## 1 Introduction  
Better understanding of the variability in individual driving styles would be especially useful for understanding driver preferences, mechanisms for vehicle control, path planning, and for developing more realistic traffic simulations [1, 2] to improve road safety and driver's acceptance of L2-L3 level intelligent vehicles [3, 4]. Previous studies have shown that drivers with an aggressive style are prone to bad behavior such as rapid acceleration, rapid deceleration, near following, and frequent lane changing in the process of driving. Regulating and warning of such behavior are conducive to promote the driving safety of vehicles [5]. In addition, drivers have personalized requirements for the advanced driver assistance system (ADAS) due to different driving styles. For example, a personalized adaptive cruise control (PACC) was designed to satisfy the personalized needs of drivers with different driving styles [6]. [PERSON] et al. [7] collected various styles of lane changing characteristics and designed a personalized lane change strategy to meet the personalized requirements of lane changing. It is necessary to explore the driving styles and their application. Figure 1 summarizes the framework of driving style analysis [8; 9]. Driving style refers to all activities (layers) performed by a driver, including perception, strategic decision, state adjustment, vehicle operation (action), as well as maintaining situation awareness and engaging in secondary tasks. Existing studies have been performed on the above activities. Studies on the perception aspects of driving style focus on visual characteristics before and after lane changes, such as the different focuses in scanning and critical areas [10]. Studies of the decision aspects of driving style consider time-saving or short-distance routes [11]. Studies of the state aspects of driving style consider maneuver preferences such as close following, far following, and frequently changing lanes, etc. [12]. Studies of the operation (action) aspects of driving style include preferences for rapid acceleration, hard braking, etc. [13]. Previous studies showed that driving style mainly concerns state and operational aspects [13]. They categorized driving behavior into driving maneuvers (e.g., following, deceleration with respect to a moving target, lane changing, etc.) [14]. These studies focused on identifying the difference in driving style between categories based on statistical features with respect to multi representation of driving layers, without further consideration of the driving behavior frequency and duration proportion properties. This paper focuses on quantizing driving style and revealing the correlation between driving style and driving behavior. The key of driving style analysis is to segment the time series driving data into fragments and extract effective indicators to characterize driving behaviors. Usually, the vehicle states and driver actions are recorded in time series data and can be decomposed into fragments to characterize the driving style. [PERSON] et al. [15] proposed a novel methodology to generate a stochastic driving cycle by segmenting the highly simplified acceleration and deceleration. However, because the synthetic character and numerous driving situations of real driving, such as transition areas from city to country driving, were not considered, it does not reflect actual driving behavior. [PERSON] and [PERSON] [16] developed a two-step algorithm to segment drivers' behaviors in car-following. Eight predefined variables, longitudinal acceleration, lateral acceleration, yaw rate, vehicle speed, lane offset, yaw angle, range, and range rate, were used to obtain 30 state-action clusters, based on which the car-following model related to the driving style was established. [PERSON] [14] and [PERSON] [17] presented a new two-stage segmentation approach.