DA-Nav: Direction-Aware City-Scale Vision-Language Navigation

• URL: http://arxiv.org/abs/2607.11638v2
• Date: Tue, 14 Jul 2026 03:00:36 GMT
• Title: DA-Nav: Direction-Aware City-Scale Vision-Language Navigation
• Abstract summary: We introduce a novel paradigm for leveraging directional instructions from commercial navigation tools.<n>We propose DA-Nav, a Direction-Aware vision-language Navigation framework that reformulates navigation as a discrete spatial grounding problem.<n>Experiments in CARLA demonstrate that DA-Nav achieves a high success rate of 56.16% in unseen urban environments.
• Score: 39.38337824377708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: City-scale outdoor navigation is currently hindered by the heavy reliance on dense maps or costly navigation supervision. In this work, we introduce a novel paradigm for leveraging directional instructions from commercial navigation tools (e.g., Google Maps). To bridge the gap between commercial instructions and executable navigation actions, while mitigating long-horizon error accumulation through robust trajectory recovery, we propose DA-Nav, a Direction-Aware vision-language Navigation framework that reformulates navigation as a discrete spatial grounding problem on the egocentric 2D image plane. To achieve trajectory recovery, DA-Nav employs a Chain-of-Thought (CoT) reasoning process encompassing deviation assessment, action prediction, and target grid selection. We further introduce ReDA, a dataset that provides direction-aware instructions and recovery trajectories to enhance spatial grounding and support CoT recovery reasoning. Extensive experiments in CARLA demonstrate that DA-Nav achieves a high success rate of 56.16% in unseen urban environments, outperforming existing State-of-The-Art (SoTA) methods while maintaining a substantially stronger recovery capability. Furthermore, without fine-tuning, DA-Nav seamlessly adapts to both quadruped and humanoid robots, enabling stable kilometer-scale closed-loop outdoor navigation in complex real world environments.

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