Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914930416943104 |
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| author | You, Junwei Chen, Ying Jiang, Zhuoyu Liu, Zhangchi Huang, Zilin Ding, Yifeng Ran, Bin |
| author_facet | You, Junwei Chen, Ying Jiang, Zhuoyu Liu, Zhangchi Huang, Zilin Ding, Yifeng Ran, Bin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_13906 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer You, Junwei Chen, Ying Jiang, Zhuoyu Liu, Zhangchi Huang, Zilin Ding, Yifeng Ran, Bin Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2310.13906 |