Due to the challenge of limited line of sight in the perception system of intelligent driving vehicles (cameras, radar, body sensors, etc.), which can only perceive threats within a limited range, potential threats outside the line of sight cannot be fed back to the driver. Therefore, this article proposes a safety perception detection method for beyond the line of sight for intelligent driving. This method can improve driving safety, enabling drivers to perceive potential threats to vehicles in the rear areas beyond the line of sight earlier and make decisions in advance. Firstly, the electronic toll collection (ETC) transaction data are preprocessed to construct the vehicle trajectory speed dataset; then, wavelet transform (WT) is used to decompose and reconstruct the speed dataset, and lightweight gradient noosting machine learning (LightGBM) is adopted to train and learn the features of the vehicle section speed. On this basis, we also consider the features of vehicle type, traffic flow, and other characteristics, and construct a quantitative method to identify potential threat vehicles (PTVs) based on a fuzzy set to realize the dynamic safety assessment of vehicles, so as to effectively detect PTVs within the over-the-horizon range behind the driver. We simulated an expressway scenario using an ETC simulation platform to evaluate the detection of over-the-horizon PTVs. The simulation results indicate that the method can accurately detect PTVs of different types and under different road scenarios with an identification accuracy of 97.66%, which verifies the effectiveness of the method in this study. This result provides important theoretical and practical support for intelligent driving safety assistance in vehicle–road collaboration scenarios.

The research explores methods for onboard real-time data analysis and health status assessment, focusing on the working principles and status assessment theory of onboard equipment in the electronic toll collection (ETC) system. By applying AHP, fuzzy comprehensive evaluation method, entropy method, and Delphi method, a practical health status assessment model for onboard equipment has been established, providing an innovative path for dynamic real-time monitoring of onboard devices. The research first constructs an indicator system and assessment model, combining expert scoring with fuzzy judgment to objectively quantify the status of onboard equipment, ultimately resulting in a health status assessment indicator system for the objects under evaluation. During the assessment process, expert scoring is used to determine the subjective weights via AHP, and the combined weights are derived by incorporating objective weights, with instance results showing that the comprehensive weights of various indicators lie between the subjective and objective weights. The research also utilizes the Weibull distribution to simulate the failure rate of onboard equipment, indicating that the failure modes of the onboard devices are closely related to their operational status, providing a theoretical basis for future technological improvements. Through empirical analysis of the assessment model, the research verifies the superiority of the comprehensive weights and emphasizes its new approach in health status assessment of onboard devices, opening up new avenues for research in related fields.