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.

当前亟需优化驾驶考试作为个体安全驾驶能力衡量标尺的有效性。本文探讨了利用算法分析智能手机与GoPro采集的加速度数据及GPS数据，以区分不同驾驶风格的研究路径，实验由经验丰富的考官模拟典型驾考考生的驾驶行为开展。本研究对九次驾驶测试的多项指标进行了分析，涵盖（剧烈）加速度、加加速度(jerk)、平均车速以及超速情况。研究结果表明，车辆类型、指定驾驶风格与行驶路线均会对因变量测量结果产生影响。研究结论指出，GPS与加速度计(accelerometer)数据可有效区分谨慎型、普通型与激进型三类驾驶风格。不过，仍需考虑纳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，本文还演示了量化道路状况差异的方法，并为向驾驶考官呈现相关数据提供了建议。