing insights into scenario safety. Finally, we created approximate analytical equations for the normalized disturbance frequencies in the nonlinear response scenarios of autonomous driving systems by combining the SCP with a frequency spectrum analysis model. The results showed that the normalized disturbance frequency in the cut-in scenario was approximately 0.2. When the relative longitudinal distance and speed of the vehicle are the same, if the cut-in speed of the cut-in vehicle is larger, the normalized disturbance frequency is higher, indicating that the cut-in process of the autonomous vehicle is more dangerous and may trigger a collision.