发表机构
Centre for Automation and Robotics, CSIC-UPM; Aragon Institute for Engineering Research (I3A), University of Zaragoza（自动化与机器人中心（西班牙国家研究委员会-马德里理工大学联合机构）; 萨拉戈萨大学阿拉贡工程研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动驾驶中传感器视野有限导致地图重建范围不足的问题，提出超越视野的矢量化地图生成任务及方法BeyondFormer，并构建首个专用数据集，验证了基于学习的地图预测方法的可行性与一致性。
AI 中文摘要
自动驾驶依赖于高清（HD）地图来实现安全导航。传统高清地图的构建在硬件、数据和人力资源方面成本高昂，加之其更新受限，阻碍了其可扩展性。近期研究提出了利用车载传感器在线生成高清矢量化地图的替代方案。然而，传感器的视野有限，车辆前方重建地图的范围不足以支持安全规划。本文旨在解决这一局限，提出了新颖的超越视野的矢量化地图生成问题：给定车辆感知区域（视野内）的矢量化地图，生成合理的地图延续。为实验评估其可行性，我们提出了BeyondFormer，据我们所知，这是首个针对超越视野地图生成设计的工作。鉴于该问题的新颖性，我们生成了首个专门为此设计的数据集，并评估了所提方法。结果表明，该方法在多种场景下表现一致，确立了基于学习的方法作为自动驾驶中地图预测的一个有前景的方向。在证明任务可行性的同时，我们对方法的局限性进行了广泛讨论，并指出了将其扩展到更复杂驾驶条件的关键未来研究方向。代码可在以下网址获取：此https URL。
英文摘要
Autonomous driving relies on High Definition (HD) maps for safe navigation. Traditional HD maps construction is costly in hardware, data and human resources, which together with its update limitations hinders scalability. Recent works have proposed online alternatives for HD vectorized mapping from onboard sensors. However, sensor field of view is limited, and the range of the reconstructed maps ahead of the vehicle is insufficient for safe planning. This paper aims to address this limitation by proposing the novel beyond-view vectorized map generation problem: given vectorized maps of the area sensed by the vehicle (in-view), to generate plausible map continuations. To experimentally assess its feasibility, we propose BeyondFormer, which, to the best of out knowledge, is the first work designed towards beyond-view map generation. Given the novelty of the problem, we generate the first dataset specifically designed for it and evaluate the proposed approach. The results demonstrate consistent performance across diverse scenarios, establishing learning-based methods as a promising direction for map forecasting in autonomous driving. Beyond demonstrating the feasibility of the task, we provide an extensive discussion of the method's limitations and identify key future research directions for scaling it to more complex driving conditions. Code is available at https://git-autopia.car.upm-csic.es/beyondformer.
