Abstract
Purpose Analysis of characteristic driving operations can help develop supports for drivers with different driving skills. However, the existing knowledge on analysis of driving skills only focuses on single driving operation and cannot reflect the differences on proficiency of coordination of driving operations. Thus, the purpose of this paper is to analyze driving skills from driving coordinating operations. There are two main contributions: the first involves a method for feature extraction based on AdaBoost, which selects features critical for coordinating operations of experienced drivers and inexperienced drivers, and the second involves a generating method for candidate features, called the combined features method, through which two or more different driving operations at the same location are combined into a candidate combined feature. A series of experiments based on driving simulator and specific course with several different curves were carried out, and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method. Design/methodology/approach AdaBoost was used to extract features and the combined features method was used to combine two or more different driving operations at the same location. Findings A series of experiments based on driving simulator and specific course with several different curves were carried out, and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method. Originality/value There are two main contributions: the first involves a method for feature extraction based on AdaBoost, which selects features critical for coordinating operations of experienced drivers and inexperienced drivers, and the second involves a generating method for candidate features, called the combined features method, through which two or more different driving operations at the same location are combined into a candidate combined feature.
Highlights
With an increasing volume of automobiles, a number of traffic problems, including frequent traffic accidents and severe shortage of energy efficiency, are on the rise (Sagberg et al, 2015)
We found that the error rate of strong classifiers was less than three per cent as the number of weak classifiers reached 15
This paper proposed a method for driving operations characteristics analysis, using AdaBoost and feature cooccurrence
Summary
With an increasing volume of automobiles, a number of traffic problems, including frequent traffic accidents and severe shortage of energy efficiency, are on the rise (Sagberg et al, 2015). To reduce traffic accidents and improve energy efficiency, many studies have been conducted with different results. Kato and Kobayashi (2008) found that fuel consumption could be reduced by 10-30 per cent while driving in eco-mode, which underscored the significance of driving behavior. Bingham et al (2012) found that calm drivers tend to have a lower fuel rate than aggressive drivers in similar situations. For the purpose of honing the skills of inexperienced drivers, research studies focused on driving skills by establishing a driver classification model. For the purpose of honing the skills of inexperienced drivers, research studies focused on driving skills by establishing a driver classification model. Wahab et al (2009) applied the driving style questionnaire (DSQ) method to define individual driving
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