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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2399-9802 |