As a core component of intelligent transportation systems, vehicle localization technology enables accurate positioning, supporting comprehensive insights into traffic flow, vehicle status, and environmental changes. Current vehicle position localization technologies primarily rely on visual sensors and the Global Positioning System, while their performance can be affected by extreme weather conditions and signal stability. However, vehicle-generated sound, as a stable data source unaffected by environmental conditions and free from signal limitations, is often underutilized by existing studies. In this paper, we construct a vehicle localization dataset based on sound signals and further propose a combined filtering strategy that integrates adaptive filtering with spectral subtraction filtering, dynamically adjusting the filter parameters to suppress time-correlated noise within the signal. We also remove broadband noise in the frequency domain while preserving high-frequency signal details, offering a significant advantage over existing methods in terms of signal-to-noise ratio improvement. The proposed dataset and filtering strategy are validated using the EfficientNet-1D Fusion model. Experimental results demonstrate that the proposed combined filtering method excels in recognition accuracy and computational efficiency.
Zhang et al. (Fri,) studied this question.