{"ID":2826153,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.19150","arxiv_id":"2512.19150","title":"AMap: Distilling Future Priors for Ahead-Aware Online HD Map Construction","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.","short_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...","url_abs":"https://arxiv.org/abs/2512.19150","url_pdf":"https://arxiv.org/pdf/2512.19150v1","authors":"[\"Ruikai Li\",\"Xinrun Li\",\"Mengwei Xie\",\"Hao Shan\",\"Shoumeng Qiu\",\"Xinyuan Chang\",\"Yizhe Fan\",\"Feng Xiong\",\"Han Jiang\",\"Yilong Ren\",\"Haiyang Yu\",\"Mu Xu\",\"Yang Long\",\"Varun Ojha\",\"Zhiyong Cui\"]","published":"2025-12-22T08:46:59Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}