Synesthesia of Vehicles: Tactile Data Synthesis from Visual Inputs
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910008607768576 |
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| author | Wang, Rui Cao, Yaoguang Chen, Yuyi Xu, Jianyi Li, Zhuoyang Shang, Jiachen Yang, Shichun |
| author_facet | Wang, Rui Cao, Yaoguang Chen, Yuyi Xu, Jianyi Li, Zhuoyang Shang, Jiachen Yang, Shichun |
| contents | Autonomous vehicles (AVs) rely on multi-modal fusion for safety, but current visual and optical sensors fail to detect road-induced excitations which are critical for vehicles' dynamic control. Inspired by human synesthesia, we propose the Synesthesia of Vehicles (SoV), a novel framework to predict tactile excitations from visual inputs for autonomous vehicles. We develop a cross-modal spatiotemporal alignment method to address temporal and spatial disparities. Furthermore, a visual-tactile synesthetic (VTSyn) generative model using latent diffusion is proposed for unsupervised high-quality tactile data synthesis. A real-vehicle perception system collected a multi-modal dataset across diverse road and lighting conditions. Extensive experiments show that VTSyn outperforms existing models in temporal, frequency, and classification performance, enhancing AV safety through proactive tactile perception. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01832 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Synesthesia of Vehicles: Tactile Data Synthesis from Visual Inputs Wang, Rui Cao, Yaoguang Chen, Yuyi Xu, Jianyi Li, Zhuoyang Shang, Jiachen Yang, Shichun Artificial Intelligence Autonomous vehicles (AVs) rely on multi-modal fusion for safety, but current visual and optical sensors fail to detect road-induced excitations which are critical for vehicles' dynamic control. Inspired by human synesthesia, we propose the Synesthesia of Vehicles (SoV), a novel framework to predict tactile excitations from visual inputs for autonomous vehicles. We develop a cross-modal spatiotemporal alignment method to address temporal and spatial disparities. Furthermore, a visual-tactile synesthetic (VTSyn) generative model using latent diffusion is proposed for unsupervised high-quality tactile data synthesis. A real-vehicle perception system collected a multi-modal dataset across diverse road and lighting conditions. Extensive experiments show that VTSyn outperforms existing models in temporal, frequency, and classification performance, enhancing AV safety through proactive tactile perception. |
| title | Synesthesia of Vehicles: Tactile Data Synthesis from Visual Inputs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.01832 |