Data-Based Analysis Of Sampling And Estimation Methods For Vehicle Tracking Over Wireless Networks

Keywords

estimation; sampling; vehicle tracking; wireless communication

Abstract

Wireless networks provide the possibility of vehicle tracking using information broadcast by vehicles in a local area. In vehicular safety networks, vehicles include their movement information in broadcast messages, allowing receivers of the messages to track them in real time and detect hazardous situations. There are many choices for sampling, communication and estimation of the movement data. In this paper, we look at four of the main choices that are currently used by industry and in research works. We also introduce a new heuristic estimation method based on our observation of data. To compare these methods, we use several datasets that are publicly available. The methods studied in this paper use either periodic beaconing or error-dependent sampling and communication method, and combine it with either constant-speed or constant-acceleration estimation methods. The results show that while a combination of error-dependent sampling and constant-acceleration estimation produces the best tracking results, it is possible to improve the performance by more realistic estimation methods. It is also observed that although different datasets produce similar results, the findings are more accurate for datasets of normal driving, compared to high dynamic situations.

Publication Date

3-29-2018

Publication Title

Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017

Volume

2018-January

Number of Pages

202-207

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.45

Socpus ID

85038036385 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85038036385

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