Objective

This study aimed to investigate the impacts of feature selection on driver cognitive distraction (CD) detection and validation in real-world nonautomated and Level 2 automated driving scenarios.

Background

Real-time driver state monitoring is critical to promote road user safety.

Method

Twenty-four participants were recruited to drive a Tesla Model S in manual and Autopilot modes on the highway while engaging in the N-back task. In each driving mode, CD was classified by the random forest algorithm built on three “hand-crafted” glance features (i.e., percent road center [PRC], the standard deviation of gaze pitch, and yaw angles), or through a large number of features that were transformed from the output of a driver monitoring system (DMS) and other sensing systems.

Results

In manual driving, the small set of glance features was as effective as the large set of machine-generated features in terms of classification accuracy. Whereas in Level 2 automated driving, both glance and vehicle features were less sensitive to CD. The glance features also revealed that the misclassified driver state was the result of the dynamic fluctuations and individual differences of cognitive loads under CD.

Conclusion

Glance metrics are critical for the detection and validation of CD in on-road driving.

Applications

The paper suggests the practical value of human factors domain knowledge in feature selection and ground truth validation for the development of driver monitoring technologies.

中文翻译：

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评估驾驶员特征以在手动和 2 级自动驾驶中进行认知分心检测和验证

客观的

本研究旨在调查特征选择对现实世界非自动驾驶和 2 级自动驾驶场景中驾驶员认知分心 (CD) 检测和验证的影响。

背景

实时驾驶员状态监控对于促进道路使用者安全至关重要。

方法

招募了 24 名参与者在高速公路上以手动和自动驾驶模式驾驶特斯拉 Model S，同时参与 N-back 任务。在每种驾驶模式下，CD 由基于三个“手工制作的”扫视特征（即道路中心百分比 [PRC]、注视俯仰标准差和偏航角）的随机森林算法或通过大量从驾驶员监控系统 (DMS) 和其他传感系统的输出转换而来的特征。

结果

在手动驾驶中，就分类精度而言，少量的一目了然特征与大量机器生成的特征一样有效。而在 2 级自动驾驶中，视线和车辆功能对 CD 的敏感度都较低。一目了然的特征还显示，错误分类的驾驶员状态是CD下认知负荷的动态波动和个体差异的结果。

结论

Glance 指标对于道路驾驶中 CD 的检测和验证至关重要。

应用

该论文提出了人为因素领域知识在特征选择和地面实况验证中对驾驶员监控技术发展的实用价值。