  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, designed to analyze
AV driving behavior. The proposed GAF-ViT model consists of three key
components: GAF Transformer Module, Channel Attention Module, and Multi-Channel
ViT Module. These modules collectively convert representative sequences of
multivariate behavior into multi-channel images and employ image recognition
techniques for behavior classification. A channel attention mechanism is
applied to multi-channel images to discern the impact of various driving
behavior features. Experimental evaluation on the Waymo Open Dataset of
trajectories demonstrates that the proposed model achieves state-of-the-art
performance. Furthermore, an ablation study effectively substantiates the
efficacy of individual modules within the model.
