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Effective classification of autonomous vehicle (AV) driving behavior emergesas a critical area for diagnosing AV operation faults, enhancing autonomousdriving algorithms, and reducing accident rates. This paper presents theGramian Angular Field Vision Transformer (GAF-ViT) model, designed to analyzeAV driving behavior. The proposed GAF-ViT model consists of three keycomponents: GAF Transformer Module, Channel Attention Module, and Multi-ChannelViT Module. These modules collectively convert representative sequences ofmultivariate behavior into multi-channel images and employ image recognitiontechniques for behavior classification. A channel attention mechanism isapplied to multi-channel images to discern the impact of various drivingbehavior features. Experimental evaluation on the Waymo Open Dataset oftrajectories demonstrates that the proposed model achieves state-of-the-artperformance. Furthermore, an ablation study effectively substantiates theefficacy of individual modules within the model.

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