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
Abstract
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.