驾驶员分心的力量：可解释的车载触摸屏交互视觉需求预测
On the Forces of Driver Distraction: Explainable Predictions for the Visual Demand of In-Vehicle Touchscreen Interactions
摘要：
随着现代信息娱乐系统的发展，驾驶员越来越倾向于在驾驶时从事次要任务。由于分心驾驶已经是致命事故的主要原因之一，车载触摸屏人机界面（HMI）必须尽可能少地分散注意力。为了确保这些系统使用安全，它们需要经过精心设计和昂贵的经验测试，需要功能齐全的原型。因此，早期阶段的方法通知设计师的影响，他们的设计可能对司机分心是非常有价值的。本文提出了一种机器学习方法，基于预期的使用场景，预测的视觉需求的车载触摸屏交互，并提供本地和全局的解释的因素影响驾驶员的视觉注意力分配。该方法基于从生产线车辆连续收集的大规模自然驾驶数据，并采用SHapley加法解释（SHAP）方法来提供利用明智的设计决策的解释。我们的方法比相关工作更准确，并确定了68%的准确率发生长时间扫视的相互作用，并预测了总的扫视时间，平均误差为2.4秒。我们的解释复制了最近各种研究的结果，并提供了关于UI元素，驾驶自动化和车辆速度对驾驶员分心的影响的快速和易于访问的见解。该系统不仅可以帮助设计师评估当前的设计，还可以帮助他们更好地预测和理解他们的设计决策可能对未来设计的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
