## 1 Introduction
### Related Works  
Due to their highly flexible mobility, UAVs have attracted significant research interest in the field of VDTNs. There are numerous works that combine UAVs with different techniques to improve the quality of service (QoS) of vehicular applications. The authors in [20] addressed the challenge of the low transmission efficiency in DTNs by proposing a social-based mechanism that utilizes community and centrality information to enhance the delivery rate. In this mechanism, a collaborative multiagent reinforcement learning algorithm is introduced, which can enable distributed nodes to learn effective collaboration strategies for relay selection. In [21], the authors investigated the feasibility of deploying the epidemic routing protocol in VDTNs. The study demonstrates that maintaining an appropriate time-to-live (TTL) value in the epidemic routing protocol ensures high packet delivery and low packet loss rates, even in large-scale VDTNs. Additionally, the authors in [22] proposed a novel service discovery algorithm based on Markov models to address the challenges of high mobility in VDTNs. Building upon the BUBBLE Rap forwarding protocol, two relay selection approaches were designed in [23], aiming to improve delivery delay.  
Moreover, UAVs have garnered significant interest in supporting VDTNs due to their cost-effectiveness, high maneuverability, and flexible deployment. Inspired by these, the authors in [24] presented an optimal deployment strategy for relaying UAVs in vehicular networks. The proposed strategy can improve the system capacity by maximizing user rates and reducing outage probabilities. To deal with the problem of the uneven distribution of on-board tasks, a mobility-aware service offloading scheme is proposed for UAV-assisted vehicular networks [25]. This scheme effectively minimizes task processing delays while attaining near-optimal performance in service offloading and migration costs. Furthermore, efforts [26; 27; 28] have been made to explore the use of both cooperative and non-cooperative UAVs in vehicular networks.  
The above works promote the application of UAVs into VDTNs to support opportunistic transmissions. Nevertheless, there are still some problems remained to be further investigated.
* First, the authors in [20; 21; 22; 23] have explored the application of opportunistic transmission in VDTNs. However, these studies primarily focused on leveraging the mobility of ICVs or temporary caching at roadside RSUs to facilitate data distribution. While these studies effectively improve the transmission performance of vehicular communications, they ignore the potential benefits of integrating UAVs into VDTNs. Therefore, additional research is essential to investigate the integration of UAVs in opportunistic transmission strategies and to assess their effects on delivery ratios and latency.
* Second, although the authors in [24; 25; 26; 27; 28] have investigated the deployment of relaying UAVs in vehicular networks, these works have primarily focused on end-to-end delay-sensitive transmissions and optimized network resources (such as spectrum, power, and trajectory) to enhance network performance. However, they did not address the transmission problem in scenarios where direct communication links are not available within vehicular networks.
* Finally, most of the aforementioned studies assumed that all communication nodes in vehicular networks are cooperative. The existence of selfish nodes that can negatively affect transmission performance was not taken into account. It is noted that, in vehicular networks characterized by social attributes, selfish nodes may exhibit non-cooperative behavior by refusing to forward data or by consuming excessive resources, resulting in lower overall efficiency.