Classifying Motorcyclist Behaviour with XGBoost Based on IMU Data

Autor: Gerhard Navratil, Ioannis Giannopoulos
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Sensors, Vol 24, Iss 3, p 1042 (2024)
Druh dokumentu: article
ISSN: 1424-8220
DOI: 10.3390/s24031042
Popis: Human behaviour detection is relevant in many fields. During navigational tasks it is an indicator for environmental conditions. Therefore, monitoring people while they move along the street network provides insights on the environment. This is especially true for motorcyclists, who have to observe aspects such as road surface conditions or traffic very careful. We thus performed an experiment to check whether IMU data is sufficient to classify motorcyclist behaviour as a data source for later spatial and temporal analysis. The classification was done using XGBoost and proved successful for four out of originally five different types of behaviour. A classification accuracy of approximately 80% was achieved. Only overtake manoeuvrers were not identified reliably.
Databáze: Directory of Open Access Journals
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