ControlMap: Controllable High-Definition Map Generation for Traffic Scenario Simulation

A new data-driven pipeline called ControlMap aims to lower the cost of validating autonomous vehicles by generating controllable high-definition maps for traffic simulation, according to a paper submitted in June 2026 [1]. The model uses latent diffusion and spatial conditioning to produce realistic road topologies on demand. High-definition maps are critical for autonomous driving simulation, but their creation remains expensive and labor-intensive, requiring extensive data collection and manual processing [1][2]. The ControlMap framework addresses this bottleneck by injecting spatial guidance signals directly into a diffusion model, a technique the authors describe as the first of its kind for HD map synthesis [1][2]. The system is built on ControlNet, which provides the spatial conditioning, and supports adjustable conditioning strength through classifier-free guidance [1][2]. It also enables city-level style transfer, allowing generated maps to preserve region-specific road characteristics by conditioning on city labels [1][2]. The broader autonomous vehicle industry has long grappled with the mapping challenge. As of 2026, no system has achieved full autonomy across all driving domains, and the advanced software and mapping required to operate safely under diverse conditions remains a primary obstacle [3]. Simulation environments depend on varied, realistic maps to expose driving systems to edge cases, yet the high cost of producing HD maps has constrained scenario diversity [1][2]. ControlMap’s approach could allow developers to generate targeted road topologies without commissioning new survey data. To evaluate output quality, the researchers introduce two novel metrics that measure adherence to the input control signal and similarity to ground-truth maps [1][2]. Experiments reported in the paper show that the model generates maps that faithfully follow specified road layouts while preserving city-specific details [1][2]. The paper was posted on arXiv, the open-access repository that hosts more than two million e-prints and receives roughly 24,000 submissions per month as of late 2024 [11]. The work appears under the Robotics category and is accompanied by links to code and data repositories, including Hugging Face [1]. arXiv Labs, the framework that enables community-contributed tools on article pages, lists several integrations for the paper, including Bibliographic Explorer, Connected Papers, and the CORE Recommender [1][9][10]. These tools allow readers to navigate citation trees and discover related open-access research [9][10]. The Labs initiative, launched in 2020, sets guidelines for third-party collaborations and requires partners to adhere to arXiv’s values of openness, community, excellence, and user data privacy [9].