With modern infotainment systems, drivers are increasingly tempted to engage
in secondary tasks while driving. Since distracted driving is already one of
the main causes of fatal accidents, in-vehicle touchscreen Human-Machine
Interfaces (HMIs) must be as little distracting as possible. To ensure that
these systems are safe to use, they undergo elaborate and expensive empirical
testing, requiring fully functional prototypes. Thus, early-stage methods
informing designers about the implication their design may have on driver
distraction are of great value. This paper presents a machine learning method
that, based on anticipated usage scenarios, predicts the visual demand of
in-vehicle touchscreen interactions and provides local and global explanations
of the factors influencing drivers' visual attention allocation. The approach
is based on large-scale natural driving data continuously collected from
production line vehicles and employs the SHapley Additive exPlanation (SHAP)
method to provide explanations leveraging informed design decisions. Our
approach is more accurate than related work and identifies interactions during
which long glances occur with 68 % accuracy and predicts the total glance
duration with a mean error of 2.4 s. Our explanations replicate the results of
various recent studies and provide fast and easily accessible insights into the
effect of UI elements, driving automation, and vehicle speed on driver
distraction. The system can not only help designers to evaluate current designs
but also help them to better anticipate and understand the implications their
design decisions might have on future designs.

本文利用大量自然驾驶数据和机器学习方法，预测车内触摸屏界面的可视需求和驾驶员视觉关注分配的影响因素，并提供局部和全局的解释说明，以帮助设计师评估当前设计并更好地预测和理解其对未来设计所可能产生的影响。