自动驾驶超视距VLA如何实现？小鹏NavigScene另辟蹊径！

Core Viewpoint - The article discusses the limitations of current autonomous driving systems in bridging the gap between local perception and global navigation, highlighting the introduction of NavigScene as a solution to enhance navigation capabilities in autonomous vehicles [3][4]. Group 1: Research and Development - Autonomous driving systems have made significant progress in local visual information processing, but they struggle to integrate broader navigation context used by human drivers [4][9]. - NavigScene is introduced as a navigation-guided natural language dataset that simulates a human-like driving environment within autonomous systems [5][9]. - The development of three complementary paradigms utilizing NavigScene aims to improve reasoning, preference optimization, and the integration of visual-language-action models [5][9]. Group 2: Methodologies - Navigation-guided reasoning enhances visual language models by incorporating navigation context into prompting methods [5]. - Navigation-guided preference optimization is a reinforcement learning approach that improves visual language model responses by establishing preference relationships based on navigation-related information [5]. - The navigation-guided vision-language-action model integrates navigation guidance and visual language models with traditional end-to-end driving models through feature fusion [5]. Group 3: Event and Engagement - A live session is scheduled to discuss the advancements and methodologies related to NavigScene, emphasizing its role in overcoming the limitations of current autonomous driving systems [4][9].