OSM vs HD Maps: Map Representations for Trajectory Prediction

High Definition (HD) Maps have long been favored for their precise depictions of static road elements. However, their accessibility constraints and vulnerability to rapid en- vironmental changes impede the widespread deployment of highly map-reliant autonomous driving tasks, such as motion forecasting. In this context, we propose to leverage Open- StreetMap (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 observation landscape 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 conduct- ing class-aware comparisons. This research not only advances long-term motion forecasting with coarse map representations but additionally offers a scalable solution within the domain of autonomous driving.