## 1 Introduction  
The swift advancement and proliferation of intelligent driving technology are now at the forefront of the automotive industry's discussions [1; 2]. As intelligent driving vehicles emerge, they showcase vast potential across various domains. However, given the intricate and unpredictable nature of road traffic conditions, ensuring safe obstacle avoidance in complex settings [3; 4] stands as a pivotal challenge in this area of research. In the autonomous navigation of intelligent driving vehicles, path planning for obstacle avoidance is crucial for ensuring safe travel. Achieving this necessitates the continuous perception of the surrounding environment and the implementation of precise and efficient path planning algorithms. At present, autonomous vehicles commonly depend on individual sensors, such as LiDAR or cameras, for environmental perception. However, due to the intricate and heterogeneous nature of environmental scenarios and the inherent limitations of single-sensor approaches, these methods fall short of satisfying the demands of diverse driving situations. Consequently, to achieve a more holistic perception of the surrounding environment and proficiently handle diverse scenarios, autonomous driving systems often necessitate real-time sensor data supplementation and fusion, aided by resources such as up-to-the-minute mapping information. GIS platforms, by leveraging pre-existing road environment data and other relevant information, facilitate real-time sensor data processing and fusion. This substantially augments the environmental perception and navigation planning abilities of intelligent driving vehicles [5]. [PERSON] et al. [6] employed GIS-based path planning technology to significantly reduce the system burden induced by search algorithms, facilitating intelligent cruise control in high-speed, intricate settings. [PERSON] et al. [7] harnessed the Geographic Information System (GIS) to dissect pre-acquired global path data and to embed road feature details within these segmented paths. This approach empowered autonomous vehicles to execute precise lane-change decisions in alignment with GIS information. Meanwhile, [PERSON] et al. [8] devised a framework that synergizes varied sensor data with GIS maps, segmenting dynamic entities within the roadway. This led to the accurate pinpointing of such dynamic objects. [PERSON] et al. [9] incorporated a GIS module to create reference lines within the vehicle's local coordinate system and amalgamated this with lane detection data. Throughout the vehicle's movement, they dynamically realigned the GIS reference lines to ensure the vehicle remained centered within its lane. [PERSON] et al. [10] harnessed pre-existing GIS data on the roadway environment, employing a blend of geometric matching algorithms and the A* algorithm. This approach realized optimal path planning while accounting for task-point constraints. [PERSON] et al. [11] amalgamated traffic lane data, Geographic Information Systems (GIS), and obstacle intelligence to craft a planning map. Subsequently, the best route was chosen from an array of parallel lines generated by the GIS and road networks. [PERSON] et al. [12] implemented a perception algorithm that integrates a three-line LiDAR system with a camera. Leveraging multi-hypothesis tracking data association alongside Kalman filtering methods, they achieved consistent tracking of obstacles across multiple frames. This enabled real-time, dynamic perception of obstacles for autonomous vehicles operating in dynamic outdoor settings. [PERSON] et al. [13], concentrating on the real-time dynamic perception of the surrounding environment using onboard sensors, capitalized on the vehicle's embedded computing capabilities for astute decision-making. They established a feedback mechanism for control decisions, facilitated by information exchange between vehicles. [PERSON] et al. [14] implemented a graph-based structural matching technique, merging data from both roadside infrastructure and vehicle LiDAR.