基于多视图协同交互技术的换道图谱构建与分类

Development and Classification of Lane-changing Graph Based on Multi-view Collaborative and Interactive Techniques

为直观展示换道过程中驾驶人视觉感知与手脚操作的细节特征,研究了多视图协同可视化的换道图谱。采用驾驶模拟舱进行高速公路驾驶实验,提取换道过程相关指标数据。将平行坐标、计数图、柱状图与换道轨迹协同可视化以构建换道图谱。采用多视图交互技术对提取的40个换道过程进行分析,提出换道过程的合格区范围并以此将换道图谱分为合格、临界合格和不合格3类,并对不合格图谱进行致因分析。结果表明,合格、临界合格和不合格图谱的比例分别为10.00%、12.50%和77.50%。不合格图谱的转向盘转速、加速度、横向加速度的平均标准差(6.57°;0.91 m/s^(2);0.41 m/s^(2))都大于合格图谱的平均标准差(4.55°;0.34 m/s^(2);0.17 m/s^(2))。导致图谱不合格的主要因素是:驾驶人手的急速操作引起转向盘转动幅度过大、横向加速度过大;驾驶人脚的急速操作引起纵向加速度的变化幅度过大。换道图谱能够精准地对换道过程进行可视化分析与诊断,为驾驶人优化换道行为提供支撑。

This paper aims to intuitively display the details of drivers'visual perception and related driving behavior in the lane-changing process by developing a multi-view collaborative visualization-based lane-changing graph.Specifically,driving behavior data related to lane-changing process are extracted from a simulated expressway,which is carried out by a driving simulator.The lane-changing graph is developed by coordinating parallel coordinates,count diagram,and bar chart with lane-changing trajectory.Following the analysis of 40 data sets of lane-changing behavior using the multi-view technique and the criteria for qualified lane-changing area,the lane-changing behavior is then classified into“Qualified”“Barely Qualified”,and“Unqualified”.Meanwhile,the reasons of the unqualified lane-changing processes are also studied.The results show that the proportions of“Qualified”“Barely Qualified”,and“Unqualified”processes are 10.00%,12.50%,and 77.50%respectively.The average standard deviations of the turning speed of the steering wheel,acceleration,and lateral acceleration observed over the unqualified processes(6.57°;0.91 m/s^(2);0.41 m/s^(2))are larger than those observed over the qualified processes(4.55°;0.34 m/s^(2);0.17 m/s^(2)).The reasons for showing unqualified processes are mainly twofold:excessive lateral acceleration due to a large turning angle of the steering wheel and excessive change of longitudinal acceleration due to inappropriate operation of the gas panel.In general,the lane-changing graph can analyze and diagnose the lane-changing process accurately,which can provide supports for optimizing driver behavior in the lane-changing process.