Abstract
Awareness and attention of drivers while driving show a vital role in decreasing the number of collisions. In the modern decades, in-car entertainment is a major cause of degradation of drivers' performance and losing consciousness. However, there are other factors such as drowsiness and tiredness that have an important part as well. Earlier detection of this degradation in behavior can be used to provide suggestions to the driver while driving. The main motive behind the work is to mitigate the effects of manually labeling the data which costs lot of time and also would result in inaccurate labels. The proposed work has 2 parts: Feature reconstruction and Stacking for classification. The Feature reconstruction phase involves clustering of data from different sensors and labeling the style of driving. Then ensemble-based Stacking classifiers which are optimized with artificial bee colony algorithm is adopted for the driving style classification. The performance analysis is done by applying the stacking classifier to data from OBD. The Stacking classifier is able to achieve an accuracy of above 98% irrespective of the classical learning algorithms.
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PG: Study conception, Design, Data collection, Analysis; FUM: Interpretation of results and manuscript preparation.
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Communicated by Meng Joo.
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Priyadharshini, G., Ukrit, M.F. Stacking optimized with artificial bee colony for driving style classification by feature reconstruction from OBD II data. Soft Comput 27, 591–603 (2023). https://doi.org/10.1007/s00500-022-07135-3
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DOI: https://doi.org/10.1007/s00500-022-07135-3