Volume 41 Issue 4
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(4): 80-87
WANG Zhongqi, CHEN Wei, DU Luyao, LEI Zhen, LEI Ting. Cooperative Positioning of Vehicle Fleets Using Road Probability Field[J]. Journal of Transport Information and Safety, 2023, 41(4): 80-87. doi: 10.3963/j.jssn.1674-4861.2023.04.009
Citation: WANG Zhongqi, CHEN Wei, DU Luyao, LEI Zhen, LEI Ting. Cooperative Positioning of Vehicle Fleets Using Road Probability Field[J]. Journal of Transport Information and Safety, 2023, 41(4): 80-87. doi: 10.3963/j.jssn.1674-4861.2023.04.009
shu

Cooperative Positioning of Vehicle Fleets Using Road Probability Field

doi: 10.3963/j.jssn.1674-4861.2023.04.009
  • 1.

    School of Automation, Wuhan University of Technology, Wuhan 430070, China

  • 2.

    University of Kansas, Lawrence 66045, USA

  • Received Date: 2023-02-10
    Available Online: 2023-11-23
    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.

     

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