关键词: 智能网联汽车；无人机；协同感知；路径决策；改进 JPS 算法；MAPPO 算法；双层 优化模型

Abstract: With the rapid implementation of high-level autonomous driving technology, intelligent connected vehicles (ICV) face core pain points in complex urban traffic environments, such as limited perception range of single vehicle, insufficient blind area coverage, weak prediction ability of dynamic traffic risks, and poor robustness of path decision-making in extreme scenarios, which seriously restrict the large-scale application of high-level autonomous driving. Aiming at the above problems, this paper proposes a unmanned aerial vehicle (UAV) collaborative perception and path decision-making method for intelligent connected vehicles. A complete technical system is formed through integrated environment modeling, multi-objective optimization model construction, and two-layer collaborative algorithm design, and the effectiveness of the method is verified through simulation experiments in medium-density and high-density urban traffic scenarios. The results show that in the single vehicle-single UAV collaborative scenario, compared with the traditional A* algorithm, the improved jump point search (JPS) algorithm proposed in this paper can reduce the path decision-making time by up to 28.32%, improve the global perception coverage by 19.64%, and reduce the comprehensive traffic cost by up to 6.15%. The research effectively makes up for the spatial limitation of single vehicle perception of intelligent connected vehicles, realizes the closed-loop optimization of collaborative perception and path decision-making, can provide theoretical support and technical reference for the implementation of air-ground integrated autonomous driving technology, and has important engineering application value for promoting the in-depth integration of low-altitude economy and intelligent transportation system.

Key words: intelligent connected vehicles; UAV; collaborative perception; path decision-making; improved JPS algorithm; MAPPO algorithm; two-layer optimization model