InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer

• URL: http://arxiv.org/abs/2502.17949v1
• Date: Tue, 25 Feb 2025 08:20:16 GMT
• Title: InVDriver: Intra-Instance Aware Vectorized Query-Based Autonomous Driving Transformer
• Authors: Bo Zhang, Heye Huang, Chunyang Liu, Yaqin Zhang, Zhenhua Xu,
• Abstract summary: InVDriver is a novel vectorized query-based system for intra-instance spatial dependencies.<n>We show that InVDriver achieves state-of-the-art performance, surpassing prior methods in both accuracy and safety.
• Score: 12.441180142943328
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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 prior methods in both accuracy and safety, while maintaining high computational efficiency. Our work validates that explicit modeling of intra-instance geometric coherence is critical for advancing vectorized autonomous driving systems, bridging the gap between theoretical advantages of end-to-end frameworks and practical deployment requirements.

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