The conventional Simultaneous Localization and Mapping (SLAM) algorithm assumes a static world, which is easily influenced by dynamic elements of the surrounding environment. For high-precision localization in dynamic scenes, a dynamic SLAM algorithm combining instance segmentation and dynamic feature point filtering is proposed to address this issue. Initially, YOLACT-dyna, a one-stage instance segmentation network, was developed in order to perform instance segmentation on the input image, eliminate potential moving objects in the scene, and estimate the camera pose roughly. Second, based on the camera pose and polar constraint, the motion probability of each possible moving object was computed. Finally, the moving feature points were filtered out, and the static feature points were used to calculate the pose. The experimental results reveal that this algorithm’s recall rate in the dynamic regional KITTI dataset was 94.5% in public datasets. Accuracy is enhanced in environments with dynamic object location. At the same time, it can guarantee the positioning accuracy of a static scene, effectively enhancing the visual SLAM system’s position precision and robustness in a dynamic environment. It can meet the requirements of the automatic driving system’s real-time operation.

As autonomous vehicles increasingly populate roads, robust testing is essential to ensure their safety and reliability. Due to the limitation that traditional testing methodologies (real-world and simulation testing) are difficult to cover a wide range of scenarios and ensure repeatability, this study proposes a novel virtual-real fusion testing approach that integrates Graph Theory and Artificial Potential Fields (APF) in virtual-real fusion autonomous vehicle testing. Conducted using SUMO software, our strategic lane change and speed adjustment simulation experiments demonstrate that our approach can efficiently handle vehicle dynamics and environmental interactions compared to traditional Rapidly-exploring Random Tree (RRT) methods. The proposed method shows a significant reduction in maneuver completion times—up to 41% faster in simulations and 55% faster in real-world tests. Field experiments at the Vehicle-Road-Cloud Integrated Platform in Suzhou High-Speed Railway New Town confirmed the method’s practical viability and robustness under real traffic conditions. The results indicate that our integrated approach enhances the authenticity and efficiency of testing, thereby advancing the development of dependable, autonomous driving systems. This research not only contributes to the theoretical framework but also has practical implications for improving autonomous vehicle testing processes.