# MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning

Source: [MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning](<https://www.sota2.com/research/paper/68fa797f1e209131cea7dd2f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:55:51.510Z

Authors: Leonid Ivanov, Vasily Yuryev, Dmitry Yudin

Published: 2025-06-18T09:42:30.000Z

## Abstract

In autonomous driving, high-definition (HD) maps and semantic maps in bird's-eye view (BEV) are essential for accurate localization, planning, and decision-making. This paper introduces an enhanced End-to-End model named MapFM for online vectorized HD map generation. We show significantly boost feature representation quality by incorporating powerful foundation model for encoding camera images. To further enrich the model's understanding of the environment and improve prediction quality, we integrate auxiliary prediction heads for semantic segmentation in the BEV representation. This multi-task learning approach provides richer contextual supervision, leading to a more comprehensive scene representation and ultimately resulting in higher accuracy and improved quality of the predicted vectorized HD maps. The source code is available at https://github.com/LIvanoff/MapFM.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2506.15313>)

[PDF](<https://arxiv.org/pdf/2506.15313.pdf>)