Producing and Leveraging Online Map Uncertainty in Trajectory Prediction

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...

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Bibliographic Details
Published in2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 14521 - 14530
Main Authors Gu, Xunjiang, Song, Guanyu, Gilitschenski, Igor, Pavone, Marco, Ivanovic, Boris
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.06.2024
Subjects
Autonomous Driving
Behavior Prediction
Estimation
Measurement uncertainty
Online HD Map Estimation
Pattern recognition
Predictive models
Training
Trajectory
Uncertainty
Uncertainty Estimation
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Summary: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 1 1 Code: https://github.com/alfredgu001324/MapUncertaintyPrediction. 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.
ISSN:2575-7075
DOI:10.1109/CVPR52733.2024.01376