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

Koldo Basterretxea, Jon Gutiérrez-Zaballa, Javier Echanobe

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.

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

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.

Paper Structure

This paper contains 9 sections, 3 figures, 6 tables.

Table of Contents

  1. Introduction
  2. HSI SNAPSHOT camera technologies
  3. spectral information: data quality, consistency, and processing constraints
  4. New data labeling
  5. Reflectance correction and data normalization
  6. enhanced segmentation models and experimental results
  7. U-Net with spectral attention modules
  8. Experimental results
  9. concluding remarks

Figures (3)

  • Figure 1: Example of manually labelled ground true images in HSI-Drive versions v2.0 and v2.1
  • Figure 2: figure
  • Figure 3: Example of the identification of a maximum albedo pixel for the white balance scaling. This image corresponds to a cloudy Autumn morning recording with low lightning. Although the maximum irradiance values are generated by the rear and front lights of the cars (a), the algorithm successfully rejects those pixels and selects a pixel corresponding to the road mark as the highest reflectance pixel in the image (b)