## 1 Introduction  
In recent years, with the rapid integration of high-tech and advanced automotive technologies such as computers, the Internet, communications and navigation, automatic control, artificial intelligence, machine vision, precision sensors, high-precision maps and smart cars (or unmanned vehicles), smart driving has become one of the world's automotive engineering research hotspots and a new impetus of the automotive industry's growth. According to authoritative media at home and abroad, in the future of the automotive industry, more than 90% of scientific and technological innovation will focus on the field of automotive intelligence. Therefore, smart vehicles are safe, efficient, energy-efficient next-generation vehicles [1; 2], and the study of smart cars has a very important significance, which has become the focus of the global automotive industry. The driver intention is reflected by his/her own inner state in the driving process. It cannot be obtained directly during driving and is only predicted by the driver's movements, vehicle status and traffic environment information. Driver's intention classification and identification are identified as comprising the key technology for intelligent vehicles and are widely used in a variety of Advanced Driver Assistant Systems (ADAS) [3; 4], such as the Adaptive Cruise Control System (ACC) [5; 6], the Active Front Steering System (AFS) [7; 8; 9; 10], the Parking Assistance Systems (PAS) [11; 12], the steer-by-wire systems [13] and Man-machine Co-driving Electric Power Steering (MCEPS) system [14; 15]. The classification and identification of driver's intention are based on the real-time acquisition of the driver's operating signal and car state, or at the same time, by monitoring the driver's head movement range and facial expressions, the driver's behavior is distinguished and identified to obtain the driver's driving intention [16]. Many scholars are committed to study the classification and identification of driver's intention. [PERSON] et al. [17] proposed a novel method based on an artificial error back-propagation neural network to identify the driver's starting intention. [PERSON] et al. [18] proposed an intelligent cognitive method for driver's intention identification based on the Hidden Markov Model (HMM). The method mainly includes data segmentation, time series data labeling and the identification and generation of the driving mode. [PERSON] et al. [19] proposed an algorithm based on the driver's intention to identify the direct yaw moment control, in which the driver steering intention identification is through the Hidden Markov Model (HMM). The use of the dynamic Bayesian network was combined with the past driving state and the current driving state to predict the driver's intention of parking at the crossroads. [PERSON] [20] used the maximum posterior probability assessment method to obtain the driver's steering model parameters. He established a steering model that can effectively improve the recognition accuracy and that has the function of predicting the driver's driving strategy. The previous research works mainly concentrated on a single traffic environment, such as the straight road or the crossroads intentions, and not on variety of typical steering conditions under the driving intention identification study. The driving intention under each condition requires multiple drivers' characteristic steering parameters, such as driving parameters (steering angle, angular velocity and torque) and vehicle status parameters (roll angle, lateral acceleration and yaw rate). However, if all the characteristic parameters are used for classification and identification, the computational complexity is increased due to the large number of characteristic parameters. Additionally, analysis of the situation becomes much more difficult. Although each feature parameter provides some information, some of the characteristic parameters are correlated, and the characteristic parameters are not independent of each other.