Rational design of a vision fusion system with visible and near-infrared spectral integration for improved environmental perception

作者：Sen Zhang, Pingdan Xiao, Qinghui Hong, Lin Tang, Zhengdao Xie, Rui He, Bei Jiang, Xitong Hong, Xinjie Li, Haodi Zhu, Ruohao Hong, Chang Liu, Xingqiang Liu, Yawei Lv, Yang Chai, Lei Liao, Xuming Zou · 发表于：National Science Review · 年份：2025 · DOI：10.1093/nsr/nwaf204 · 被引用次数：8 · 研究领域：Advanced Image Fusion Techniques、Infrared Target Detection Methodologies、Remote-Sensing Image Classification

ABSTRACT With the rapid advancements in autonomous driving, pure vision-based solutions have garnered significant attention. However, existing vision sensors are limited by their specific spectral operating ranges and the complexity of processing hybrid optical/electrical signals. In this study, we present a fully circuit-emulated vision system that employs a vision fusion solution for autonomous driving, integrating image sensing, fusion, edge extraction, and decision-making functionalities. This system utilizes vision sensors featuring an Al2O3/two-dimensional Ruddlesden-Popper perovskite (2D PVK) heterostructural dielectric and MoS2/black phosphorus (BP)/MoS2 heterostructural channel, which exhibits persistent nonvolatility and fully light-tunable positive and negative photoresponses when exposed to 1064 nm and 532 nm light, respectively. Notably, when combined with edge extraction circuit design, our vision system achieves all-day visual perception with a 99.0% recognition accuracy for driving scenario information. The integration of the fully circuit-emulated vision system with the vision fusion solution enables a more comprehensive and accurate representation of the driving environment.