基于道路概率场的车队协同定位方法

汪仲琪; 陈伟; 杜路遥; 雷震; 雷霆

doi:10.3963/j.jssn.1674-4861.2023.04.009

基于道路概率场的车队协同定位方法

doi: 10.3963/j.jssn.1674-4861.2023.04.009

汪仲琪^1,,
陈伟¹,
杜路遥¹,
雷震^1, ,,
雷霆²

1.
武汉理工大学自动化学院武汉 430070
2.
美国堪萨斯大学劳伦斯 66045

基金项目:

国家自然科学基金项目 41971334

详细信息

作者简介:
汪仲琪（1997—），硕士研究生. 研究方向：车联网与地图匹配. E-mail: wzq19971203@whut.edu.cn

通讯作者:
雷震(1977—)，博士，副教授. 研究方向：数据融合与智能交通. E-mail: leizhen@whut.edu.cn

中图分类号: U666.11
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- 文章访问数: 304
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出版历程
- 收稿日期: 2023-02-10
- 网络出版日期: 2023-11-23

Cooperative Positioning of Vehicle Fleets Using Road Probability Field

WANG Zhongqi^1
,,
CHEN Wei¹,
DU Luyao¹,
LEI Zhen^{1
, ,},
LEI Ting²

1.
School of Automation, Wuhan University of Technology, Wuhan 430070, China
2.
University of Kansas, Lawrence 66045, USA

摘要

摘要: 标量场方法通常被用于无人机集群和潜艇集群的协同定位，但在车队场景中，应用磁异常场、水深场等类似的标量场存在困难。本研究提出了1种基于道路概率场和车辆运动模型的车队协同定位方法，该方法利用开源数据库获取电子地图，对地图进行缓冲区处理、栅格化处理和数学形态学处理，构建了道路概率场。同时，基于GNSS技术建立了车辆运动模型，利用车队内车辆之间的相对位置作为协同信息，把车辆预测位置在道路概率场中的取值作为权重计算标准，使用粒子滤波定位算法不断更新预测车辆行驶轨迹。新方法建立了道路概率场这一标量场，把车辆位置对应的道路概率值作为判定车辆位置的重要依据，将电子地图中的地理空间信息应用于车队协同定位。与传统的标量场方法不同，道路概率场无需新的专门测量，并且可以利用已有的海量电子地图资源进行生成，而车辆也无需新增传感器。此外，新方法根据不同场景设计了车辆运动模型，在车辆行驶过程中利用道路概率场不断优化轨迹，与传统车队定位方法相比更注重单个时刻点定位的差异。在真实场景和仿真场景中设置不同缓冲区宽度和车辆数进行对比测试，结果显示：根据定位误差作为定位效果的判定标准，与经典的利用车辆运动模型的扩展卡尔曼滤波方法相比，新方法在仿真场景和真实场景中将改进幅度分别提高了49.6%和49.8%；与空道路概率场的车队协同定位方法相比，在仿真场景和真实场景中新方法分别改进了59.5%和50.3%。本研究为车队协同定位提供了1种新的方法，通过构建道路概率场并利用车辆运动模型，相较于传统方法，该方法提高了定位的精度和可靠性，具有重要的应用前景。
- 智能交通 /
- 协同定位 /
- 定位性能 /
- 道路概率场 /
- 地理信息系统
Abstract: Scalar field methods are widely used in the coordinated positioning of groups of unmanned automatic vehicles (UAVs) and submarines. However, there are difficulties in applying similar scalar fields such as magnetic anomaly fields and water depth fields in vehicle fleet scenarios. To address this issue, a vehicle fleet collaborative positioning method based on road probability field and vehicle motion model is proposed an open-source database is used to obtain electronic maps, and the electronic maps were buffered, rasterized, and processed with mathematical morphology to construct road probability field. At the same time, a vehicle motion model is established based on GNSS technology, using the relative positions between vehicles in the fleet as collaborative information, taking the value of the predicted position of the vehicle in the road probability field as a weight calculation criterion, and using particle filtering localization algorithms to continually update the predicted trajectory of the vehicle. The new method establishes the road probability field as a scalar field, using the road probability value corresponding to the vehicle position as an important basis for determining the vehicle position, and applying the geographic spatial information contained in the electronic map to the collaborative positioning of the vehicle fleet. Unlike traditional scalar field methods, road probability fields do not require new specialized measurements and can be generated using the massive electronic map resources already available, and vehicles do not require new sensors. A vehicle motion model is designed based on the application scenario, and the trajectory is continuously optimized using the road probability field during the dynamic process of vehicle driving, reflecting the difference with traditional fleet positioning methods that pay more attention to single time point positioning. Comparative tests were conducted on different buffer widths and vehicle numbers in real and simulation experiments. The results show that using positioning error as the criterion for determining the positioning effect, compared to the classic extended Kalman filtering method using vehicle motion models, proposed method achieves improvements of 49.6% and 49.8% in simulated and real scenarios, respectively. Compared with the fleet collaborative localization method based on empty road probability field, this method has improved by 59.5% and 50.3% in simulation and real scenarios, respectively. This study provides a new method for cooperative positioning by constructing a road probability field and utilizing a vehicle motion model. Compared to traditional methods, this method improves the accuracy and reliability of positioning and has important application prospects.
- intelligent transportation /
- cooperative positioning /
- localization performance /
- road probability field /
- Geographic Information Systems

HTML全文

图 1 凸角圆弧法缓冲区求取

Figure 1. Finding circular buffers around track corner points

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图 2 基于道路概率场的车队协同定位流程

Figure 2. Process of cooperative positioning of vehicle fleets using road probability field

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图 3 实验场景与道路概率场（红色路段为实验路段）

Figure 3. Experiment scene and road probability map (red section in dicates experiment section)

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图 4 PreScan仿真轨迹

Figure 4. PreScan simulation trajectories

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图 5 MATLAB仿真真值与观测值轨迹

Figure 5. MATLAB simulation truth trajectories and observed trajectories

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图 6 真实场景协同定位结果

Figure 6. Result of cooperative positioning in the real scene

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图 7 仿真场景协同定位结果

Figure 7. Result of cooperative positioning in the simulation scene

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图 8 真实场景定位误差

Figure 8. The positioning error in the real scene

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图 9 仿真场景定位误差

Figure 9. The positioning error in the simulation scene

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表 1 真实场景下缓冲区宽度影响的误差中位数和标准差

Table 1. Error median and standard deviation of the influence of buffer width in the real scene

缓冲区宽度/（°）	GNSS观测/m	EKF方法/m	VMMCL-RPF /m
0.000 01	12.6±2.38	12.5±2.56	6.12±2.88
0.000 02	12.6±2.38	12.5±2.56	6.46±2.88
0.000 03	12.6±2.38	12.5±2.56	5.54±2.99
0.000 04	12.6±2.38	12.5±2.56	7.06±3.00

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表 2 仿真场景下缓冲区宽度影响的误差中位数和标准差

Table 2. Error median and standard deviation of the influence of buffer width in the simulation scene

缓冲区宽度/（°）	GNSS观测/m	EKF方法/m	VMMCL-RPF /m
0.000 01	8.14±3.14	7.76±3.73	3.68±2.02
0.000 02	8.14±3.14	7.76±3.73	3.85±2.00
0.000 03	8.14±3.14	7.76±3.73	3.92±1.86
0.000 04	8.14±3.14	7.76±3.73	4.34±1.88

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表 3 真实场景下车辆数影响的误差中位数和标准差

Table 3. Error median and standard deviation of the influence of the number of vehicles in the real scene

车辆数	GNSS观测/m	EKF方法/m	VMMCL-RPF/m
1	12.65±2.38	12.64±2.56	10.76±3.32
2	12.65±2.38	12.64±2.55	10.53±3.21
3	12.65±2.38	12.51±2.56	7.70±3.11
4	12.65±2.38	12.53±2.56	7.06±3.00

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表 4 仿真场景下车辆数影响的误差中位数和标准差

Table 4. Error median and standard deviation of the influence of the number of vehicles in the simulation scene

车辆数	GNSS观测/m	EKF方法/m	VMMCL-RPF /m
1	8.14±3.84	7.69±3.80	9.11±3.38
2	8.14±3.84	7.69±3.80	7.62±3.36
3	8.14±3.84	7.76±3.77	5.64±2.52
4	8.14±3.84	7.75±3.76	4.71±2.07
5	8.14±3.84	7.77±3.74	4.50±2.01
10	8.14±3.84	7.76±3.73	3.86±1.94
15	8.14±3.84	7.76±3.72	3.64±2.08
20	8.14±3.84	7.76±3.72	3.57±2.05

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表 5 定位误差统计表

Table 5. Positioning error statistics

场景	GNSS观测	EKF	VMMCL-NoRPF	VMMCL-RPF
仿真	482.937 9	460.519 0	572.477 9	232.043 0
实景	776.055 5	777.320 7	785.500 7	390.453 1

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