Abstract
Accurately describing and classifying driving style is crucial for driving safety intervention strategies in the design of advanced driver assistance systems (ADASs). This paper presents a novel driving style classification method based on constructed driving operational pictures (DOPs) which map sequential data from naturalistic driving into 2-D pictures. By using the nested time window method, 798/1683/1153 DOPs sized 42 (features) × 60 (seconds) were generated for three different driving styles (low-risk, moderate-risk, and high-risk), respectively. The three kinds of neural network algorithms, i.e., convolutional neural network (CNN), long short-term memory (LSTM) network, and pretrain-LSTM were applied to recognize driving styles based on DOPs. The results showed that CNN performed the best with an accuracy of 98.5%, better than the traditional support vector machine (SVM) method. This study provides a new perspective to classify driving style which may help design ADASs operating characteristics to improve driving comfort and safety.
Highlights
Driving style is defined as a set of individual driving habits formed gradually with the accumulation of driving experience [1]
The multidimensional driving style inventory (MDSI) is a 44-item questionnaire for driving style evaluation [10], The associate editor coordinating the review of this manuscript and approving it for publication was Xiaosong Hu
NEURAL NETWORKS FOR DRIVING STYLE CLASSIFICATION In this paper, three advanced neural network algorithms including convolutional neural network (CNN), long shortterm memory (LSTM) network, and pretrain-long short-term memory (LSTM) were applied for driving style classification
Summary
Driving style is defined as a set of individual driving habits formed gradually with the accumulation of driving experience [1]. Subjective evaluation can be effective for classifying driving style, the method is excessively labor intensive and requires experts to always be in the vehicle. G. Li et al.: Driving Style Classification Based on DOPs expert evaluator often is not normally in the vehicle. Eboli et al [14] used vehicle speed and accelerations to determine drivers’ driving style by counting the number of data out of the pre-defined safety domain. To advance the state-of-the-art and overcome concerns with previous methods, this paper innovatively proposes a nested time window method to construct drivers’ quasi-realtime driving operational pictures (DOPs), based on which advanced machine learning techniques are used to classify driving style.
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