In recent years, we have been able to use various services using the position information of smartphones and tablets. In addition, research on intelligent transport systems (ITS) has been actively conducted. To consider reducing traffic accidents by exchanging position information between pedestrians and vehicles by vehicle-to-pedestrian communication, we require accurate position information for pedestrians and vehicles. The GPS (global positioning system) is the most widely used method for acquiring position information. However, in urban areas, the GPS signal is affected by the surrounding buildings, which increases the positioning error. In this study, a method to improve the positioning accuracy of pedestrians using the signal strengths from vehicles and beacons was proposed. First, a Kalman filter was applied to the signal strength. Then, the path loss index was dynamically calculated using vehicle-to-vehicle communication. Finally, the position of a pedestrian was obtained using weighted centroid localization (WCL) after filtering the nodes. The positioning accuracy was evaluated using a simulator and demonstrated the superiority of the proposed method.
References
[1]
Anaya, J.J., Merdrignac, P., Shagdar, O., Nashashibi, F. and Naranjo, J.E. (2014) Vehicle to Pedestrian Communications for Protection of Vulnerable Road Users. IEEE Intelligent Vehicles Symposium Proceedings, Dearborn, 8-11 June 2014, 1037-1042.
https://doi.org/10.1109/IVS.2014.6856553
[2]
Sepulcre, M. and Gozalvez, J. (2012) Experimental Evaluation of Cooperative Active Safety Applications Based on V2V Communications. In: Proceedings of the 9th ACM International Workshop on Vehicular Inter-Networking, Systems, and Applications, ACM, New York, 13-20. https://doi.org/10.1145/2307888.2307893
[3]
Krista, M. and Pete, B. (2019) Smartphone GPS Accuracy Study in an Urban Environment. PLoS ONE, 14, e0219890. https://doi.org/10.1371/journal.pone.0219890
[4]
Garnett, R. and Stewart, R. (2015) Comparison of GPS Units and Mobile Apple GPS Capabilities in an Urban Landscape. Cartography and Geographic Information Science, 42, 1-8. https://doi.org/10.1080/15230406.2014.974074
[5]
Gu, Y., Hsu, L.-T. and Kamijo, S. (2015) Passive Sensor Integration for Vehicle Self-Localization in Urban Traffic Environment. Sensors (Basel), 15, 30199-30220.
https://doi.org/10.3390/s151229795
[6]
Suhr, J.K., Jang, J., Min, D. and Jung, H.G. (2017) Sensor Fusion-Based Low-Cost Vehicle Localization System for Complex Urban Environments. IEEE Transactions on Intelligent Transportation Systems, 18, 1078-1086.
https://doi.org/10.1109/TITS.2016.2595618
[7]
Xue, W., Qiu, W., Hua, X. and Yu, K. (2017) Improved Wi-Fi RSSI Measurement for Indoor Localization. IEEE Sensors Journal, 17, 2224-2230.
https://doi.org/10.1109/JSEN.2017.2660522
[8]
Varshney, V., Goel, R.K. and Qadeer, M.A. (2016) Indoor Positioning System Using Wi-Fi & Bluetooth Low Energy Technology. 13th International Conference on Wireless and Optical Communications Networks, Hyderabad, 21-23 July 2016, 1-6.
https://doi.org/10.1109/WOCN.2016.7759023
[9]
Zhu, J.Y., et al. (2014) RSSI Based Bluetooth Low Energy Indoor Positioning. International Conference on Indoor Positioning and Indoor Navigation, Busan, 27-30 October 2014, 526-533. https://doi.org/10.1109/IPIN.2014.7275525
[10]
Li, G., Geng, E., et al. (2018) Indoor Positioning Algorithm Based on the Improved RSSI Distance Model. Sensors, 18, 2820. https://doi.org/10.3390/s18092820
[11]
Blumenthal, J., Grossmann, R., Golatowski, F. and Timmermann, D. (2007) Weighted Centroid Localization in Zigbee-Based Sensor Networks. IEEE International Symposium on Intelligent Signal Processing, Alcala de Henares, 3-5 October 2007, 1-6. https://doi.org/10.1109/WISP.2007.4447528
[12]
Subedi, S., Kwon, G.-R., Shin, S., seung Hwang, S. and Pyun, J.-Y. (2016) Beacon Based Indoor Positioning System Using Weighted Centroid Localization Approach. Eighth Inter-National Conference on Ubiquitous and Future Networks, Vienna, 5-8 July 2016, 1016-1019. https://doi.org/10.1109/ICUFN.2016.7536951
[13]
Tang, S. and Obana, S. (2018) Improving Performance of Pedestrian Positioning by Using Vehicular Communication Signals. IET Intelligent Transport Systems, 12, 366-374. https://doi.org/10.1049/iet-its.2017.0134
P Series, Propagation Data and Prediction Methods for the Planning of Short-Range Outdoor Radiocommunication Systems and Radio Local Area Networks in the Frequency Range 300 MHz to 100 GHz.
https://www.itu.int/dms_pubrec/itu-r/rec/p/R-REC-P.1411-9-201706-I!!PDF-E.pdf
