This paper conducts a thorough exploration of vehicletoeverything (V2X) communication in the realm of intelligent connected vehicles (ICVs). It initiates by tackling challenges across three pivotal phases of cooperative communication: precommunication, duringcommunication, and postcommunication. The discourse delves into a spectrum of concepts and strategies to surmount these challenges. Furthermore, it meticulously scrutinizes diverse communication scenarios and associated techniques, evaluating their significance and feasibility. Moreover, an indepth analysis of various datasets is undertaken, considering their distinctive attributes and suitability for diverse communication tasks. The paper critically examines and debates the platforms and frameworks used in the experiments, providing valuable insights into their performance. Following a comprehensive review of existing methods and datasets, the paper identifies potential research directions and challenges that warrant further exploration in the realm of V2X communication for intelligent connected vehicles. This comprehensive examination contributes to a deeper understanding of the subject, paving the way for future advancements in this dynamic field.

A new type of transportation vehicle, the flying car, is attracting increasing attention in the automotive and aviation industries to meet people's personalized transportation needs for urban air traffic and future travel. With its vertical takeoff and landing capability, flying cars can expand its feasible routes into 3D space. The above process, however, requires sufficient path planning to obtain optimal 3D path. To solve the above issue, the inspiration was drawn from animals in the natural world to design a type of flying car that can travel in various urban environments such as land and low altitude by using different components like wheels and propellers. Incorporating the motion characteristics of flying cars in the future urban environment, segmenting the energy consumption and time models of various stages of flying cars is conducted. The introduction of temporal A* algorithm into the new field of flying cars for the first time, the priority planning algorithm for multiple flying car groups based on an improved A* algorithm utilizing safety intervals is proposed. The proposed strategy is validated on different sizes of urban environment maps. The results indicate that on a complex map with 452 nodes, the strategy effectively reduces distance by 4.5 m, decreases energy consumption by 85.8% and improves planning speed. Compared with the strategy based on multicommodity network flow integer linear programming, the planning results are roughly the same, but the weighted cost of employing this strategy is decreased by 5.2%, and the path distance is reduced by 0.34 m.