## 1 Introduction  
On one hand, with the continuous advancement of intelligent network technologies such as vehicle-to-infrastructure coordination, Connected and Autonomous Vehicles (CAVs) with vehicle-to-vehicle (V2V) communication capabilities are gradually replacing Non-networked Autonomous Vehicles. As a result, the permeability of CAVs is expected to increase, leading to the emergence of heterogeneous traffic flows consisting of a mix of CAVs and NAVs on highways [1; 2; 3; 4; 5]. On the other hand, the communication network of highways for C-ITS mainly involves vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Roadside Units (RSUs), as the information exchange hubs in the communication network, directly impact the communication quality and efficiency of the entire C-ITS network. Therefore, investigating the optimization of RSU deployment on highways for heterogeneous traffic flows with different CAV permeabilities is of significant research value. In a hybrid communication network based on V2V and V2I, RSUs can directly communicate with vehicles within their communication range, while vehicles outside their range adopt V2V multi-hop links to exchange data with RSUs [6]. [PERSON] et al. [7] proposed the Capacity Maximization Placement (CMP) strategy, which combines direct communication and multi-hop relay access modes through an integer linear programming model that considers wireless interference, vehicle population distribution, and vehicle speed. [PERSON] et al. [8] proposed a multi-hop forwarding scheme combining V2V and V2I communications to improve the overall connectivity of vehicular self-organizing networks. The combination of these two communication modes can enhance the average throughput and service continuity of the communication network to a certain extent. Finding the optimal RSU deployment scheme is an NP-hard combinatorial optimization problem [9; 10; 11]. Existing studies on RSU deployment methods generally focus on determining the optimal locations, interval, minimum number of deployments, maximum coverage range, or maximum connectivity within cost constraints [12; 13; 14; 15]. [PERSON] et al. [16] proposed the use of metaheuristic methods, [PERSON] et al. [17] proposed the use of a bio-inspired RSU placement system using ant colony optimization, [PERSON] et al. [18] proposed an improved multi-objective quantum particle swarm optimization (MOQPSO) algorithm for RSU deployment, and [PERSON] et al. [19] presented an integer linear programming formulation and heuristic methods based on taboo search, all of which maximize network coverage with lower cost. [PERSON] et al. [20] proposed the Enhanced Information SHAring scheme using RSU allocation (EISHA-RSU) to address the coverage and connectivity between vehicles and infrastructure. [PERSON] et al. [21] proposed a multi-objective differential evolution method with discrete elitist guidance (MODE-deg) based on crowding distance ranking and non-dominated ranking for RSU selection. In the study of RSU deployment on highways, it was found that vehicle clusters can improve network connectivity [22] and effectively reduce RSU deployment costs. In terms of clustering algorithms, current research mainly focuses on improving clustering algorithms to increase the network lifetime of vehicle clusters and enhance the overall network forwarding mechanism, thereby reducing the number of RSU deployments or increasing the interval between RSUs. Commonly used clustering algorithms include partition-based clustering algorithms, density-based clustering algorithms, and hierarchical clustering algorithms. [PERSON] et al. [23] derived the relationship between the transmission range radius and the probability of connecting vehicle clusters through curve-fitting methods and deduced the relationship between the information transmission range and delay. For any given delay threshold and highway segment length, the optimal number of RSUs can be accurately calculated by analyzing the relationship with segment length. [PERSON] et al.