Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer

Effective classification of autonomous vehicle (AV) driving behavior emerges as a critical area for diagnosing AV operation faults, enhancing autonomous driving algorithms, and reducing accident rates. This paper presents the Gramian Angular Field Vision Transformer (GAF-ViT) model, specifically designed for analyzing AV driving behavior. The GAF-ViT model is developed upon a novel integration of three key components: GAF Transformation Module, which transforms multivariate driving behavior representative sequences into multi-channel images; Channel Attention Module, which prioritizes relevant behavioral features to enhance classification effectiveness; and Multi-Channel ViT Module, which employs advanced image recognition techniques to accurately classify the resulting multi-channel driving behavior images. This framework not only facilitates detailed analysis of complex multivariate driving behavioral data but also leverages the capabilities of vision-based pattern recognition methods to uncover subtle driving behavior nuances. Experimental evaluation on the Waymo Open Dataset of trajectories demonstrates that the proposed model outperforms baseline models, achieving state-of-the-art performance. Furthermore, an ablation study effectively validates the efficacy of individual modules within the model.