Comprehensive Identification of Driving Style Based on Vehicle's Driving Cycle Recognition
Kaichong Liang, Zhiguo Zhao, Wenchang Li, Jiawei Zhou, Danshu Yan
Abstract
The driving style of human automobile drivers plays a vital role in vehicle energy management and driving safety. Human drivers often exhibit different driving styles during different driving cycles. Moreover, short-term driving behaviors cannot accurately reflect their comprehensive driving styles. Thus, a comprehensive driving style identification model, based on driving cycle recognition, is proposed in this study. The model was developed to comprehensively assess and more accurately identify long-term driving styles. First, the characteristic parameters of the driving cycle and style are selected and calculated, and the dimensions of the data collected from real vehicles are minimized through principal component analysis (PCA). Second, a two-stage clustering algorithm based on the particle swarm optimization K-means optimized by the genetic algorithm (GA-PSO-K-means) is used to obtain the type labels of driving cycles and styles to ensure the stability of the clustering algorithm. Third, an artificial neural network (ANN) is utilized to build the instantaneous recognition model of the driving cycle and style. Based on this, a comprehensive driving style identification model is built using the analytic hierarchy process(AHP). Finally, the bench and real vehicle tests are used to verify that the proposed model is accurate and effective.