## 1 Introduction  
As a core component of future transportation systems, Intelligent Connected Vehicles (ICVs) are driving rapid advancements in onboard intelligent services, including advanced driver assistance systems, real-time route planning, and in-vehicle infotainment [1]. While these services enhance driving safety and user experience, they also impose stringent computational demands on vehicles. Due to limited onboard computing resources and energy constraints, processing computation-intensive tasks locally often leads to severe computational overload and excessive power consumption, making it difficult to meet real-time requirements. Although offloading tasks to remote cloud servers presents a potential solution, the long-distance deployment of cloud infrastructure introduces significant transmission delays and backhaul energy consumption, particularly unsuitable for latency-sensitive vehicular applications [2]. MEC technology, which brings computing and storage resources closer to the network edge, offers a promising alternative [3]. In this context, UAV-assisted vehicular MEC systems leverage their high mobility, flexible coverage, and Line-of-Sight (LoS) communication advantages to provide on-demand edge services for high-speed vehicles, effectively addressing the limitations of traditional ground-based MEC infrastructure, such as high deployment costs and restricted coverage [4]. In UAV-assisted vehicular MEC systems, content caching plays a crucial role in reducing service latency and improving user experience. Existing research primarily focuses on optimizing caching strategies based on content popularity or user mobility to maximize cache hit rates or minimize content delivery delays [5; 6]. For instance, in [7], [PERSON] and [PERSON] utilized deep reinforcement learning (DRL) to optimize edge caching in vehicular networks by predicting user mobility, aiming to minimize content delivery delays. However, most of these works consider content caching in isolation, overlooking the dependencies between computational service caching and task offloading decisions. For example, works like [7] primarily focus on caching optimization, while studies such as [8] by [PERSON] et al. concentrate on developing task offloading strategies for connected vehicles, often considering these aspects in isolation and thus overlooking their inherent dependencies [9]. In practice, vehicles not only require access to content data but also rely on specific computational services for task execution [10]. Successful task processing necessitates that edge nodes cache both the required service programs and relevant data, necessitating a co-optimization of caching and offloading strategies [11; 12]. To address this, recent efforts, including [13] by [PERSON] et al., have begun to explore co-optimization of task offloading and multi-cache placement in UAV-assisted MEC networks, recognizing the joint requirement for service programs and data. Similarly, Ref. [14] by [PERSON] proposed a joint task offloading and resource allocation framework in medical vehicular networks, demonstrating the trend towards integrated solutions. Meanwhile, a common limitation in many existing task offloading studies, such as [15] by [PERSON] et al., which focuses on energy-efficient data offloading in high-mobility vehicular MEC environments, is the implicit assumption that necessary services are always pre-deployed at edge nodes, thereby overlooking the critical impact of service availability on offloading feasibility. Such fragmented optimization approaches struggle to achieve optimal performance. Furthermore, UAV flight trajectories directly influence communication quality, coverage range, and energy consumption, significantly affecting caching efficiency and offloading performance [16]. For instance, in [17], [PERSON] and [PERSON] designed a two-layer edge intelligence framework for UAV-assisted vehicular networks, explicitly considering how UAV trajectories impact task offloading and computing capacity allocation.