Driver2Map: Imitating Human Driving for Online High-Definition Map Construction

2026-08-02

AI Summary

Existing approaches struggle to effectively align and fuse multimodal data—such as multi-view images, semantic maps, and satellite imagery—limiting the accuracy and robustness of online high-definition (HD) map construction. Inspired by human driving cognition, this work proposes Driver2Map, the first model to enable collaborative mapping across these three modalities. It employs a two-stage alignment strategy to mitigate cross-modal spatial misalignment, introduces a pose-guided bird’s-eye-view (BEV) fusion module to handle dynamic occlusions, and incorporates a map refinement mechanism leveraging pre-trained priors to enhance structural and textural consistency. Experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art techniques in terms of Intersection over Union (IoU) and Average Precision (AP), achieving more accurate and robust online HD map generation.

Abstract

High-definition (HD) maps are essential for autonomous driving systems. In constructing such maps, onboard multi-view camera images, standard-definition maps and satellite images provide crucial information. However, due to the modality and perspective differences among these data sources, existing methods often struggle to effectively align and fuse them, making online HD map construction still challenging. To address these issues, we propose Driver2Map, an online HD map construction model inspired by human drivers. Unlike existing HD map construction models that utilize only two modalities, our Driver2Map can simultaneously exploit three modalities. Specifically, we propose a "two-stage alignment" strategy to reduce spatial misalignment across different modalities. Additionally, we introduce "Pose-Guided BEV Fusion", a BEV (bird's-eye-view) generation module that leverages camera pose information to adaptively weight multi-view features, thereby effectively suppressing cross-view feature overlap during BEV generation. Also, we design a "Pretrained Prior for Map Refinement" module to refine the initial prediction by learning map structure priors, thus improving the HD map prediction under dynamic occlusions. Extensive experiments demonstrate that Driver2Map outperforms existing methods on both IoU and AP metrics.

Problem

• HD map construction
• multi-modality fusion
• spatial alignment
• online mapping
• autonomous driving

Innovation

• multi-modality fusion
• two-stage alignment
• pose-guided BEV fusion
• map prior refinement
• online HD map construction