{"ID":2898186,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.04139","arxiv_id":"2507.04139","title":"Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles","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 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.","short_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 th...","url_abs":"https://arxiv.org/abs/2507.04139","url_pdf":"https://arxiv.org/pdf/2507.04139v2","authors":"[\"Mahdi Rezaei\",\"Mohsen Azarmi\"]","published":"2025-07-05T19:27:03Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.ET\",\"cs.LG\",\"cs.RO\"]","methods":"[]","has_code":false}