Core Insights - The article introduces MambaMap, a novel framework for online vector high-definition map construction based on state space models, which is crucial for autonomous driving as it provides precise road information for downstream tasks [4][5]. Summary by Sections Key Contributions - MambaMap framework efficiently integrates long-range temporal information for online vector high-definition map construction using state space models [5]. - An effective gating mechanism is introduced in the state space for efficient information selection and integration at both BEV feature and instance query levels, along with various scanning strategies to leverage spatiotemporal dependencies [5]. - Extensive experiments on nuScenes and Argoverse2 datasets demonstrate that MambaMap outperforms state-of-the-art methods across various settings [5]. Experimental Results - In the nuScenes dataset, MambaMap achieved an average precision (mAP) of 40.1, outperforming other methods like StreamMapNet and SQD-MapNet [12]. - For the Argoverse2 dataset, MambaMap also showed superior performance with a mAP of 61.0, indicating its robustness and generalization capabilities [12]. - The article presents detailed performance metrics across different methods and datasets, highlighting MambaMap's advantages in various scenarios [11][12]. Methodology - MambaMap utilizes a dynamic memory mechanism and a gating state space model to efficiently fuse BEV features and instance-level features over multiple time steps, capturing long-range dependencies with minimal computational overhead [18]. - The introduction of multi-directional and spatiotemporal scanning strategies enhances feature extraction capabilities and temporal consistency [18]. Future Directions - Future work aims to extend MambaMap to address other BEV perception tasks, such as 3D object detection and motion prediction, thereby broadening its applicability in robotics [18].