Producing and Leveraging Online Map Uncertainty in Trajectory Prediction
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913281720975360 |
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| author | Gu, Xunjiang Song, Guanyu Gilitschenski, Igor Pavone, Marco Ivanovic, Boris |
| author_facet | Gu, Xunjiang Song, Guanyu Gilitschenski, Igor Pavone, Marco Ivanovic, Boris |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16439 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Producing and Leveraging Online Map Uncertainty in Trajectory Prediction Gu, Xunjiang Song, Guanyu Gilitschenski, Igor Pavone, Marco Ivanovic, Boris Robotics Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Producing and Leveraging Online Map Uncertainty in Trajectory Prediction |
| topic | Robotics Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.16439 |