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.

本研究提出了 Gramian Angular Field Vision Transformer (GAF-ViT) 模型，用于分析自动驾驶汽车 (AV) 的驾驶行为。该模型通过将多变量行为的代表序列转化为多通道图像，并应用图像识别技术进行行为分类，实现了对多通道图像的特征驾驶行为的识别。对 Waymo Open 数据集的实验评估表明，该模型实现了最先进的性能，同时消融研究有效地证实了模型中各个组件的功效。