基于多视图协同交互技术的换道图谱构建与分类

龙彦; 黄建玲; 赵晓华; 李振龙

doi:10.3963/j.jssn.1674-4861.2022.01.013

基于多视图协同交互技术的换道图谱构建与分类

doi: 10.3963/j.jssn.1674-4861.2022.01.013

龙彦^1,,
黄建玲^{1, 2},
赵晓华^1, ,,
李振龙¹

1.
北京工业大学城市交通学院北京 100124
2.
北京市交通信息中心北京 100073

基金项目:

国家自然科学基金项目 61876011

详细信息

作者简介:
龙彦（1979—），博士研究生. 研究方向：驾驶行为及智能交通.E-mail: longyan@emails.bjut.edu.cn

通讯作者:
赵晓华（1971—），博士，教授. 研究方向：驾驶行为与交通安全. E-mail: zhaoxiaohua@bjut.edu.cn

中图分类号: U491.54
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- 文章访问数: 904
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- 被引次数: 0
出版历程
- 收稿日期: 2021-09-20
- 网络出版日期: 2022-03-31

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

1.
College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
2.
Beijing Transportation Information Center, Beijing 100073, China

摘要

摘要:
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/s²；0.41 m/s²）都大于合格图谱的平均标准差（4.55°；0.34 m/s²；0.17 m/s²）。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。
- 智能交通 /
- 换道过程 /
- 换道图谱 /
- 多视图协同
Abstract:
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/s²;0.41 m/s²) are larger than those observed over the qualified processes (4.55°; 0.34 m/s²;0.17 m/s²). 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.
- intelligent transportation /
- lane-changing process /
- lane-changing graph /
- multi-view collaborative visualization

HTML全文

图 1 模拟器与仿真场景

Figure 1. The simulator and scenarios

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图 2 模式组合图

Figure 2. Pattern combination diagram

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图 3 换道图谱构架

Figure 3. The framework of lane changing

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图 4 直角坐标与平行坐标

Figure 4. Cartesian coordinates and parallel coordinates

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图 5 平行坐标

Figure 5. Parallel coordinates

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图 6 换道图谱

Figure 6. Lane changing graph

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图 7 换道图谱合格区

Figure 7. The qualified area of the lane change graph

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图 8 图谱示例

Figure 8. The examples of the lane change graph

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图 9 多视图交互

Figure 9. Multi-view interaction

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图 10 不同类型图谱

Figure 10. Different types of graphs

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表 1 转向盘操作的分类

Table 1. Classification of steering wheel operation

类别	转向盘旋转速度ω的分类
急速左转	98%分位数≤ω
缓慢左转	75%分位数≤ω < 98%分位数
保持不动	25%分位数≤ω < 75%分位数
缓慢右转	2%分位数≤ω < 25%分位数
急速右转	ω < 2%分位数

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表 2 3类图谱的指标标准差

Table 2. The SD of the indexes of three types of graphs

指标	合格图谱	临界合格图谱	不合格图谱
转向盘转速标准差/(°)	4.55	5.27	6.57
油门踏板标准差/%	12.48	11.94	20.12
速度标准差/(km/h)	5.74	3.66	6.80
横向位置标准差/m	1.40	1.63	1.60
加速度标准差/(m/s²)	0.34	0.29	0.91
横向加速度标准差/(m/s²)	0.17	0.25	0.41
刹车踏板标准差/%	0.00	0.00	2.45

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表 3 导致图谱不合格的异常指标数

Table 3. The number of abnormal indexes resulting in the unqualified

异常指标数	不合格图谱的数量	占所有不合格图谱的比例/%
2	4	12.90
3	9	29.03
4	10	32.26
5	3	9.68
6	3	9.68
7	1	3.23
8	1	3.23
9	0	0.00
10	0	0.00

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表 4 临界合格和不合格图谱中异常指标的频繁项

Table 4. Frequent items of abnormal indexes in critical qualified and unqualified graphs

	指标	影响频率/%
频繁1项集	{转向盘角度}	63.89
	{手的急速操作}	63.89
	{横向加速度}	58.33
	{脚的急速操作}	55.56
	{加速度}	36.11
频繁2项集	{转向盘角度，手的急速操作}	55.56
	{转向盘角度，横向加速度}	44.44
	{横向加速度，手的急速操作}	41.67
	{手的急速操作、脚的急速操作}	36.11
	{脚的急速操作，转向盘角度}	33.33
频繁3项集	{转向盘角度，手的急速操作，横向加速度}	38.89
频繁3项集	{转向盘角度，手的急速操作，脚的急速操作}	30.56

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