The usage of a smartphone while driving is a pervasive problem and has been acknowledged as a significant source of road accidents and crashes. Several solutions have been developed to control and minimize risky driving behavior. However, these solutions were mainly designed from the perspective of normal users to be used in a nondriving scenario. In a driving scenario, any deviation from these assumptions (e.g., touching or taping interfaces and looking to visual items) could impact driving performance. In this research paper, we aimed to design and develop a context-aware adaptive user interface framework to minimize driver distraction. The proposed framework is implemented in Android platform, namely, “DriverSense,” which is capable of adapting smartphone user interfaces based on contextual factors including driver preferences, environmental factors, and device usage in real time using adaptation rules. The proposed solution is evaluated both in real time using AutoLog application and through an empirical study by collecting data from 93 drivers through a mixed-mode survey using a questionnaire. Results obtained from AutoLog dataset show that performing activities on smartphone native interfaces while driving leads to abrupt changes in speed and steering wheel angle. However, minimal variations have been observed while performing activities on DriverSense interfaces. The results obtained from the empirical study show that the data are found to be internally consistent with 0.7 Cronbach’s alpha value. Furthermore, an Iterated Principal Factor Analysis (IPFA) retained 60 of a total of 61 measurement items with lower uniqueness values. The findings show that the proposed solution has significantly minimized the driver distractions and has positive perceptions in terms of usefulness, attitude, learnability and understandability, and user satisfaction.

中文翻译：

---

旨在设计上下文相关的自适应用户界面，以最大程度地减少驾驶员的分心

驾车时使用智能手机是一个普遍的问题，已被认为是造成道路交通事故和交通事故的重要原因。已经开发了几种解决方案来控制和最小化危险的驾驶行为。但是，这些解决方案主要是从普通用户的角度设计的，用于非驾驶场景。在驾驶场景中，与这些假设的任何偏离（例如，触摸或敲击界面以及查看视觉项目）都可能影响驾驶性能。在这篇研究论文中，我们旨在设计和开发一种上下文感知的自适应用户界面框架，以最大程度地减少驾驶员的分心。提议的框架是在Android平台（即“ DriverSense”）中实现的，该平台能够根据上下文因素（包括驾驶员偏好，环境因素，以及使用适应规则实时地使用设备。使用AutoLog应用程序对提出的解决方案进行实时评估，并通过实证研究，通过使用问卷的混合模式调查从93名驾驶员中收集数据来进行评估。从AutoLog数据集获得的结果表明，在驾驶时在智能手机本机界面上执行活动会导致速度和方向盘角度的突然变化。但是，在DriverSense界面上执行活动时，观察到的变化很小。从实证研究中获得的结果表明，该数据在内部与0.7 Cronbach的alpha值一致。此外，迭代主因子分析（IPFA）保留了61个唯一性较低的测量项目中的60个。