*1.1. The Relationship among Velocity Prediction, Traffic Environment, and Vehicle Handling Dynamics*

In order to achieve the goals of "fast", "safe", and "energy savings" for future intelligent networked NEVs, a basic challenge is to model the road traffic environment and accurately predict the evolution of traffic flow [9]. Accurate speed prediction is the key to energy management and energy-saving control of NEVs, and the driving condition determines the energy consumption and driving safety simultaneously. Therefore, it is very important to predict and select working conditions. Drivers control vehicles' speed, and in fact interfere with the prediction of the speed. The level of human driving skills plays a key role in the prediction error of longitudinal and lateral speed. In the context of intelligent connected vehicles, a vehicle can automatically perceive the surrounding environment and drive themselves. The vehicle will have a clear understanding of its future traffic environment and its handling stability, which will increase the accuracy of speed prediction. Therefore, the velocity prediction method is based on vehicle dynamics concerned for intelligent and connected NEVs in this paper.

The road traffic network has temporal and spatial, self-organization, and random characteristics. Analyzing the dynamic evolution law of the road traffic network and the traffic operation situation is helpful to fully understand the complex characteristics of the road network, and one can grasp the mechanism of traffic bottlenecks and traffic accidents [10]. The traffic dynamic evolution law model under the mixed traffic network is constructed to realize the rapid identification of traffic behavior characteristics, which provides technical support for further prediction and evaluation of vehicle speed and road traffic network security risks.

Speed prediction is not only related to the inherent characteristics of traffic flow but also related to the judgment of drivers or intelligent vehicles for traffic risks. Combined with intelligent networked vehicles, the road network abstract model and traffic risk prediction and evaluation model with low computational resource occupancy and more flexibility will lay a foundation for vehicle velocity/routing planning technology under multiple constraints and more effective traffic management methods. Therefore, the establishment of a traffic risk prediction and assessment model based on real-time information is of great importance for improving the safety of vehicles driving in bad weather conditions such as rain and snow. How to effectively integrate the existing traffic information collection methods to analyze the evolution law and operating situation of the road traffic network in real-time, and to accurately assess and predict traffic network security risks is a key research topic in the field of road traffic safety [10]. The framework of road traffic network security risk identification is shown in Figure 3.

**Figure 3.** Research framework of road traffic network security risk identification.

The safety and reliability of traffic have an important influence on users' speed choices [11]. Road traffic network safety risk prediction and assessment is a process of accurate identification, effective assessment, and early warning of risks in road network, which is the core link of road traffic network risk identification [10]. Previous studies of road network safety risk assessment are mostly based on traffic accident simulation or later on the basis of traffic accident data analysis and the qualitative assessment, such as road network risk impact factor assessment, road network vulnerability assessment, and vehicle collision risk factor prediction in case of emergency or extreme weather, but it is difficult to find the safety risk impact factors before the accident on time [10]. Traffic hazard identification and risk quantitative assessment before accidents are the key technologies to break through the risk identification of road transportation network [10].

Under bad weather conditions such as rainy and snowy, research on road traffic risk prediction and assessment has a practical significance for vehicle speed forecast and planning. In order to predict traffic risk, it is necessary to explore the evolution law of road traffic system, which is quantified by the traffic flow model. The evaluation of traffic risk needs to be established with the lateral dynamic model and stability controller model of the vehicle. Vehicles with different handling characteristics need a personalized vehicle traffic risk prediction and assessment. Therefore, we will review the influence of various stability control methods on speed prediction. Next, we will introduce the current research progress of macroscopic traffic flow model and artificial intelligence big data model. At the same time, we will explore the tendency of these two-model combined with lateral traffic risk assessment model on the prediction and assessment of traffic risk. It will give a direction of the solution for basic theoretical problems of real-time accurate prediction and assessment of traffic risk before the accident, with the goal of improving vehicle safety and saving energy for the application of safe path and energy-saving speed planning of intelligent networked vehicles.
