@inproceedings{854eb464151d41d4b067bca40a07aa89,
title = "Low speed vehicle localization using WiFi fingerprinting",
abstract = "Recently, the problem of fully autonomous navigation of vehicle has gained major interest from research institutes and private companies. In general, these researches rely on GPS in fusion with other sensors to track vehicle in outdoor environment. However, as indoor environment such as car park is also an important scenario for vehicle navigation, the lack of GPS poses a serious problem. This study presents an approach to use WiFi Fingerprinting as a replacement for GPS information in order to allow seamlessly transition of localization architecture from outdoor to indoor environment. Often, movement speed of vehicle in indoor environment is low (10-12km/h) in comparison to outdoor scene but still surpasses human walking speed (3-5km/h, which is usually maximum movement speed for effective WiFi localization). This paper proposes an ensemble classification method together with a motion model in order to deal with the above issue. Experiments show that proposed method is capable of imitating GPS behavior on vehicle tracking.",
keywords = "WiFi fingerprinting, autonomous vehicle, ensemble classification, indoor localization, low speed vehicle",
author = "Nguyen, \{Dinh Van\} and Recalde, \{Myriam Elizabeth Vaca\} and Fawzi Nashashibi",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016 ; Conference date: 13-11-2016 Through 15-11-2016",
year = "2016",
doi = "10.1109/ICARCV.2016.7838772",
language = "English",
series = "2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016",
address = "United States",
}