The classification of distracted drivers is pivotal for ensuring safe driving. Previous studies demonstrated the effectiveness of neural networks in automatically predicting driver distraction, fatigue, and potential hazards. However, recent research has uncovered a significant loss of accuracy in these models when applied to samples acquired under conditions that differ from the training data. In this paper, we introduce a robust model designed to withstand changes in camera position within the vehicle. Our Driver Behavior Monitoring Network (DBMNet) relies on a lightweight backbone and integrates a disentanglement module to discard camera view information from features, coupled with contrastive learning to enhance the encoding of various driver actions. Experiments conducted using a leave-one-camera-out protocol on the daytime and nighttime subsets of the 100-Driver dataset validate the effectiveness of our approach. Cross-dataset and cross-camera experiments conducted on three benchmark datasets, namely AUCDD-V1, EZZ2021 and SFD, demonstrate the superior generalization capabilities of the proposed method. Overall DBMNet achieves an improvement of 7% in Top-1 accuracy compared to existing efficient approaches. Moreover, a quantized version of the DBMNet and all considered methods has been deployed on a Coral Dev Board board. In this deployment scenario, DBMNet outperforms alternatives, achieving the lowest average error while maintaining a compact model size, low memory footprint, fast inference time, and minimal power consumption.

翻译：分心驾驶行为分类对保障行车安全至关重要。既往研究表明，神经网络在自动预测驾驶员分心、疲劳及潜在危险方面具有显著效果。然而，最新研究发现，当模型应用于与训练数据获取条件不同的样本时，存在显著精度损失。本文提出一种能够适应车内摄像头位置变化的鲁棒模型。我们设计的驾驶员行为监控网络（DBMNet）采用轻量级骨干网络，集成解耦模块以剔除特征中的摄像头视角信息，并联合对比学习增强对各类驾驶动作的编码能力。在100-Driver数据集白天与夜间子集上采用留一摄像头协议的实验验证了本方法的有效性。在AUCDD-V1、EZZ2021和SFD三个基准数据集上进行的跨数据集与跨摄像头实验表明，所提方法具有优异的泛化能力。与现有高效方法相比，DBMNet在Top-1准确率上实现7%的提升。此外，我们已将DBMNet及所有对比方法的量化版本部署至Coral Dev Board开发板。在该部署场景中，DBMNet在保持紧凑模型尺寸、低内存占用、快速推理速度与最小功耗的同时，实现了最低平均误差，超越其他备选方案。