Exploring Driving Behavior for Autonomous Vehicles Based on Gramian
  Angular Field Vision Transformer
Junwei You Ying Chen Zhuoyu Jiang Zhangchi Liu Zilin Huang Yifeng Ding Bin Ran
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.