## 1 Introduction
This two-stage classification procedure enables the robust and unambiguous assignment of sequences to the four global driving states, acceleration, deceleration, cruising, and idling with fixed criteria. The result showed that more than 95% of all driving points can be assigned to one of the four global driving states. [PERSON] et al. [18; 19] proposed the double articulation analyzer with temporal prediction (DAA-TP) model on the basis of the double articulation analyzer (DAA) model. It was applied to the ADAS to predict the  
Figure 1: A framework of driving style analysis. driving scene and driving behaviors in the near future. In order obtain the more precise solution for log time series data, [PERSON] et al. [20] proposed a memetic algorithm for multivariate time-series segmentation by calculating the score of a point using regularized covariance. Experiments demonstrated that the proposed method was superior to conventional segmentation methods. [PERSON] et al. [21] presented an online timing data segmentation and behavior recognition model using HDP-HMM (hierarchical Dirichlet process-hidden Markov model). The above approaches can decompose time series data effectively. However, they require prior knowledge about the number of states or clusters. Setting prior information artificially may lead to mode overfitting or underfitting. Meanwhile, the residence time distribution of each state is not considered, which may result in an extremely short period of some data fragments. Although many studies have been carried out to characterize driving styles, most of the current studies use statistical features or the frequency and duration proportion of driving maneuvers separately and in combination to quantify driving styles. In order to obtain better speed performance control, [PERSON] et al. [22]. collected driving data under different scenarios through a real vehicle platform and divided drivers into three categories (aggressive, moderate, and mild) according to the statistical characteristics of the data (the mean/standard deviation of brake pressure, throttle position, and vehicle speed). Their analysis result showed that the aggressive driver had the highest values for all throttle position indices, while the mild had the least. The hidden Markov model (HMM) has been widely applied to model and predict the driver state and driving behavior; researchers in [23,24] applied a hidden Markov model (HMM) to identify the underlying relationship between observations and driver state. To deal with driver behavior uncertainty in driving style recognition, [PERSON] et al. [25] developed a statistical-based recognition method, based on the Bayesian theory, to classify drivers into two groups, i.e., aggressive and normal (typical) using vehicle speed and throttle position. [PERSON] et al. [26] presented a rapid driving style recognition method in a car following scene based on the trajectory features (acceleration, relative speed, and relative distance). These methods are easy to describe driver characteristics from a statistical perspective. However, under actual traffic conditions, people's driving behaviors are random, and it is not sufficient to use statistical metrics to describe the driving styles. Considering user comfort, [PERSON] [13,27] classified driving style based on the objective variables (longitudinal acceleration and jerk) selected according to their frequency of occurrence in real traffic. These variables allow the driving style to be classified on a comfort-oriented scale. However, the maximum acceleration or maximum speed was limited during driving behavior construction. [PERSON] et al. [8] presented a method to estimate driving style in highway traffic using the transition probabilities between 12 maneuvers. The result demonstrated that high-risk drivers were more likely to be involved in approaching, near following, and constrained left and right lane changes.