@article{Zhang2025, 
author = {Bo Zhang and Heye Huang and Chunyang Liu and Yaqin Zhang and Zhenhua Xu},
title = {InVDriver: Intra-instance aware vectorized query-based autonomous driving transformer},
year = {2025},
journal = {Journal of Intelligent and Connected Vehicles},
volume = {8},
number = {2},
pages = {9210060},
keywords = {end-to-end autonomous driving, decision making, motion prediction, transformer, artificial intelligence},
url = {https://www.sciopen.com/article/10.26599/JICV.2025.9210060},
doi = {10.26599/JICV.2025.9210060},
abstract = {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.}
}