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.
@article{arxiv.2310.13906,
title = {Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer},
author = {Junwei You and Ying Chen and Zhuoyu Jiang and Zhangchi Liu and Zilin Huang and Yifeng Ding and Bin Ran},
journal= {arXiv preprint arXiv:2310.13906},
year = {2024}
}