Delving into Mapping Uncertainty for Mapless Trajectory Prediction
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918103613440000 |
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| author | Zhang, Zongzheng Qiu, Xuchong Zhang, Boran Zheng, Guantian Gu, Xunjiang Chi, Guoxuan Gao, Huan-ang Wang, Leichen Liu, Ziming Li, Xinrun Gilitschenski, Igor Li, Hongyang Zhao, Hang Zhao, Hao |
| author_facet | Zhang, Zongzheng Qiu, Xuchong Zhang, Boran Zheng, Guantian Gu, Xunjiang Chi, Guoxuan Gao, Huan-ang Wang, Leichen Liu, Ziming Li, Xinrun Gilitschenski, Igor Li, Hongyang Zhao, Hang Zhao, Hao |
| contents | Recent advances in autonomous driving are moving towards mapless approaches, where High-Definition (HD) maps are generated online directly from sensor data, reducing the need for expensive labeling and maintenance. However, the reliability of these online-generated maps remains uncertain. While incorporating map uncertainty into downstream trajectory prediction tasks has shown potential for performance improvements, current strategies provide limited insights into the specific scenarios where this uncertainty is beneficial. In this work, we first analyze the driving scenarios in which mapping uncertainty has the greatest positive impact on trajectory prediction and identify a critical, previously overlooked factor: the agent's kinematic state. Building on these insights, we propose a novel Proprioceptive Scenario Gating that adaptively integrates map uncertainty into trajectory prediction based on forecasts of the ego vehicle's future kinematics. This lightweight, self-supervised approach enhances the synergy between online mapping and trajectory prediction, providing interpretability around where uncertainty is advantageous and outperforming previous integration methods. Additionally, we introduce a Covariance-based Map Uncertainty approach that better aligns with map geometry, further improving trajectory prediction. Extensive ablation studies confirm the effectiveness of our approach, achieving up to 23.6% improvement in mapless trajectory prediction performance over the state-of-the-art method using the real-world nuScenes driving dataset. Our code, data, and models are publicly available at https://github.com/Ethan-Zheng136/Map-Uncertainty-for-Trajectory-Prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18498 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Delving into Mapping Uncertainty for Mapless Trajectory Prediction Zhang, Zongzheng Qiu, Xuchong Zhang, Boran Zheng, Guantian Gu, Xunjiang Chi, Guoxuan Gao, Huan-ang Wang, Leichen Liu, Ziming Li, Xinrun Gilitschenski, Igor Li, Hongyang Zhao, Hang Zhao, Hao Computer Vision and Pattern Recognition Recent advances in autonomous driving are moving towards mapless approaches, where High-Definition (HD) maps are generated online directly from sensor data, reducing the need for expensive labeling and maintenance. However, the reliability of these online-generated maps remains uncertain. While incorporating map uncertainty into downstream trajectory prediction tasks has shown potential for performance improvements, current strategies provide limited insights into the specific scenarios where this uncertainty is beneficial. In this work, we first analyze the driving scenarios in which mapping uncertainty has the greatest positive impact on trajectory prediction and identify a critical, previously overlooked factor: the agent's kinematic state. Building on these insights, we propose a novel Proprioceptive Scenario Gating that adaptively integrates map uncertainty into trajectory prediction based on forecasts of the ego vehicle's future kinematics. This lightweight, self-supervised approach enhances the synergy between online mapping and trajectory prediction, providing interpretability around where uncertainty is advantageous and outperforming previous integration methods. Additionally, we introduce a Covariance-based Map Uncertainty approach that better aligns with map geometry, further improving trajectory prediction. Extensive ablation studies confirm the effectiveness of our approach, achieving up to 23.6% improvement in mapless trajectory prediction performance over the state-of-the-art method using the real-world nuScenes driving dataset. Our code, data, and models are publicly available at https://github.com/Ethan-Zheng136/Map-Uncertainty-for-Trajectory-Prediction. |
| title | Delving into Mapping Uncertainty for Mapless Trajectory Prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.18498 |