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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

TL;DR

The paper addresses driving-behavior classification for autonomous vehicles to aid fault diagnosis and algorithmic improvement. It proposes GAF-ViT, which converts multivariate driving sequences into multi-channel images using Gramian Angular Field representations and classifies them with a Vision Transformer augmented by a channel-attention mechanism. Key contributions include (i) visualizing driving-behavior features via GAF, (ii) transforming multivariate sequences into image-like inputs for ViT-based classification, and (iii) demonstrating performance gains and ablation-backed module necessity on the Waymo trajectory dataset. The work holds practical significance for AV safety, enabling anomaly detection and adaptive control, with deployment strategies leveraging edge or RSU infrastructure for scalable real-time analysis.

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, 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.

Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer

TL;DR

The paper addresses driving-behavior classification for autonomous vehicles to aid fault diagnosis and algorithmic improvement. It proposes GAF-ViT, which converts multivariate driving sequences into multi-channel images using Gramian Angular Field representations and classifies them with a Vision Transformer augmented by a channel-attention mechanism. Key contributions include (i) visualizing driving-behavior features via GAF, (ii) transforming multivariate sequences into image-like inputs for ViT-based classification, and (iii) demonstrating performance gains and ablation-backed module necessity on the Waymo trajectory dataset. The work holds practical significance for AV safety, enabling anomaly detection and adaptive control, with deployment strategies leveraging edge or RSU infrastructure for scalable real-time analysis.

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, 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.
Paper Structure (19 sections, 17 equations, 9 figures, 3 tables, 2 algorithms)

This paper contains 19 sections, 17 equations, 9 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Vision-Based Methods
  4. Trajectory-Based Methods
  5. General Trajectory-Based Analysis
  6. Car-Following Behaviors
  7. Smartphone-Based Methods
  8. Methodology
  9. Structure of GAF-ViT Model
  10. GAF Transformation Module
  11. Channel Attention Module
  12. Multi-Channel ViT Module
  13. GAF-ViT Model Operation
  14. Experiment
  15. Dataset
  16. ...and 4 more sections

Figures (9)

  • Figure 1: Overview of GAF-ViT Model
  • Figure 2: Workflow of GAF Transformation Module
  • Figure 3: Structure of the Channel Attention Module
  • Figure 4: Structure of Multi-Channel ViT Module
  • Figure 5: Visualization of a Trajectory
  • ...and 4 more figures