AMap: Distilling Future Priors for Ahead-Aware Online HD Map Construction

2025-12-22

AI Summary

Existing online high-definition (HD) map construction methods rely on historical frame fusion—essentially “spatial hindsight”—and thus struggle to improve perception accuracy in unexplored forward regions; this limitation directly compromises downstream motion planning safety. To address this, we propose a novel “future distillation” paradigm: a teacher model with access to future frames guides a lightweight student model that operates solely on the current frame, implicitly endowing it with zero-overhead forward-looking capability. Our approach introduces multi-level bird’s-eye-view (BEV) knowledge distillation, spatial mask guidance, and an asymmetric query adaptation module. Evaluated on nuScenes and Argoverse 2, our method significantly enhances mapping accuracy in critical forward regions of the current frame, outperforming state-of-the-art temporal models while maintaining single-frame real-time inference efficiency.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Computer Vision: Diffusion Models for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Online High-Definition (HD) map construction is pivotal for autonomous driving. While recent approaches leverage historical temporal fusion to improve performance, we identify a critical safety flaw in this paradigm: it is inherently ``spatially backward-looking." These methods predominantly enhance map reconstruction in traversed areas, offering minimal improvement for the unseen road ahead. Crucially, our analysis of downstream planning tasks reveals a severe asymmetry: while rearward perception errors are often tolerable, inaccuracies in the forward region directly precipitate hazardous driving maneuvers. To bridge this safety gap, we propose AMap, a novel framework for Ahead-aware online HD Mapping. We pioneer a ``distill-from-future" paradigm, where a teacher model with privileged access to future temporal contexts guides a lightweight student model restricted to the current frame. This process implicitly compresses prospective knowledge into the student model, endowing it with ``look-ahead" capabilities at zero inference-time cost. Technically, we introduce a Multi-Level BEV Distillation strategy with spatial masking and an Asymmetric Query Adaptation module to effectively transfer future-aware representations to the student's static queries. Extensive experiments on the nuScenes and Argoverse 2 benchmark demonstrate that AMap significantly enhances current-frame perception. Most notably, it outperforms state-of-the-art temporal models in critical forward regions while maintaining the efficiency of single current frame inference.

Problem

• Addresses safety flaw in backward-looking HD map construction
• Proposes distillation method for forward-aware map prediction
• Enhances autonomous driving safety without inference cost increase

Innovation

• Distill future priors into current-frame model
• Multi-level BEV distillation with spatial masking
• Asymmetric query adaptation for knowledge transfer