Core Viewpoint - The article discusses the capabilities and performance of the VLA (Vehicle Language Architecture) in autonomous driving, particularly in comparison to E2E (End-to-End) models combined with VLM (Vision Language Model) [1][2]. Group 1: Performance Comparison - VLA demonstrates superior defensive driving capabilities, particularly in scenarios with obstructed views, showing smooth deceleration based on remaining distance, unlike E2E models which struggle with such nuanced control [2][3]. - In congested traffic situations, VLA exhibits advanced decision-making by choosing to change lanes after assessing the environment, whereas E2E models typically resort to rerouting logic [2][3]. - VLA's trajectory generation is more stable and less prone to deviations, as it understands non-standard lane widths and adjusts driving strategies accordingly, significantly reducing the "snake-like" driving behavior seen in E2E models [3][4]. Group 2: Technical Insights - The VLA system integrates a large language model (LLM) for enhanced scene understanding, which allows for better decision-making in complex driving environments [2][4]. - The system is not fully autonomous but serves as an advanced driver assistance system, requiring human intervention when necessary [5][6]. - VLA's architecture allows for faster iterations and optimizations across different driving scenarios, improving overall performance compared to traditional E2E models [5][6]. Group 3: Limitations and Considerations - There are still scenarios where VLA may misinterpret traffic signals, indicating areas for improvement in its decision-making algorithms [5][6]. - The system's capabilities differ significantly from E2E models, necessitating driver readiness to take control when required [5][6].