Deep neural networks enable real-time monitoring of in-vehicle driver, facilitating the timely prediction of distractions, fatigue, and potential hazards. This technology is now integral to intelligent transportation systems. Recent research has exposed unreliable cross-dataset end-to-end driver behavior recognition due to overfitting, often referred to as ``shortcut learning", resulting from limited data samples. In this paper, we introduce the Score-Softmax classifier, which addresses this issue by enhancing inter-class independence and Intra-class uncertainty. Motivated by human rating patterns, we designed a two-dimensional supervisory matrix based on marginal Gaussian distributions to train the classifier. Gaussian distributions help amplify intra-class uncertainty while ensuring the Score-Softmax classifier learns accurate knowledge. Furthermore, leveraging the summation of independent Gaussian distributed random variables, we introduced a multi-channel information fusion method. This strategy effectively resolves the multi-information fusion challenge for the Score-Softmax classifier. Concurrently, we substantiate the necessity of transfer learning and multi-dataset combination. We conducted cross-dataset experiments using the SFD, AUCDD-V1, and 100-Driver datasets, demonstrating that Score-Softmax improves cross-dataset performance without modifying the model architecture. This provides a new approach for enhancing neural network generalization. Additionally, our information fusion approach outperforms traditional methods.

翻译：深度神经网络实现了对车内驾驶员的实时监控，有助于及时预测分心、疲劳及潜在危险。该技术已成为智能交通系统的核心组成部分。近期研究表明，由于数据样本有限导致的过拟合（常称为"捷径学习"），跨数据集端到端驾驶员行为识别存在不可靠性。本文提出得分-Softmax分类器，通过增强类间独立性与类内不确定性来解决该问题。受人类评分模式的启发，我们基于边缘高斯分布设计了一个二维监督矩阵来训练分类器。高斯分布在增强类内不确定性的同时，确保得分-Softmax分类器能够学习到准确的知识。此外，利用独立高斯分布随机变量之和的特性，我们引入了一种多通道信息融合方法。该策略有效解决了得分-Softmax分类器的多信息融合难题。同时，我们论证了迁移学习与多数据集组合的必要性。利用SFD、AUCDD-V1和100-Driver数据集进行的跨数据集实验表明，得分-Softmax无需修改模型架构即可提升跨数据集性能，为增强神经网络泛化能力提供了新思路。此外，我们的信息融合方法优于传统方法。