Rezaei, M. orcid.org/0000-0003-3892-421X and Azarmi, M. (Accepted: 2025) Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles. In: IEEE Intelligent Vehicles Symposium Proceedings. 36th IEEE Intelligent Vehicles Symposium 2025, 22-25 Jun 2025, Napoca, Romania. IEEE (In Press)
Abstract
Ensuring safe transition of control in automated vehicles requires an accurate and timely assessment of driver readiness. This paper introduces Driver-Net, a novel deep learning framework that fuses multi-camera inputs to estimate driver take-over readiness. Unlike conventional vision-based driver monitoring systems that focus on head pose or eye gaze, Driver-Net captures synchronised visual cues from the driver’s head, hands, and body posture through a triplecamera setup. The model integrates spatio-temporal data using a dual-path architecture, comprising a Context Block and a Feature Block, followed by a cross-modal fusion strategy to enhance prediction accuracy. Evaluated on a diverse dataset collected from the University of Leeds Driving Simulator, the proposed method achieves an accuracy of up to 95.8% in driver readiness classification. This performance significantly enhances existing approaches and highlights the importance of multimodal and multi-view fusion. As a real-time, nonintrusive solution, Driver-Net contributes meaningfully to the development of safer and more reliable automated vehicles and aligns with new regulatory mandates and upcoming safety standards.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of a conference paper accepted for publication in IEEE Symposium on Intelligent Vehicle Proceedings made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) |
Funding Information: | Funder Grant number EU - European Union 101006664 |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Jul 2025 13:44 |
Last Modified: | 29 Jul 2025 13:44 |
Status: | In Press |
Publisher: | IEEE |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:229641 |