Core Viewpoint - The article discusses the development of the Impromptu VLA dataset, which aims to address the data scarcity issue in unstructured driving environments for autonomous driving systems. It highlights the dataset's potential to enhance the performance of vision-language-action models in complex scenarios [4][29]. Dataset Overview - The Impromptu VLA dataset consists of over 80,000 meticulously constructed video clips, extracted from more than 2 million original materials across eight diverse open-source datasets [5][29]. - The dataset focuses on four key unstructured challenges: boundary-ambiguous roads, temporary traffic rule changes, unconventional dynamic obstacles, and complex road conditions [12][13]. Methodology - The dataset construction involved a multi-step process, including data collection, scene classification, and multi-task annotation generation, utilizing advanced visual-language models (VLMs) for scene understanding [10][17]. - A rigorous manual verification process was implemented to ensure high-quality annotations, with significant F1 scores achieved for various categories, confirming the reliability of the VLM-based annotation process [18]. Experimental Validation - The effectiveness of the Impromptu VLA dataset was validated through comprehensive experiments, showing significant performance improvements in mainstream autonomous driving benchmarks. For instance, the average score in the closed-loop NeuroNCAP test improved from 1.77 to 2.15, with a reduction in collision rates from 72.5% to 65.5% [6][21]. - In open-loop trajectory prediction evaluations, models trained with the Impromptu VLA dataset achieved L2 errors as low as 0.30 meters, demonstrating competitive performance compared to leading methods that rely on larger proprietary datasets [24]. Conclusion - The Impromptu VLA dataset serves as a critical resource for developing more robust and adaptive autonomous driving systems capable of handling complex real-world scenarios. The research confirms the dataset's significant value in enhancing perception, prediction, and planning capabilities in unstructured driving environments [29].