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

arXiv: https://arxiv.org/abs/2508.19905 · HTML: https://mlsanity.com/paper/2508.19905/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 126 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 23 of 23)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [High-Resolution Representations for Labeling Pixels and Regions](https://mlsanity.com/paper/1904.04514/) (2019)
- [Snapshot Multispectral Imaging Using a Diffractive Optical Network](https://mlsanity.com/paper/2212.05217/) (2022)
- [HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios](https://mlsanity.com/paper/2409.11205/) (2024)
- [HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for Autonomous Driving](https://mlsanity.com/paper/2411.17530/) (2024)
- [Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image](https://mlsanity.com/paper/2012.10122/) (2020)
- [Hyperspectral Imaging-Based Perception in Autonomous Driving Scenarios: Benchmarking Baseline Semantic Segmentation Models](https://mlsanity.com/paper/2410.22101/) (2024)
- [Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective](https://mlsanity.com/paper/2412.03630/) (2024)
- [Hyperspectral City V1.0 Dataset and Benchmark](https://mlsanity.com/paper/1907.10270/) (2019)
- [On-chip Hyperspectral Image Segmentation with Fully Convolutional Networks for Scene Understanding in Autonomous Driving](https://mlsanity.com/paper/2411.19274/) (2024)
- [CSNR and JMIM Based Spectral Band Selection for Reducing Metamerism in Urban Driving](https://mlsanity.com/paper/2508.10962/) (2025)
- [Hyperspectral vs. RGB for Pedestrian Segmentation in Urban Driving Scenes: A Comparative Study](https://mlsanity.com/paper/2508.11301/) (2025)
- [Designing DNNs for a trade-off between robustness and processing performance in embedded devices](https://mlsanity.com/paper/2412.03682/) (2024)
- [Multi-Scale Spectral Attention Module-based Hyperspectral Segmentation in Autonomous Driving Scenarios](https://mlsanity.com/paper/2506.18682/) (2025)
- [SpectralZoom: Efficient Segmentation with an Adaptive Hyperspectral Camera](https://mlsanity.com/paper/2406.04287/) (2024)
- [Exploring Fully Convolutional Networks for the Segmentation of Hyperspectral Imaging Applied to Advanced Driver Assistance Systems](https://mlsanity.com/paper/2412.03982/) (2024)
- [Optimization of DNN-based HSI Segmentation FPGA-based SoC for ADS: A Practical Approach](https://mlsanity.com/paper/2507.16556/) (2025)

## Built on by (most cited first, 6 of 6)

- [Learnable Quantum Efficiency Filters for Urban Hyperspectral Segmentation](https://mlsanity.com/paper/2603.26528/) (2026)
- [RPT-SR: Regional Prior attention Transformer for infrared image Super-Resolution](https://mlsanity.com/paper/2602.15490/) (2026)
- [Cosine-Normalized Attention for Hyperspectral Image Classification](https://mlsanity.com/paper/2604.01763/) (2026)
- [Beyond Bayer: Task-Optimal Sensor Co-Design for Robust Autonomous-Driving Segmentation](https://mlsanity.com/paper/2606.24096/) (2026)
- [HSI-Road Relabeled: Surface-Aware Road-Scene Segmentation](https://mlsanity.com/paper/2609.12151/) (2026)
- [Band-Selection Stability and Semantic Segmentation Performance: A Study on Hyperspectral City](https://mlsanity.com/paper/2609.31074/) (2026)

## Abstract (arXiv)

Hyperspectral imaging (HSI) offers a transformative sensing modality for Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD) applications, enabling material-level scene understanding through fine spectral resolution beyond the capabilities of traditional RGB imaging. 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 to this qualitative review, we analyze 216 commercially available HSI and multispectral 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 terms of scale, spectral consistency, the number of spectral channels, and environmental diversity, posing challenges for the development of perception algorithms and the adequate validation of HSI's true potential in ADAS/AD applications. This review paper establishes the current state of HSI in automotive contexts as of 2025 and outlines key research directions toward practical integration of spectral imaging in ADAS and autonomous systems.