## 1 Introduction  
With the current rapid economic growth, vehicle ownership is fast increasing, accompanied by more than one million traffic accidents per year worldwide. According to statistics, about 89.8% of accidents are caused by driver's wrong decision-making [1]. So, in order to alleviate traffic accidents, autonomous vehicles have been the world's special attention for its non-driver's participation. Key issues in researching autonomous vehicle include autonomous positioning, environmental awareness, driving decision-making, motion planning, and vehicle control [2]. As an important manifestation of the intelligent level of autonomous vehicles, the driving decision-making has currently become the focus and difficulty for experts in the study of autonomous vehicle [3]. For autonomous vehicle, it needs to rely on driving decision-making mechanism (DDM) to decide accurate driving strategy [4]. Collecting and extracting traffic scene feature by sensors and based on the driving rules, it could not only make accurate driving decisions, but also drive safely in complex traffic environment. In recent years, many scholars have devoted themselves to the research of DDM for autonomous vehicles. [PERSON] et al. [5] established vehicles' desired steering angle model and longitudinal acceleration model based on vehicle states, and developed a control algorithm for the driving model. [PERSON] et al. [6] established a DDM for car following, free driving, and lane changing, with the decision tree algorithm only considering vehicles' running states on the road. To improve the disadvantage of lacking flexibility existing in the decision tree algorithm, [PERSON] et al. [7] used traditional artificial neural network to substitute the decision tree algorithm and trained an ANN (Artificial neural networks) driving decision-making model. [PERSON] and [PERSON] [8] presented a driving decision-making framework for automated driving in highway environment, which considers the interactions between the subject and surrounding vehicles. The previous research works mainly took vehicle states as the reference indexes of DDM, and ignored the influence of road conditions on the driving decision-making. The empirical studies show that road conditions have a great influence on driving decision-making, including weather-related and road geometry related factors [9; 10]. For example, reducing road visibility will change the traffic flow dynamics [11], and changing the geometric layout of the road will easily lead to changes in driving behavior [12]. [PERSON] et al. [10] analyzed the impact of road conditions on vehicles' longitudinal operation, and found that extreme environmental conditions could increase the extent to which a vehicle deviated from normal driving behavior. [PERSON] et al. [11] conducted a series of driving simulations and found that driving in foggy weather led to the lower speed and acceleration, as well as to consider a larger distance from the lead vehicle. [PERSON] et al. [13] studied the car following decision-making under three visibility conditions, and the results showed that low visibility could reduce driver's risk identification ability. [PERSON] et al. [14] analyzed the impact of road curvature and slope on car following behavior. They found that when driving on road with different slopes and curvature, the car-following characteristics of vehicles varied greatly, and then established a car following model while considering curve and slope. [PERSON] et al. [15] investigated optimal maneuvers for vehicles on different road surfaces, such as asphalt, snow, and ice, and found that there were fundamental differences in the optimal maneuvers depending on tire-road characteristics. Previous studies have shown that driving in abnormal road conditions would increase the incidence of traffic accidents that are caused by incorrect driving behavior. So, road conditions, including road curvature, slope, visibility, and friction coefficient, are important parameters for DDM.