Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912575310004224 |
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| author | Rezaei, Mahdi Azarmi, Mohsen |
| author_facet | Rezaei, Mahdi Azarmi, Mohsen |
| contents | 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 triple-camera 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, non-intrusive 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_04139 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles Rezaei, Mahdi Azarmi, Mohsen Computer Vision and Pattern Recognition Artificial Intelligence Emerging Technologies Machine Learning Robotics I.4.9 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 triple-camera 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, non-intrusive 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. |
| title | Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Emerging Technologies Machine Learning Robotics I.4.9 |
| url | https://arxiv.org/abs/2507.04139 |