Producing and Leveraging Online Map Uncertainty in Trajectory Prediction

Xunjiang Gu, Guanyu Song, Igor Gilitschenski, Marco Pavone, Boris Ivanovic; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 14521-14530

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.