AMap: Distilling Future Priors for Ahead-Aware Online HD Map Construction
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| _version_ | 1866918258978848768 |
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| author | Li, Ruikai Li, Xinrun Xie, Mengwei Shan, Hao Qiu, Shoumeng Chang, Xinyuan Fan, Yizhe Xiong, Feng Jiang, Han Ren, Yilong Yu, Haiyang Xu, Mu Long, Yang Ojha, Varun Cui, Zhiyong |
| author_facet | Li, Ruikai Li, Xinrun Xie, Mengwei Shan, Hao Qiu, Shoumeng Chang, Xinyuan Fan, Yizhe Xiong, Feng Jiang, Han Ren, Yilong Yu, Haiyang Xu, Mu Long, Yang Ojha, Varun Cui, Zhiyong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19150 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AMap: Distilling Future Priors for Ahead-Aware Online HD Map Construction Li, Ruikai Li, Xinrun Xie, Mengwei Shan, Hao Qiu, Shoumeng Chang, Xinyuan Fan, Yizhe Xiong, Feng Jiang, Han Ren, Yilong Yu, Haiyang Xu, Mu Long, Yang Ojha, Varun Cui, Zhiyong Computer Vision and Pattern Recognition 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. |
| title | AMap: Distilling Future Priors for Ahead-Aware Online HD Map Construction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.19150 |