• Designing DNNs for a trade-off between robustness and processing performance in embedded devices 

  Gutiérrez Zaballa, Jon; Basterrechea Oyarzabal, Koldobika; Echanove Arias, Francisco Javier (IEEE, 2024-12-03)

  Machine learning-based embedded systems employed in safety-critical applications such as aerospace and autonomous driving need to be robust against perturbations produced by soft errors. Soft errors are an increasing concern ...
• Rapid Deployment of Domain-specific Hyperspectral Image Processors with Application to Autonomous Driving 

  Gutiérrez Zaballa, Jon; Basterrechea Oyarzabal, Koldobika; Echanove Arias, Francisco Javier; Mata Carballeira, Oscar; Martínez González, María Victoria (IEEE, 2024-01-10)

  The article discusses the use of low cost System-On-Module (SOM) platforms for the implementation of efficient hyperspectral imaging (HSI) processors for application in autonomous driving. The work addresses the challenges ...
• HSI-Drive v2. 0: More Data for New Challenges in Scene Understanding for Autonomous Driving 

  Gutiérrez Zaballa, Jon; Basterrechea Oyarzabal, Koldobika; Echanove Arias, Francisco Javier; Martínez González, María Victoria; Martínez Corral, Unai (IEEE, 2024-01-01)

  We present the updated version of the HSI-Drive dataset aimed at developing automated driving systems (ADS) using hyperspectral imaging (HSI). The v2.0 version includes new annotated images from videos recorded during ...
• Exploring fully convolutional networks for the segmentation of hyperspectral imaging applied to advanced driver assistance systems 

  Gutiérrez Zaballa, Jon; Basterrechea Oyarzabal, Koldobika; Echanove Arias, Francisco Javier; Martínez González, María Victoria; Del Campo Hagelstrom, Inés Juliana (Springer, 2022-07-30)

  Advanced Driver Assistance Systems (ADAS) are designed with the main purpose of increasing the safety and comfort of vehicle occupants. Most of current computer vision-based ADAS perform detection and tracking tasks quite ...

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