Supplementary data for the paper 'Using mobile devices for driving test assessment: A study of acceleration and GPS data'

There is a need to improve the validity of the driving test as a measure of an individual’s ability to drive safely. This paper explores the use of algorithms to analyze acceleration and GPS data from a smartphone and a GoPro to distinguish between different driving styles, as performed by experienced examiners portraying stereotypical driving test candidates. Measures from nine driving tests were analyzed, including (harsh) accelerations, jerk, mean speed, and speeding. Results showed that the type of car, instructed driving style, and driving route impacted the dependent measures. It is concluded that GPS and accelerometer data can effectively distinguish between cautious, normal, and aggressive driving. However, it is important to consider additional sensors, such as cameras, to allow for more context-aware assessments of driving behavior. Furthermore, we demonstrate methods to quantify variations in road conditions and provide suggestions for presenting the data to driving examiners.

目前亟需提升驾驶考试作为衡量个人安全驾驶能力指标的有效性。本文探讨了利用算法分析智能手机与GoPro设备采集的加速度及GPS数据，以区分不同驾驶风格——这些风格由经验丰富的考官模拟典型驾驶考生的行为呈现。研究分析了九次驾驶测试中的多项指标，包括（急）加速、加加速度、平均车速及超速情况。结果表明，车辆类型、指定驾驶风格及行驶路线均对上述依赖指标产生影响。研究结论指出，GPS与加速度计数据可有效区分谨慎、正常及激进三种驾驶风格。然而，需考虑引入摄像头等额外传感器，以实现对驾驶行为更具情境感知的评估。此外，本文还展示了量化道路条件变化的方法，并为向驾驶考官呈现数据提供了建议。