Core Viewpoint - The article discusses the advancements and challenges in autonomous driving systems, particularly in unstructured environments, and introduces the Impromptu VLA framework developed by Tsinghua AIR and Bosch Research Institute to address data gaps in these scenarios [1]. Group 1: Advancements in Autonomous Driving - Current autonomous driving systems have made significant progress in structured environments like cities and highways, but face challenges in unstructured scenarios such as rural roads and construction zones [1]. - Existing large-scale autonomous driving datasets primarily focus on conventional traffic conditions, leading to a lack of specialized, large-scale, and finely annotated data for complex unstructured environments [1]. Group 2: Impromptu VLA Framework - The Impromptu VLA framework aims to provide an open-weight and open-data driving vision-language-action model, which is a fully end-to-end system that extracts multimodal features directly from driving video segments [1]. - Impromptu VLA generates driving commands in natural language format without the need for manually designed perception modules or intermediate representations [1]. - In the NeuroNCAP closed-loop safety evaluation system, Impromptu VLA demonstrates strong decision robustness and generalization capabilities, significantly outperforming the latest BridgeAD system proposed at CVPR 2025 (2.15 vs. 1.60) [1].

Core Insights - The article discusses the rapid advancements in end-to-end autonomous driving, particularly focusing on Vision-Language-Action (VLA) models and their applications in the industry [2][3]. Group 1: VLA Model Developments - The introduction of AutoVLA, a new VLA model that integrates reasoning and action generation for end-to-end autonomous driving, shows promising results in semantic reasoning and trajectory planning [3][4]. - ReCogDrive, another VLA model, addresses performance issues in rare and long-tail scenarios by utilizing a three-stage training framework that combines visual language models with diffusion planners [7][9]. - Impromptu VLA introduces a dataset aimed at improving VLA models' performance in unstructured extreme conditions, demonstrating significant performance improvements in established benchmarks [14][24]. Group 2: Experimental Results - AutoVLA achieved competitive performance metrics in various scenarios, with the best-of-N method reaching a PDMS score of 92.12, indicating its effectiveness in planning and execution [5]. - ReCogDrive set a new state-of-the-art PDMS score of 89.6 on the NAVSIM benchmark, showcasing its robustness and safety in driving trajectories [9][10]. - The OpenDriveVLA model demonstrated superior results in open-loop trajectory planning and driving-related question-answering tasks, outperforming previous methods on the nuScenes dataset [28][32]. Group 3: Industry Trends - The article highlights a trend among major automotive manufacturers, such as Li Auto, Xiaomi, and XPeng, to invest heavily in VLA model research and development, indicating a competitive landscape in autonomous driving technology [2][3]. - The integration of large language models (LLMs) with VLA frameworks is becoming a focal point for enhancing decision-making capabilities in autonomous vehicles, as seen in models like ORION and VLM-RL [33][39].