# Challenges in Hyperspectral Imaging for Autonomous Driving: The HSI-Drive Case

Source: [Challenges in Hyperspectral Imaging for Autonomous Driving: The HSI-Drive Case](<https://www.sota2.com/research/paper/69c60f370636e5bf4afc86be>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-28T05:00:12.409Z

Authors: Koldo Basterretxea, Jon Guti\\'errez-Zaballa, Javier Echanobe

Published: 2026-03-26T14:45:57.000Z

## Abstract

The use of hyperspectral imaging (HSI) in autonomous driving (AD), while promising, faces many challenges related to the specifics and requirements of this application domain. On the one hand, non-controlled and variable lighting conditions, the wide depth-of-field ranges, and dynamic scenes with fast-moving objects. On the other hand, the requirements for real-time operation and the limited computational resources of embedded platforms. The combination of these factors determines both the criteria for selecting appropriate HSI technologies and the development of custom vision algorithms that leverage the spectral and spatial information obtained from the sensors. In this article, we analyse several techniques explored in the research of HSI-based vision systems with application to AD, using as an example results obtained from experiments using data from the most recent version of the HSI-Drive dataset.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on HSI-Drive 6-class experiment: pedestrians v2.0 v2.1](<https://www.sota2.com/research/sota/semantic-segmentation-on-hsi-drive-6-class-experiment-pedestrians-v2-0-v2-1.md>): Semantic segmentation; HSI-Drive 6-class experiment: pedestrians v2.0 v2.1; Road Accuracy: 97.6.

- [Semantic Segmentation on HSI-Drive 6-class painted metal v2.0, v2.1 (5-fold cross-validation)](<https://www.sota2.com/research/sota/semantic-segmentation-on-hsi-drive-6-class-painted-metal-v2-0-v2-1-5-fold-cross-validation.md>): Semantic segmentation; HSI-Drive 6-class painted metal v2.0, v2.1 (5-fold cross-validation); Road Accuracy: 98.08.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.25510>)

[PDF](<https://arxiv.org/pdf/2603.25510.pdf>)