让自动驾驶地图超越车辆视野，预测前方未感知路段。
Generation of Vectorized Maps Beyond Vehicle View
- 基于车载传感器感知区域，生成合理延伸的矢量地图。
- 首次构建专用数据集，验证了学习方法在多场景下的有效性。
- 适合自动驾驶地图更新与规划研究者参考。
自动驾驶依赖高精（HD）地图实现安全导航。传统HD地图构建成本高，且更新受限，难以规模化。近期工作尝试从车载传感器在线构建矢量地图，但传感器视域有限，前方可重建地图范围不足，影响安全规划。本文提出全新的「超视域矢量地图生成」问题：给定车辆感知区域的矢量地图（在视内），生成合理的地图延续。为评估可行性，我们提出BeyondFormer，据我们所知是首个专为此任务设计的方法。由于任务新颖，我们构建了首个专门用于该任务的数据集并进行评估。结果表明，该方法在多种场景下表现稳定，证明了基于学习的方法在自动驾驶地图预测中的潜力。除验证可行性外，还深入讨论了方法局限，并指明未来扩展至复杂驾驶环境的关键方向。代码已开源。
原文摘要 · Abstract (English)
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.
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