## 2 Literature Review  
Although a platoon formed by the aggregation of multiple vehicles can bring a certain improvement effect to the traffic flow, these improvements are assuming that the vehicle has completed the platoon forming process. Most existing research has focused on the control and optimization of vehicle platoons which have already completed their formation. However, how to form a platoon from randomly spaced vehicles has not yet been adequately addressed. At present, most of the research on this issue has been conducted from the perspective of cybernetics. The vehicles in the platoon are regarded as intelligent units, and the dynamic platoon system is coupled through the cooperative control of the intelligent units. A recent study by [PERSON] et al. (2018) pointed out that the formation control strategy of agents can be divided into five types based on different research methods: the virtual structure strategy, the behavior-based strategy, the leader-follower strategy, the graph-based strategy and the artificial potential field strategy. According to the characteristics of each strategy, Table 1 summarizes the functions that each strategy can achieve during the formation process.  
From Table 1, it can be found that the graph-based strategy and artificial potential field strategy have the most extensive abilities. These two strategies can be well applied to the formation control of intelligent agents. In particular, the graph-based strategy can take into account the topology structure between agents in the formation process. However, due to the kinematic limitations of vehicles and the complicated traffic environment of actual roads, there are essential differences between agents and vehicles. Therefore, the existing agent formation control strategies cannot be directly applied to vehicle platoons. [PERSON] and [PERSON] described the connection between CAVs within a platoon as a spring-mass-damper system ([PERSON] et al., 2018). They completed the description of the concept of a platoon through the construction of this system. Taking the platoon velocity and space distance as optimization objects, [PERSON] and [PERSON] constructed an optimization algorithm for platoon formation and verified it through simulations ([PERSON] and [PERSON], 2018). [PERSON] et al. found that, in practice, a reasonable optimization of the number of vehicles in the platoon and the speed of the platoon can greatly improve the efficiency of road traffic and reduce fuel consumption ([PERSON] and [PERSON], 2018).  
All of the above methods lack the consideration of complex traffic environment factors. The road traffic environment is composed of people, vehicles, roads and other traffic infrastructures. The layout of road signs and markings and the design of the road geometry will greatly affect the actual operation of vehicles because the impact of the traffic environment on vehicle travel is difficult to quantify with a specific indicator. In recent years, a novel research method for driving safety has been proposed [27; 28; 29]. In our previous study, we proposed a potential field model to better describe the driving behavior of vehicles under the CAVs environment [30]. The results indicate that the potential field model can well describe the driving risks constituted by the complex traffic environment and can be used to evaluate the potential driving risks in actual traffic scenarios. In this paper, we will further expand the previous research and focus on exploring the process of forming a platoon when multiple CAVs are in different initial states (different position and speed states), and an innovative combination of graph theory and safety potential field theory is developed to identify the geometric configuration and characterize the driving risk under complex traffic environments during the platoon formation process.