Autonomous vehicles depend entirely on Simultaneous Localization and Mapping (SLAM) to navigate safely in unknown environments. However, relying on a single sensory modality introduces critical failure points: LiDAR systems degrade in featureless corridors, Inertial Measurement Units (IMUs) accumulate mathematical drif...
Amr O. Elmehrath, Farah Khaled, Rana Nahas et al.· 0 citations
An automated pipeline is presented that transforms safe driving scenes into safety-critical scenarios by combining computer vision, Large Language Models (LLMs), and Augmented Reality (AR) to support the testing and validation of autonomous driving systems.
Noura Fady, Farah Khaled, Catherine M. Elias· 0 citations
NIKG occupies the space between scene-oriented traffic KGs and intention-prediction models: a small, explicit semantic layer intended to support downstream work such as knowledge graph embedding, SPARQL-based feature extraction, or hybrid neuro-symbolic reasoning.
Khaled Nowara, Mohamed A. Manzour, Catherine M. Elias· SKILLED-LLMs@FLoC· 0 citations
The INTENT framework is proposed using LSTM model to predict the vehicle's intention at intersections 2 seconds ahead of the event occurrence to predict whether the cars in intersections are going straight, turning left, or turning right.
Logine M. Zaki, Catherine M. Elias· arXiv.org· 0 citations
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