Abstract:

To enhance the intelligent system's understanding of individual driving behavior under human-machine interaction driving circumstances, an en-route driving style recognition method based on LDA model was proposed. The method explored vehicle trajectory information from multi-dimensions to quickly extract and identify latent driving style features of drivers. Firstly, the semantic understanding rules of driving behavior were established to discretize continuous trajectory data into semantic vocabularies of driving behavior, considering the scene perception layer, pattern layer, operation layer and vehicle status layer. Secondly, according to topic perplexity and consistency, habitual driving styles were classified into four categories: stable, conservative, moderate and aggressive. Finally, each driver's en-route driving style was identified as a probabilistic combination of the aforementioned driving styles. The results show that the proposed en-route driving style recognition method considers drivers' heterogeneity and explains the phenomenon of the same driver exhibiting different driving styles in varying driving environments. Additionally, this research improves the comprehensiveness and comprehensibility of en-route driving style recognition.

Key words: latent Dirichlet allocation (LDA) topic model, en-route driving style, trajectory data, semantic understanding, driving behaviour