Cooperative Multi-sensor Multi-vehicle Localization in Vehicular Adhoc Networks

Goli, Sepideh Afkhami; Far, Behrouz H.; Fapojuwo, Abraham O.

doi:10.1109/iri.2015.31

Cited by 6 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, in the research of compound positioning by multi-source data, it is necessary to consider the working characteristics of different sensors and the complementarity of applicable scenarios [26]. In addition, the compound positioning of CEVs in simple scenarios can be achieved through the global navigation satellite system (GNSS), and GNSS is not effective for intersection scenarios with poor signals and complex environments [27,28]. At present, many scholars have considered the research on the fusion of compound multisource data and applied it to various traffic scenarios for CEVs.…”

Section: Introductionmentioning

confidence: 99%

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

Wang

Fan

2022

Sustainability

View full text Add to dashboard Cite

With the development of electrified transportation, electric vehicle positioning technology plays an important role in improving comprehensive urban management ability. However, the traditional positioning methods based on the global positioning system (GPS) or roadside single sensors make it hard to meet requirements of high-precision positioning. Considering the advantages of various sensors in the cooperative vehicle-infrastructure system (CVIS), this paper proposes a compound positioning method for connected electric vehicles (CEVs) based on multi-source data fusion technology, which can provide data support for the CVIS. Firstly, Dempster-Shafer (D-S) evidence theory is used to fuse the position probability in multi-sensor detection information, and screen vehicle existence information. Then, a hybrid neural network model based on a long short-term (LSTM) framework is constructed to fit the mapping relationship between measured and undetermined coordinates. Moreover, the fused data are proceeded as the input of the hybrid LSTM model, which can export the vehicular real-time compound positioning information. Finally, an intersection in Shijingshan District, Beijing is selected as the test field for trajectory information collection of CEVs. The experimental results have shown that the uncertainty of fusion data can be reduced to 0.38% of the original level, and the maximum error of real-time positioning accuracy is less than 0.0905 m based on the hybrid LSTM model, which can verify the effectiveness of the model.

show abstract

Section: Introductionmentioning

confidence: 99%

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

Wang

Fan

2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…In the PHD filter, all states and measures are modeled as set-based values in the format of a random finite set (RFS), which makes the PHD filter a promising approach to solving these issues. The PHD filter has been used in CL for vehicles [ 20 , 21 ] and the experiment implied its effectiveness in clutter environments. The PHD filter was also introduced to address the visual tracking issue [ 22 , 23 ], aiming to solve the number variation and noise corruption of the camera.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory

Zhang

Wang

Zhang

et al. 2017

Sensors

View full text Add to dashboard Cite

Cooperative localization (CL) is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs). In this paper, we proposed a CL algorithm based on information entropy theory and the probability hypothesis density (PHD) filter, aiming to enhance the global localization accuracy of the follower. In the proposed framework, the follower carries lower cost navigation systems, whereas the leaders carry better ones. Meanwhile, the leaders acquire the followers’ observations, including both measurements and clutter. Then, the PHD filters are utilized on the leaders and the results are communicated to the followers. The followers then perform weighted summation based on all received messages and obtain a final positioning result. Based on the information entropy theory and the PHD filter, the follower is able to acquire a precise knowledge of its position.

show abstract

Fireworks: an intelligent location discovery algorithm for vehicular ad hoc networks

Basaran

Bulut

2016

Wireless Netw

View full text Add to dashboard Cite

Cooperative Multi-sensor Multi-vehicle Localization in Vehicular Adhoc Networks

Cited by 6 publications

References 13 publications

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory

Fireworks: an intelligent location discovery algorithm for vehicular ad hoc networks

Contact Info

Product

Resources

About