用三路摄像头融合头手身姿态，实时判断自动驾驶司机接管意愿。
Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles
- 三路摄像头同步捕捉头、手、身体姿态，多视角融合输入。
- 在利兹大学模拟器数据集上达到95.8%的接管就绪分类准确率。
- 适合关注自动驾驶安全交接的工程师与标准制定者。
确保自动驾驶车辆控制权安全转移，需精准及时评估驾驶员准备状态。本文提出Driver-Net，一种新型深度学习框架，通过多摄像头输入融合来估计驾驶员接管就绪程度。与传统基于头部姿态或眼球注视的驾驶监控系统不同，Driver-Net利用三路摄像头同步获取驾驶员头部、双手及身体姿态的视觉线索。模型采用双路径架构，包含上下文模块（Context Block）和特征模块（Feature Block），并结合跨模态融合策略以提升预测精度。在利兹大学驾驶模拟器收集的多样化数据集上评估，该方法在驾驶员就绪状态分类任务中最高达95.8%的准确率。这一性能显著优于现有方法，凸显了多模态与多视角融合的重要性。作为实时、非侵入式解决方案，Driver-Net对构建更安全可靠的自动驾驶系统具有重要意义，并契合新的监管要求与未来安全标准。
原文摘要 · Abstract (English)
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.
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