A Constructive Role for Social Science in the Development of Automated Vehicles Based on LFM-BiGRU Approach

This proposed system delves into the transformative realm of autonomous vehicles, a technological marvel that has captivated society's anticipation for its profound societal implications. While existing social science research predominantly focuses on regulatory, safety, and efficiency considerations, this method contends that these discussions fall short in comprehensively appreciating the extensive social ramifications of autonomous vehicles. Addressing this gap, the system aims to provide a more nuanced understanding of the broader societal impacts that autonomous vehicles are poised to instigate. The proposed methodology comprises three integral modules: preprocessing, feature extraction, and model training. Preprocessing involves essential tasks such as punctuation removal, URL elimination, and batch normalization. The feature extraction stage leverages TF-IDF, contributing to the discernment of significant features. For model training, the paper employs the LFM-BiGRU architecture, a distinctive aspect of the proposed
method. Comparative analysis against conventional approaches, namely BiGRU-LSTM and BiLSTM, underscores
the superior performance exhibited by the proposed method.
This suggests that the LFM-BiGRU approach holds promise
for advancing the capabilities of autonomous vehicle research and, consequently, societal understanding of this
groundbreaking technology.