标题： VLAD：一种具有分层规划和可解释决策过程的VLM增强型自动驾驶框架 显示英文标题

标题： VLAD: A VLM-Augmented Autonomous Driving Framework with Hierarchical Planning and Interpretable Decision Process

摘要： 最近在开源视觉语言模型（VLMs）如LLaVA、Qwen-VL和Llama方面的进展，推动了它们与各种系统集成的广泛研究。这些模型中包含的互联网规模的一般知识，为增强自动驾驶的感知、预测和规划能力提供了重要机遇。在本文中，我们提出了VLAD，一种视觉语言自动驾驶模型，它结合了微调的VLM和VAD，一个最先进的端到端系统。我们使用专门设计的定制问答数据集，实施了一种特殊的微调方法，旨在提高模型的空间推理能力。增强的VLM生成高层次的导航指令，VAD随后处理这些指令以指导车辆操作。此外，我们的系统生成可解释的自然语言驾驶决策说明，从而提高传统黑盒端到端架构的透明度和可信度。在真实世界nuScenes数据集上的综合评估表明，与基线方法相比，我们的集成系统将平均碰撞率降低了31.82%，为VLM增强的自动驾驶系统设立了新的基准。 显示英文摘要

摘要： Recent advancements in open-source Visual Language Models (VLMs) such as LLaVA, Qwen-VL, and Llama have catalyzed extensive research on their integration with diverse systems. The internet-scale general knowledge encapsulated within these models presents significant opportunities for enhancing autonomous driving perception, prediction, and planning capabilities. In this paper we propose VLAD, a vision-language autonomous driving model, which integrates a fine-tuned VLM with VAD, a state-of-the-art end-to-end system. We implement a specialized fine-tuning approach using custom question-answer datasets designed specifically to improve the spatial reasoning capabilities of the model. The enhanced VLM generates high-level navigational commands that VAD subsequently processes to guide vehicle operation. Additionally, our system produces interpretable natural language explanations of driving decisions, thereby increasing transparency and trustworthiness of the traditionally black-box end-to-end architecture. Comprehensive evaluation on the real-world nuScenes dataset demonstrates that our integrated system reduces average collision rates by 31.82% compared to baseline methodologies, establishing a new benchmark for VLM-augmented autonomous driving systems.