## 1 Introduction
This fusion significantly bolstered real-time perception interactions between vehicles and adjacent roadside facilities. [PERSON] et al. [15] tapped into vehicle-to-vehicle communication to amplify collective perception, thereby broadening the scope of local sensing. They introduced an approach for real-time lane perception, drawing on information from cooperatively distributed vehicles and further extending the perceptual radius. Obstacle avoidance path planning algorithms can generally be grouped into three primary categories, depending on their underlying planning principles: search-based algorithms, optimization-based algorithms, and geometric curve interpolation-based algorithms [16]. [PERSON] et al. [17] introduced an approach that combines target biasing with a bidirectional Rapidly-exploring Random Tree (RRT) technique for generating obstacle avoidance paths. Given that RRT techniques employ random seeds, their associated planning efficiency tends to be on the lower side. [PERSON] et al. [18] refined the heuristic function inherent to the A* algorithm and incorporated a novel path-smoothing strategy. This approach aims to produce safer paths by ensuring a specified distance is maintained from obstacles. However, this algorithm necessitates the discretization of the planning space, leading to a non-continuous path curvature. While search-based methods operate under the presumption of a static environment, their incremental nature causes a sharp escalation in computational expenses. Though effective at lower speeds, the heightened computational demand in intricate scenarios impedes its ability to generate the most optimal path [19]. [PERSON] et al. [20] implemented a directed expansion strategy for the addition of new nodes, thereby enhancing the efficiency of the modified RRT algorithm. They further integrated curvature constraints to smooth the path. Notwithstanding these improvements, the obstacle avoidance paths produced by this algorithm tend to be more extended, and the calculation of directed nodes within this framework presents a relative intricacy. Relative to search and optimization approaches, geometric curve interpolation algorithms offer computational simplicity and facilitate the straightforward construction of optimal objective functions [21]. These algorithms encompass parameterized curves, including but not limited to, circular curves, spiral curves, trigonometric curves, and polynomial curves. Key merits of these algorithms encompass their computational efficiency, intuitive design, and high precision [22]. Such algorithms are prevalently employed in studies focused on obstacle avoidance trajectory planning for intelligent driving vehicles. While paths crafted from line segments, circular arcs, and helical arcs might be apt for human drivers, they do not inherently align with the unique needs of intelligent driving vehicles. Bezier curves and B-spline curves stand as foundational geometries, ensuring continuous curvature at the junctures of path segments [23]. They frequently serve to craft smooth obstacle avoidance paths that are compliant with vehicle dynamic constraints. As noted by [PERSON] et al. [24], they harnessed Bezier curves for continuous curvature path generation. Nevertheless, the intrinsic characteristic of Bezier curves having locally fixed control points renders them somewhat rigid and less adaptable. Building on this, [PERSON] et al. [25] presented an algorithm predicated on cubic B-spline curves to derive collision-free paths for autonomous vehicles. While this algorithm autonomously crafts paths with sustained curvature, it does exhibit curvature discontinuities at specific nodes. [PERSON] et al. [26] explored a quasi-optimal trajectory planning methodology employing cubic B-spline curves, targeting paths for dual independently driven wheeled vehicle platforms. Yet, their investigations overlooked the incorporation of an upper bound constraint on curvature. Taking a different approach, [PERSON] et al.