构建首个面向自动驾驶的语义视觉语言数据集，支持场景级理解与可解释性评估。
CARScenes: Semantic VLM Dataset for Safe Autonomous Driving
- 基于GPT-4o+人工审核，对5192张图像进行28类350+属性标注
- 包含环境、车辆行为、路权使用者等多维度标签，支持严重度评分（1-10）
- 提供可复现基线模型与分析脚本，适合智能驾驶可解释性研究
CAR-Scenes是一个面向自动驾驶的帧级数据集，支持视觉语言模型（VLMs）在可解释性场景理解方面的训练与评估。我们从Argoverse 1、Cityscapes、KITTI和nuScenes中选取5,192张图像，采用涵盖环境、道路几何、背景车辆行为、自车行为、弱势道路使用者、传感器状态及离散严重度量表（1-10）的28个类别/子类别知识库进行标注，共生成350+个叶节点属性。标签通过GPT-4o辅助的视觉语言管道生成，并经人工闭环验证；我们公开完整提示词、后处理规则及各字段基线模型性能。该数据集还提供属性共现图与JSONL记录，支持语义检索、数据筛选与跨源风险感知场景挖掘。为校准任务难度，我们提供可复现的非基准基线，包括使用确定性解码的LoRA微调Qwen2-VL-2B模型，基于固定验证集评估标量准确率、列表属性的微平均F1以及严重度的MAE/RMSE。我们公开标注与分析脚本，包括图构建与评估脚本，推动未来智能汽车的可解释、以数据为中心的研究工作。
原文摘要 · Abstract (English)
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
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