OSM vs HD Maps: Map Representations for Trajectory Prediction
Jing-Yan Liao Parth Doshi Zihan Zhang David Paz Henrik Christensen
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.