OSM vs HD Maps: Map Representations for Trajectory Prediction
This work addresses the accessibility and adaptability issues of HD Maps for autonomous driving, offering a scalable solution, though it is incremental in improving map representations.
The paper tackles the problem of long-term motion forecasting for autonomous driving by proposing OpenStreetMap (OSM) as an alternative to HD Maps, achieving competitive performance and doubling the forecasting horizon compared to previous studies.
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.