Transportation Systems Engineering, Department of Civil Engineering, Indian Institute of Technology Hyderabad, Kandi, Medak 502285, India.
Department of Electronics and Communication, LNM Institute of Information Technology, Jaipur, India.
Accid Anal Prev. 2021 Dec;163:106464. doi: 10.1016/j.aap.2021.106464. Epub 2021 Oct 30.
Assessing the individual's driving profile and identifying the at-fault behaviors contributes to road safety, riding comfort, and driver assistance systems. This study proposes a framework to identify aggressive driving patterns in longitudinal control using real-time driving profiles of heavy passenger vehicle (HPV) drivers. The main objective is to detect and quantify the instantaneous driving decisions and classify the identified maneuvers (acceleration, braking) using unsupervised machine learning techniques without any prior-ground truth. To this end, total 8295 acceleration events, and 7151 braking events, were extracted from 142 driving profiles collected using high-resolution (10 Hz) GPS instrumentation. The principal component analysis was conducted on a multi-dimensional feature set, followed by a two-stage k-means clustering on the reduced feature subspace. The results showed that 86.5% of accelerations and 65.3% of braking maneuvers were characterized as non-aggressive, indicating safe or base-line driving behavior. However, 13.5% of accelerations and 34.7% of braking maneuvers were featured to be aggressive, indicative of the actual risky behaviors. Further analysis demonstrated the heterogeneity in drivers' trip-level frequency of aggressive maneuvers and highlighted the need for a continuous driving assessment. The study also revealed that the thresholds derived from the obtained clusters featuring the aggressive accelerations (+0.3 to +0.48 g) and aggressive braking (-0.42 to -0.27 g) maneuvers were beyond the acceptable limits of passenger safety and comfort. The insights from the study aids in developing driver assistance systems for personalized feedback provision and improve driver behavior.
评估个体的驾驶行为特征，识别事故责任行为，有助于提高道路安全水平、提升驾乘舒适性，并为驾驶员辅助系统提供支持。本研究提出了一种基于重型客车（HPV）驾驶员实时驾驶行为特征，识别纵向控制中激进驾驶模式的框架。主要目标是利用无监督机器学习技术检测和量化瞬时驾驶决策，并对识别出的驾驶行为（加速、减速）进行分类，无需任何先验的真实数据。为此，从 142 名驾驶员的驾驶行为中提取了总计 8295 个加速事件和 7151 个减速事件，这些事件是通过使用高分辨率（10Hz）GPS 仪器收集的。对多维特征集进行主成分分析，然后在降维特征子空间上进行两级 k-均值聚类。结果表明，86.5%的加速行为和 65.3%的减速行为是非激进的，表明为安全或基线驾驶行为。然而，13.5%的加速行为和 34.7%的减速行为被认为是激进的，表明存在实际的危险行为。进一步的分析表明，驾驶员在行程级别上激进行为的频率存在差异，需要进行持续的驾驶评估。研究还表明，从具有激进加速（+0.3 至+0.48g）和激进减速（-0.42 至-0.27g）行为的聚类中得出的阈值超出了乘客安全和舒适的可接受范围。本研究的结果有助于开发驾驶员辅助系统，为驾驶员提供个性化的反馈，并改善驾驶员的行为。
