InVDriver: Intra-instance aware vectorized query-based autonomous driving transformer
End-to-end autonomous driving, with its holistic optimization capabilities, has gained increasing traction in academia and industry. Vectorized representations, which preserve instance-level topological information while reducing computational overhead, have emerged as promising paradigms. However,...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2025-06-01
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| Series: | Journal of Intelligent and Connected Vehicles |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/JICV.2025.9210060 |
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| _version_ | 1849393985118797824 |
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| author | Bo Zhang Heye Huang Chunyang Liu Yaqin Zhang Zhenhua Xu |
| author_facet | Bo Zhang Heye Huang Chunyang Liu Yaqin Zhang Zhenhua Xu |
| author_sort | Bo Zhang |
| collection | DOAJ |
| description | End-to-end autonomous driving, with its holistic optimization capabilities, has gained increasing traction in academia and industry. Vectorized representations, which preserve instance-level topological information while reducing computational overhead, have emerged as promising paradigms. However, existing vectorized query-based frameworks often overlook the inherent spatial correlations among intra-instance points, resulting in geometrically inconsistent outputs (e.g., fragmented HD map elements or oscillatory trajectories). To address these limitations, we propose intra-instance vectorized driving transformer (InVDriver), a novel vectorized query-based system that systematically models intra-instance spatial dependencies through masked self-attention layers, thereby enhancing planning accuracy and trajectory smoothness. Across all core modules, i.e., perception, prediction, and planning, InVDriver incorporates masked self-attention mechanisms that restrict attention to intra-instance point interactions, enabling coordinated refinement of structural elements while suppressing irrelevant inter-instance noise. The experimental results on the nuScenes benchmark demonstrate that InVDriver achieves state-of-the-art performance, surpassing prior methods in both accuracy and safety, while maintaining high computational efficiency. |
| format | Article |
| id | doaj-art-00907a95763e4a86b0e3d53bffa57cd0 |
| institution | Kabale University |
| issn | 2399-9802 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Intelligent and Connected Vehicles |
| spelling | doaj-art-00907a95763e4a86b0e3d53bffa57cd02025-08-20T03:40:14ZengTsinghua University PressJournal of Intelligent and Connected Vehicles2399-98022025-06-0182921006010.26599/JICV.2025.9210060InVDriver: Intra-instance aware vectorized query-based autonomous driving transformerBo Zhang0Heye Huang1Chunyang Liu2Yaqin Zhang3Zhenhua Xu4Institute for AI Industry Research (AIR), Tsinghua University, Beijing 100084, ChinaUniversity of Wisconsin‒Madison, Madison 53707, USADiDi, Beijing 100081, ChinaInstitute for AI Industry Research (AIR), Tsinghua University, Beijing 100084, ChinaSchool of Vehicle and Mobility, Tsinghua University, Beijing 100084, ChinaEnd-to-end autonomous driving, with its holistic optimization capabilities, has gained increasing traction in academia and industry. Vectorized representations, which preserve instance-level topological information while reducing computational overhead, have emerged as promising paradigms. However, existing vectorized query-based frameworks often overlook the inherent spatial correlations among intra-instance points, resulting in geometrically inconsistent outputs (e.g., fragmented HD map elements or oscillatory trajectories). To address these limitations, we propose intra-instance vectorized driving transformer (InVDriver), a novel vectorized query-based system that systematically models intra-instance spatial dependencies through masked self-attention layers, thereby enhancing planning accuracy and trajectory smoothness. Across all core modules, i.e., perception, prediction, and planning, InVDriver incorporates masked self-attention mechanisms that restrict attention to intra-instance point interactions, enabling coordinated refinement of structural elements while suppressing irrelevant inter-instance noise. The experimental results on the nuScenes benchmark demonstrate that InVDriver achieves state-of-the-art performance, surpassing prior methods in both accuracy and safety, while maintaining high computational efficiency.https://www.sciopen.com/article/10.26599/JICV.2025.9210060end-to-end autonomous drivingdecision makingmotion predictiontransformerartificial intelligence |
| spellingShingle | Bo Zhang Heye Huang Chunyang Liu Yaqin Zhang Zhenhua Xu InVDriver: Intra-instance aware vectorized query-based autonomous driving transformer Journal of Intelligent and Connected Vehicles end-to-end autonomous driving decision making motion prediction transformer artificial intelligence |
| title | InVDriver: Intra-instance aware vectorized query-based autonomous driving transformer |
| title_full | InVDriver: Intra-instance aware vectorized query-based autonomous driving transformer |
| title_fullStr | InVDriver: Intra-instance aware vectorized query-based autonomous driving transformer |
| title_full_unstemmed | InVDriver: Intra-instance aware vectorized query-based autonomous driving transformer |
| title_short | InVDriver: Intra-instance aware vectorized query-based autonomous driving transformer |
| title_sort | invdriver intra instance aware vectorized query based autonomous driving transformer |
| topic | end-to-end autonomous driving decision making motion prediction transformer artificial intelligence |
| url | https://www.sciopen.com/article/10.26599/JICV.2025.9210060 |
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