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

arXiv: https://arxiv.org/abs/2310.13906 · HTML: https://mlsanity.com/paper/2310.13906/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 78 references that are in the map.
- First posted on arXiv 21 Oct 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [Pay Attention to MLPs](https://mlsanity.com/paper/2105.08050/) (2021)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Multivariate LSTM-FCNs for Time Series Classification](https://mlsanity.com/paper/1801.04503/) (2018)
- [Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions](https://mlsanity.com/paper/1906.08945/) (2019)
- [Imaging Time-Series to Improve Classification and Imputation](https://mlsanity.com/paper/1506.00327/) (2015)
- [Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis](https://mlsanity.com/paper/1806.08946/) (2018)
- [Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles](https://mlsanity.com/paper/2007.02574/) (2020)
- [Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification](https://mlsanity.com/paper/1812.07683/) (2018)
- [MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction with Automation](https://mlsanity.com/paper/1711.06976/) (2017)
- [Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets with Multi-stream Inputs](https://mlsanity.com/paper/1811.09128/) (2018)
- [How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior](https://mlsanity.com/paper/1706.07637/) (2017)
- [Gramian Angular Fields for leveraging pretrained computer vision models with anomalous diffusion trajectories](https://mlsanity.com/paper/2310.01416/) (2023)
- [A Machine Learning Smartphone-based Sensing for Driver Behavior Classification](https://mlsanity.com/paper/2202.01893/) (2022)

## Built on by (most cited first, 1 of 1)

- [LLM-MLFFN: Multi-Level Autonomous Driving Behavior Feature Fusion via Large Language Model](https://mlsanity.com/paper/2603.02528/) (2026)

## Abstract (arXiv)

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.