Authors: Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker

Published on: January 29, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2403.09668

Summary

• What is new: The introduction of the Qualitative Explainable Graph (QXG) for urban mobility scene understanding, which combines symbolic and qualitative data representation.
• Why this is important: The challenge of interpreting complex urban scenes in real-time for automated vehicles using existing sensor data.
• What the research proposes: QXG leverages spatio-temporal graphs and qualitative constraints to synthesize scene semantics from raw sensor inputs, enabling real-time, intelligible scene models for automated vehicles.
• Results: QXG demonstrates the ability to incrementally construct detailed and explainable scene understandings in real-time, aiding in decision-making and providing clear explanations for vehicle actions.

Technical Details

Technological frameworks used: Spatio-temporal graph analysis, Qualitative constraints representation

Models used: Machine learning models for sensor data interpretation

Data used: LiDAR, camera data

Potential Impact

Automotive industry, specifically companies involved in automated driving technologies, and urban mobility service providers.

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