Hyperspectral Sensors and Autonomous Driving: Technologies, Limitations, and Opportunities

Abstract

Hyperspectral imaging (HSI) is a transformative sensing modality for Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD). By capturing fine spectral resolution across hundreds of bands, HSI enables material-level scene understanding that overcomes critical limitations of traditional RGB imaging in adverse weather and lighting. This paper presents the first comprehensive review of HSI for automotive applications, examining the strengths, limitations, and suitability of current HSI technologies in the context of ADAS/AD. In addition, we analyze 216 commercially available spectral imaging cameras, benchmarking them against key automotive criteria: frame rate, spatial resolution, spectral dimensionality, and compliance with AEC-Q100 temperature standards. Our analysis reveals a significant gap between HSI’s demonstrated research potential and its commercial readiness. Only four cameras meet the defined performance thresholds, and none comply with AEC-Q100 requirements. In addition, the paper reviews recent HSI datasets and applications, including semantic segmentation for road surface classification, pedestrian separability, and adverse weather perception. Our review shows that current HSI datasets are limited in scale, spectral consistency, channel count, and environmental diversity, posing a challenge for perception algorithms development and adequate HSI’s potential validation in ADAS/AD applications. This review paper presents the current state of HSI in automotive contexts and outlines key research directions toward practical integration of spectral imaging in ADAS and autonomous systems.