While High Definition (HD) Maps have long been favored for their precise depictions of static road elements, their accessibility constraints and susceptibility to rapid environmental changes impede the widespread deployment of autonomous driving, especially in the motion forecasting task. In this context, we propose to leverage OpenStreetMap (OSM) as a promising alternative to HD Maps for long-term motion forecasting. The contributions of this work are threefold: firstly, we extend the application of OSM to long-horizon forecasting, doubling the forecasting horizon compared to previous studies. Secondly, through an expanded receptive field and the integration of intersection priors, our OSM-based approach exhibits competitive performance, narrowing the gap with HD Map-based models. Lastly, we conduct an exhaustive context-aware analysis, providing deeper insights in motion forecasting across diverse scenarios as well as conducting class-aware comparisons. This research not only advances long-term motion forecasting with coarse map representations but additionally offers a potential scalable solution within the domain of autonomous driving.

翻译：尽管高精地图长期以来以其对静态道路元素的精确描绘而备受青睐，但其可访问性受限以及对环境快速变化的敏感性，阻碍了自动驾驶的广泛部署，尤其是在运动预测任务中。在此背景下，我们提出利用OpenStreetMap作为高精地图在长期运动预测中的有力替代方案。本文的贡献包含三个方面：首先，我们将OSM的应用扩展至长时域预测，将预测时域较先前研究翻倍；其次，通过扩大感受野并整合路口先验信息，我们的OSM方法展现出具有竞争力的性能，缩小了与基于高精地图模型的差距；最后，我们开展了全面的上下文感知分析，为不同场景下的运动预测提供了更深入的洞见，并进行了类别感知比较。本研究不仅通过粗粒度地图表示推进了长期运动预测，还为此提供了自动驾驶领域内潜在的可扩展解决方案。