Producing and Leveraging Online Map Uncertainty in Trajectory Prediction
Xunjiang Gu Guanyu Song Igor Gilitschenski Marco Pavone Boris Ivanovic
High-definition (HD) maps have played an integral role in the development of
modern autonomous vehicle (AV) stacks, albeit with high associated labeling and
maintenance costs. As a result, many recent works have proposed methods for
estimating HD maps online from sensor data, enabling AVs to operate outside of
previously-mapped regions. However, current online map estimation approaches
are developed in isolation of their downstream tasks, complicating their
integration in AV stacks. In particular, they do not produce uncertainty or
confidence estimates. In this work, we extend multiple state-of-the-art online
map estimation methods to additionally estimate uncertainty and show how this
enables more tightly integrating online mapping with trajectory forecasting. In
doing so, we find that incorporating uncertainty yields up to 50% faster
training convergence and up to 15% better prediction performance on the
real-world nuScenes driving dataset.