Can I read InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer on EtoBox?

InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer by Zhang, Bo; Huang, Heye; Liu, Chunyang; Zhang, Yaqin; Xu, Zhenhua is a scholarly article available to read on EtoBox.

What is InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer about?

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 a promising paradigm. While 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 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. Experimental results on the nuScenes benchmark demonstrate that InVDriver achieves state-of-the-art performance, surpassing

Author

Zhang, Bo; Huang, Heye; Liu, Chunyang; Zhang, Yaqin; Xu, Zhenhua

Published

2025

Language

EN