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QualiNet: Acquiring Bird’s Eye View Qualitative Spatial Representation from 2D Images in Automated Vehicle Perception (Short Paper)

Author Nassim Belmecheri

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ACM Subject Classification
  • Computing methodologies → Artificial intelligence
  • Computing methodologies → Spatial and physical reasoning
  • Computing methodologies → Scene understanding
Keywords
  • Qualitative Spatial Representation
  • Deep Learning
  • Computer vision
  • Qualitative Scene Understanding
  • Spatio-temporal representation and reasoning models (including moving objects tracking)

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Abstract

We present QualiNet, an end-to-end deep learning framework that acquires Bird’s Eye View (BEV) qualitative spatial relations directly from 2D images, eliminating the need for depth sensors. The system combines 2D object detection, masking, and classification to infer Rectangle Algebra (RA) and Qualitative Distance Calculus (QDC) relations. Evaluated on NuScenes and PandaSet datasets, QualiNet achieves 91% accuracy for RA, 80% for QDC, and 99% top-2 accuracy, demonstrating robust performance for automated vehicle perception.

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Nassim Belmecheri. QualiNet: Acquiring Bird’s Eye View Qualitative Spatial Representation from 2D Images in Automated Vehicle Perception (Short Paper). In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 14:1-14:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.TIME.2025.14

Author Details

Nassim Belmecheri
  • Simula Research Laboratory, Oslo, Norway

Funding

This work is funded by the European Commission through the AI4CCAM project (Trustworthy AI for Connected, Cooperative Automated Mobility) under grant agreement No 101076911.

Acknowledgements

I would like to thank my colleagues Arnaud Gotlieb, Nadjib Lazaar and Helge Spieker for the fruitful discussions and continuous support.

Supplementary Materials

References

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    Software Heritage Logo archived version
    full metadata available at: https://doi.org/10.4230/artifacts.24755
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