Kumar, Amit and Assaf, Mansour and Groza, V.Z. and Petriu, E.M. (2022) Entangled Bimodal Vision in Vehicles for Decision During Risk Situation. [Conference Proceedings]
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On the foundation of a cost-effective embedded system, this work integrates the principles of entanglement and learned simulated flows in a vehicular vision for situational awareness that can be integrated with a vehicular cloud for decision making. The road scenes of bimodal vision are extracted, and the vehicles are detected a zoned based on location. The attributes of the vehicle together with the depth information are extracted and recognized. The system employs two cameras which are used for decision making. The decision-making attributes are weighted and entangled to optimize the decision process. Ten situations on the road are modelled using the finite element method which is learned and integrated with the entanglement decision making. The proposed method shows low RMSE in risk prediction in comparison to the monocular vision system and conventional fusion of multi-modal vision vehicular vision. The results show that the system is effective and promising for the modelled conditions.
| Item Type: | Conference Proceedings |
|---|---|
| Uncontrolled Keywords: | Entanglement, vehicular vision, bimodal, artificial intelligence, decision system |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Mansour Assaf |
| Date Deposited: | 27 Jul 2025 23:16 |
| Last Modified: | 27 Jul 2025 23:16 |
| URI: | https://repository.usp.ac.fj/id/eprint/13530 |
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