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arXiv · 2602.01832

Synesthesia of Vehicles: Tactile Data Synthesis from Visual Inputs

Rui Wang·Yaoguang Cao·Yuyi Chen·Jianyi Xu·Zhuoyang Li·Jiachen Shang·Shichun Yang
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Abstract

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.

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Rui Wang, Yaoguang Cao, Yuyi Chen, Jianyi Xu, Zhuoyang Li, Jiachen Shang, Shichun Yang. 2026-02-02. Synesthesia of Vehicles: Tactile Data Synthesis from Visual Inputs. https://arxiv.org/abs/2602.01832

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