Abstract: With the increasing awareness of privacy protection, identifying car drivers using vehicle trajectory data has become a hot topic in vehicle data analysis. However, existing models  struggle to accurately capture the relationship between driving style and driving context, resulting in low identification accuracy. Therefore, a driving context-aware driver identification model (CDIM) is proposed. CDIM utilizes trajectory data to calculate vehicle motion features and obtains travel routes through road network matching. It also designs a road segment information embedding module based on a bidirectional Transformer, which generates embeddings for each road segment in the travel route by fusing features of adjacent road segments. Then, a convolutional cross-modal attention fusion module is used to combine road segment features with motion features, achieving efficient fusion of the two. Additionally, external factor features are incorporated to comprehensively capture the influence of driving context on driving style. Experimental results on public datasets show that CDIM achieves a identification accuracy of 68.54%, which is an improvement of 8.14% and 4.81% compared to RM-Driver and Doufu, respectively, demonstrating higher driver identification accuracy.

Key words: driver identification, representation learning, context-aware, feature fusion