Driving Style Classification Based on Driving Operational Pictures

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.

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