## 1 Introduction  
Civilian applications benefit from the great aerial mobility and flexibility of unmanned aerial vehicles (UAVs) in areas such as surveillance [1], tracking [2], smart agriculture [3], and wildfire detection [4]. Wireless communication networks (WCNs) and sensor networks of the Internet of Things (IoTs) are among the popular areas in which UAVs play problem-solving and other key roles [5, 6]. UAVs are utilized as on-demand airborne base stations (BSs) when the terrestrial cellular network is congested in social events or when they are damaged in natural disasters [7]. Additionally, UAVs can support IoTs in remote areas where terrestrial cellular networks are not available [8]. Moreover, UAV networks can be utilized for mobile edge computing to power IoTs [9, 10, 11]. Path planning for maximizing the coverage area and quality of service (QoS) is a common research topic [12, 13]. Nevertheless, the most recent application of UAVs for future WCNs is providing reliable, low latency, and stable aerial wireless communication services for mobile nodes [14], mobile sensor networks [15], and intelligent vehicles [16; 17]. Intelligent vehicles (IVs) are designed for autonomous driving in the context of smart cities and intelligent transportation systems, which is a trending research topic [18]. For autonomous driving, IVs require capabilities such as perception (localization), path planning, and path tracking (control). For perception and localization, IVs use various inputs such as visual data, LiDAR, and radar sensors to monitor the environment and create a map for path planning [19]. Visual place recognition and localization in a map, or simultaneous localization and mapping (SLAM) are popular research topics in the robotics community [20]. For these tasks, IVs need wireless communication [21] for vehicle-to-vehicle (V2V) [22] and vehicle-to-everything (V2X) [23] communication to exchange perception knowledge; create accurate maps of dynamic environments, including other vehicles, pedestrians, etc. ; and ensure safe driving and crash avoidance. However, the increased data rate in wireless communication of smart cities [24] has raised bandwidth requirements, thus necessitating higher carrier frequencies in WCNs. This has led to the deployment of the beyond fifth-generation (B5G) and sixth-generation (6G) WCNs with millimeter-wave (mmWave) communication [25]. Beamforming through multi-input multi-output (MIMO) BSs, limited to line-of-sight (LoS) communication, is likely the main feature of future WCNs [13] for transmitting stronger signals. Furthermore, passive reconfigurable intelligent surfaces (RISs) [26] are adopted to reduce the complexity of future WCNs and enhance network sustainability by reducing energy consumption in beamforming [27]. RISs are effective in reflecting electromagnetic waves and covering blind spots in the network [26], a critical advantage in dense urban environments where LoS paths between BSs and users are obstructed by buildings and skyscrapers [28]. Utilizing UAVs is effective in providing aerial LoS wireless channels for the Internet of Intelligent Vehicles (IoVs) [29; 30] for autonomous driving and accomplishing civilian and municipal services in smart cities [31]. Here, trajectory design and wireless communication control of UAVs are critical, as UAVs may act as active BSs, active relays, or passive reflectors equipped with RIS [32; 33]. However, for mobile IoVs, time is an extra crucial factor that brings the UAV speed into the optimal path-planning formulation that converts the problem to path-plus-speed, i.e., _trajectory_ optimization. Furthermore, UAV navigation for IoVs in dense urban areas and urban canyons, where buildings pose significant obstacles, requires crash avoidance trajectory optimization. Therefore, by taking UAV speed as an optimization variable as well as considering obstacle-free navigation, UAV dynamics and kinematic motion constraints should be considered in the trajectory design [14], which has been overlooked in the wireless communication community.