A Bio‐Inspired Visual Sensor With UV‐to‐NIR Fusion for Enhanced Recognition in Autonomous Driving Systems

ABSTRACT Conventional vision systems for autonomous driving are hindered by energy‐inefficient and latency‐prone architectures due to physically segregated sensing, memory, and processing modules. Here, we report a bio‐inspired vision sensor that emulates the spectral adaptation mechanism of the Pacific salmon retina leveraging a van der Waals heterojunction of NbNiTe 5 and black phosphorus (BP). Operating at zero bias, the device exhibits intrinsic wavelength‐dependent antagonistic photoresponses: negative photoconductance under 365 nm ultraviolet illumination (mimicking visual suppression in bright environments) and positive photoconductance under 820 nm near‐infrared light (emulating visual enhancement in dim conditions). This built‐in adaptability enables robust environmental perception across extreme illumination scenarios, from high‐glare daylight to low‐light nights. Furthermore, we integrated this sensor into a full functional system that unifies image perception, non‐volatile storage, and in‐sensor processing. When deployed for traffic scenario analysis, a convolutional neural network trained on features extracted by the sensor achieved 96% classification accuracy, with performance scaling proportionally to the system's noise suppression capability. Our work establishes a practical strategy for high‐contrast bidirectional photonic synapses and highlights the potential of biomimetic systems in neuromorphic vision — particularly for autonomous driving and intelligent surveillance, where reliable operation across diverse spectral environments is critical.