CARScenes：面向安全自动驾驶的语义视觉语言模型数据集
CARScenes: Semantic VLM Dataset for Safe Autonomous Driving
AI总结：
该研究构建CAR-Scenes帧级自动驾驶数据集，含5192张图像与350+属性，通过GPT-4o辅助标注并公开相关资源，提供属性共现图等工具，还验证了LoRA微调Qwen2-VL-2B等基线，支持VLMs场景理解与智能车辆数据工作流。
摘要：
CAR-Scenes是一个用于自动驾驶的帧级数据集，可支持训练和评估视觉语言模型（VLMs）以实现可解释的场景级理解。我们从Argoverse 1、Cityscapes、KITTI和nuScenes中选取5192张图像，使用涵盖环境、道路几何、背景车辆行为、自车行为、弱势道路使用者、传感器状态以及1-10离散严重程度量表的28键类别/子类别知识库进行标注，总计包含350+叶子属性。标签由GPT-4o辅助的视觉语言流水线生成并经人在环验证；我们发布了精确的提示词、后处理规则及各字段的基线模型性能。CAR-Scenes还提供属性共现图和JSONL记录，支持跨数据源的语义检索、数据集筛选和风险感知场景挖掘。为校准任务难度，我们纳入可复现的非基准基线，尤其是采用确定性解码的LoRA微调Qwen2-VL-2B，在固定验证集上通过标量准确率、列表属性的微平均F1以及严重程度MAE/RMSE进行评估。我们公开了标注和分析脚本（包括图构建和评估脚本），以支持未来智能车辆的可解释、数据中心式工作流。数据集：https://github.com/Croquembouche/CAR-Scenes
英文摘要：
CAR-Scenes is a frame-level dataset for autonomous driving that enables training and evaluation of vision-language models (VLMs) for interpretable, scene-level understanding. We annotate 5,192 images drawn from Argoverse 1, Cityscapes, KITTI, and nuScenes using a 28-key category/sub-category knowledge base covering environment, road geometry, background-vehicle behavior, ego-vehicle behavior, vulnerable road users, sensor states, and a discrete severity scale (1-10), totaling 350+ leaf attributes. Labels are produced by a GPT-4o-assisted vision-language pipeline with human-in-the-loop verification; we release the exact prompts, post-processing rules, and per-field baseline model performance. CAR-Scenes also provides attribute co-occurrence graphs and JSONL records that support semantic retrieval, dataset triage, and risk-aware scenario mining across sources. To calibrate task difficulty, we include reproducible, non-benchmark baselines, notably a LoRA-tuned Qwen2-VL-2B with deterministic decoding, evaluated via scalar accuracy, micro-averaged F1 for list attributes, and severity MAE/RMSE on a fixed validation split. We publicly release the annotation and analysis scripts, including graph construction and evaluation scripts, to enable explainable, data-centric workflows for future intelligent vehicles. Dataset: https://github.com/Croquembouche/CAR-Scenes
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
