Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer
This work addresses the need for effective behavior classification to diagnose faults and enhance algorithms in autonomous vehicles, representing an incremental improvement in a domain-specific area.
The paper tackles the problem of classifying autonomous vehicle driving behavior by proposing the Gramian Angular Field Vision Transformer (GAF-ViT) model, which achieves state-of-the-art performance on the Waymo Open Dataset.
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.