Low-altitude inspection technology is evolving into a key component of modern inspection systems due to its significant advantages in cost reduction and efficiency improvement. The efficient operation of unmanned aerial vehicle (UAV) inspections relies on a well-structured top-level planning system, whose core challenges can be summarized into three closely related decision-making layers: site selection, task allocation, and path planning. This paper systematically reviews the current research status and modeling methodologies of this integrated planning framework. Firstly, task characteristics are classified and analyzed from multiple dimensions, including inspection targets, operational scenarios, and task combinations. Subsequently, the modeling approaches, optimization objectives, and constraint systems of the three core layers—site selection, task allocation, and path planning—are elaborated in detail. By establishing a three-tier analytical framework, the review provides a more systematic and hierarchical analytical perspective for the field. Finally, addressing current research bottlenecks in system coordination, real-time responsiveness, and environmental adaptability, future research directions are outlined. These include three-level integrated optimization, cloud-edge-device collaborative computing, and robust planning through the integration of data-driven and physics-based models. The goal of this study is to offer a comprehensive reference for both theoretical research and engineering practice in the advancement of intelligent planning systems for UAV inspection.

Autonomous lane-changing decision making and planning represent a fundamental aspect of advanced driving technologies, playing a pivotal role in improving operational safety, enhancing passenger comfort, and optimizing traffic flow. Current research predominantly emphasizes environmental perception and path planning, yet systematically modeling human behavioral patterns during lane changes remains underexplored, leading to inadequate anthropomorphic decision-making capabilities. Moreover, the conventional fragmented approach to implementing decision-making, trajectory planning, and interaction signaling modules results in insufficient coordination and feedback mechanisms, ultimately compromising dynamic adaptability in real-world driving scenarios. To solve these problems, this study systematically investigates driver behavior patterns through naturalistic driving data analysis, establishes a taxonomy of lane-changing scenarios, and develops a human-like decision architecture incorporating cognitive mechanisms. The model consists of a multilayered decision framework encompassing lane-changing motivation recognition, lane selection, feasibility evaluation, and risk assessment. Furthermore, an information feedback mechanism is established between the decision-making and trajectory planning modules, enabling dynamically coupled and closed-loop control. Simulation experiments conducted on the Prescan/Simulink platform confirm that the proposed method significantly enhances the naturalness and safety of lane-changing behavior in complex traffic environments. This study provides both theoretical support and technical guidance for the development of intelligent lane-changing systems that emulate human cognitive characteristics.