mlsanityData to 14 Oct 2026

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

arXiv 2508.19905 ↗First posted 27 Aug 2025cs.CV cs.ETcited by 6

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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.

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

23 of 126 references.

References · 23

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  3. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  4. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  5. 2016Pyramid Scene Parsing Networkcited by 2,479
  6. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  7. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 403
  8. 2019High-Resolution Representations for Labeling Pixels and Regionscited by 211
  9. 2022Snapshot Multispectral Imaging Using a Diffractive Optical Networkcited by 16
  10. 2024HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarioscited by 12
  11. 2024HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for Autonomous Drivingcited by 12
  12. 2020Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Imagecited by 8
11 more
  1. 2024Hyperspectral Imaging-Based Perception in Autonomous Driving Scenarios: Benchmarking Baseline Semantic Segmentation Modelscited by 8
  2. 2024Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspectivecited by 8
  3. 2019Hyperspectral City V1.0 Dataset and Benchmarkcited by 6
  4. 2024On-chip Hyperspectral Image Segmentation with Fully Convolutional Networks for Scene Understanding in Autonomous Drivingcited by 6
  5. 2025CSNR and JMIM Based Spectral Band Selection for Reducing Metamerism in Urban Drivingcited by 4
  6. 2025Hyperspectral vs. RGB for Pedestrian Segmentation in Urban Driving Scenes: A Comparative Studycited by 4
  7. 2024Designing DNNs for a trade-off between robustness and processing performance in embedded devicescited by 3
  8. 2025Multi-Scale Spectral Attention Module-based Hyperspectral Segmentation in Autonomous Driving Scenarioscited by 3
  9. 2024SpectralZoom: Efficient Segmentation with an Adaptive Hyperspectral Cameracited by 2
  10. 2024Exploring Fully Convolutional Networks for the Segmentation of Hyperspectral Imaging Applied to Advanced Driver Assistance Systemscited by 2
  11. 2025Optimization of DNN-based HSI Segmentation FPGA-based SoC for ADS: A Practical Approachcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026Learnable Quantum Efficiency Filters for Urban Hyperspectral Segmentationcited by 2
  2. 2026RPT-SR: Regional Prior attention Transformer for infrared image Super-Resolutioncited by 0
  3. 2026Cosine-Normalized Attention for Hyperspectral Image Classificationcited by 0
  4. 2026Beyond Bayer: Task-Optimal Sensor Co-Design for Robust Autonomous-Driving Segmentationcited by 0
  5. 2026HSI-Road Relabeled: Surface-Aware Road-Scene Segmentationcited by 0
  6. 2026Band-Selection Stability and Semantic Segmentation Performance: A Study on Hyperspectral Citycited by 0