## 1 Introduction
However, such methods are primarily applied in robotic path planning. When adapted to intelligent semi-trailers, the quality of the resultant planned paths leaves much to be desired, and their practical applicability is notably restricted. Algorithms based on graph searching include the Dijkstra algorithm, A* algorithm, D* algorithm, and their fusion and improved algorithms [20; 21; 22; 23]. However, the limitations of such algorithms are rather prominent when the vehicle velocity is high and in dynamic traffic conditions. They are mainly applicable to the determination of the shortest variable paths in static traffic conditions. Algorithms based on intelligent optimization are methods that use intelligent optimization algorithms to solve path-planning problems. These algorithms usually simulate biological group behaviors, evolutionary processes, or physical phenomena in nature to find the optimal solution or approximate optimal solution to problems. Common intelligent optimization algorithms include genetic algorithms [24], ant colony algorithms [25], particle swarm optimization algorithms [26], etc. Through continuous iteration and evolution, they search in the solution space and can effectively handle complex problems that are difficult to solve by traditional optimization algorithms. Relevant research has made improvements targeting the deficiencies of traditional optimization algorithms [27; 28; 29]. However, such methods generally have the drawbacks of slow convergence speed and low computational efficiency. At present, there are relatively few studies specifically focusing on the trajectory planning of intelligent semi-trailers. [PERSON] and [PERSON] designed an Active Trailer Steering (ATS) controller to address the high-speed driving safety issues of semi-trailers. Through theoretical analysis and simulation verification, the lateral stability of semi-trailers and the trajectory tracking performance have been effectively improved [30]. [PERSON] et al. proposed an adaptive path-following control framework for the automatic docking of semi-trailers, effectively solving the path-following problem of semi-trailers during the docking process [31]. [PERSON] et al. proposed a new type of lane-keeping path planner and steering wheel angle controller for semi-trailers. Considering the unique dynamic characteristics of semi-trailers, it effectively improves the lane-keeping performance of semi-trailers during driving [32]. Existing path-planning methods face three critical limitations:  
1. Roll stability neglect: Most of these methods focus on four-wheeled vehicles, and there is less research on semi-trailers. In comparison, the path planning problem of semi-trailers is more complicated. The intelligent semi-trailers have high requirements for roll stability, which needs to be considered when carrying out path planning. 2. Traffic conditions adaptability: Changes in the surrounding traffic conditions, such as the road surface adhesion coefficient, load capacity, and driving velocity, affect the path planning of intelligent semi-trailers, but they are often ignored. 3. As commercial vehicles for bulk transportation, the impact of load capacity on lane-changing paths cannot be underestimated. Aiming at the above problems, we propose an intelligent semi-trailer path planning method considering the surrounding traffic conditions and vehicle roll stability. The primary contributions of this article can be outlined as follows:  
1. A lane-changing method applicable to intelligent semi-trailers is proposed, which accounts for variations in the surrounding traffic conditions and the characteristics of intelligent semi-trailers, thereby maintaining their roll stability throughout the lane-changing maneuver. 2. A multi-state fusion lane-changing decision-making system based on a fuzzy inference system is constructed. This system comprehensively integrates multiple surrounding traffic conditions, including the road adhesion coefficient, velocity, acceleration, and the distance between the self-vehicle and the surrounding vehicles.