MapDiffusion: Generative Diffusion for Vectorized Online HD Map Construction and Uncertainty Estimation in Autonomous Driving

Autonomous driving requires an understanding of the static environment from sensor data. Learned Bird’s-Eye View (BEV) encoders are commonly used to fuse multiple inputs, and a vector decoder predicts a vectorized map representation from the latent BEV grid. However, traditional map construction models provide deterministic point estimates, failing to capture uncertainty and the inherent ambiguities of real-world environments, such as occlusions and missing lane markings. We propose MapDiffusion, a novel generative approach that leverages the diffusion paradigm to learn the full distribution of possible vectorized maps. Instead of predicting a single deterministic output from learned queries, MapDiffusion iteratively refines randomly initialized queries, conditioned on a BEV latent grid, to generate multiple plausible map samples. This allows aggregating samples to improve prediction accuracy and deriving uncertainty estimates that directly correlate with scene ambiguity. Extensive experiments on the nuScenes dataset demonstrate that MapDiffusion achieves state-of-the-art performance in online map construction, surpassing the baseline by 5% in single-sample performance. We further show that aggregating multiple samples consistently improves performance along the ROC curve, validating the benefit of distribution modeling. Additionally, our uncertainty estimates are significantly higher in occluded areas, reinforcing their value in identifying regions with ambiguous sensor input. By modeling the full map distribution, MapDiffusion enhances the robustness and reliability of online vectorized HD map construction, enabling uncertainty-aware decision-making for autonomous vehicles in complex environments.

Authors

• Thomas Monninger
• Zihan Zhang
• Zhipeng Mo
• Md Zafar Anwar
• Steffen Staab
• Sihao Ding 0004

Keywords

• Uncertainty
• Accuracy
• Fuses
• Noise reduction
• Robustness
• Vectors
• Decoding
• Noise measurement
• Autonomous vehicles
• Intelligent robots
• Uncertainty Estimation
• Map Construction
• Uncertainty Map
• Receiver Operating Characteristic Curve
• Sensor Data
• Vector Representation
• Bird’s Eye
• Iterative Refinement
• Map Representation
• Multiple Mapping
• Web Map
• Vector Map
• Denoising
• Sample Variance
• Number Of Steps
• Gaussian Noise
• Diffusion Process
• Random Noise
• Raster Map
• Areas Of Uncertainty
• Polyline
• Frames Per Second
• Temporal Aggregation
• Diffusion Model
• Map Elements
• Prediction Map
• Reference Architecture
• Previous Frame