NavigScene: Bridging Local Perception and Global Navigation for Beyond-Visual-Range Autonomous Driving

TL;DR

NavigScene introduces a novel dataset and three paradigms to integrate global navigation context into autonomous driving systems, significantly enhancing their reasoning, perception, and planning capabilities beyond local visual information.

Contribution

The paper presents NavigScene, a new dataset and three innovative methods to incorporate global navigation context into autonomous driving models, bridging the gap between local perception and global navigation.

Findings

Improved perception, prediction, and planning performance.

Enhanced reasoning beyond visual range.

Better generalization to diverse driving scenarios.

Abstract

Autonomous driving systems have made significant advances in Q&A, perception, prediction, and planning based on local visual information, yet they struggle to incorporate broader navigational context that human drivers routinely utilize. We address this critical gap between local sensor data and global navigation information by proposing NavigScene, an auxiliary navigation-guided natural language dataset that simulates a human-like driving environment within autonomous driving systems. Moreover, we develop three complementary paradigms to leverage NavigScene: (1) Navigation-guided Reasoning, which enhances vision-language models by incorporating navigation context into the prompting approach; (2) Navigation-guided Preference Optimization, a reinforcement learning method that extends Direct Preference Optimization to improve vision-language model responses by establishing preferences for navigation-relevant summarized information; and (3) Navigation-guided Vision-Language-Action model, which integrates navigation guidance and vision-language models with conventional driving models through feature fusion. Extensive experiments demonstrate that our approaches significantly improve performance across perception, prediction, planning, and question-answering tasks by enabling reasoning capabilities beyond visual range and improving generalization to diverse driving scenarios. This work represents a significant step toward more comprehensive autonomous driving systems capable of navigating complex, unfamiliar environments with greater reliability and safety.