为评估视觉语言模型在交通场景中的感知能力，提出带距离标注的问答数据集。
Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA)
- 构建包含真实与仿真图像的交通感知问答数据集
- 每条数据标注物体与摄像头的距离，分析性能随距离变化
- 适合评估模型在远距离交通目标感知上的可靠性
视觉语言模型在多项任务中取得显著进展，其在自动驾驶这一高安全性领域应用的前景引发关注。然而，要使这些模型在关键场景中被信任，必须具备强大的感知能力——能够理解复杂且多事件并行的交通场景。尤其由于交通中的关键对象常位于远距离（30米以上），系统不仅需在近距离（20米内）表现良好，还需在远距离保持稳定感知。因此，有必要将感知能力与其他技能（如推理或世界知识）分离进行评估。为此，本文提出距离标注的交通感知问答基准（DTPQA），专用于评估视觉语言模型在交通场景中的感知性能。该基准包含两部分：基于模拟器生成的合成数据集（DTP-Synthetic）和基于真实交通图像构建的真实数据集（DTP-Real）。每个样本均包含图像、问题、正确答案及物体与摄像头的距离信息，支持分析模型性能随距离增加的退化情况。本文还提供数据生成的Python脚本，可扩展生成同类数据。
原文摘要 · Abstract (English)
The remarkable progress of Vision-Language Models (VLMs) on a variety of tasks has raised interest in their application to automated driving. However, for these models to be trusted in such a safety-critical domain, they must first possess robust perception capabilities, i.e., they must be capable of understanding a traffic scene, which can often be highly complex, with many things happening simultaneously. Moreover, since critical objects and agents in traffic scenes are often at long distances, we require systems with not only strong perception capabilities at close distances (up to 20 meters), but also at long (30+ meters) range. Therefore, it is important to evaluate the perception capabilities of these models in isolation from other skills like reasoning or advanced world knowledge. Distance-Annotated Traffic Perception Question Answering (DTPQA) is a Visual Question Answering (VQA) benchmark designed specifically for this purpose: it can be used to evaluate the perception systems of VLMs in traffic scenarios using trivial yet crucial questions relevant to driving decisions. It consists of two parts: a synthetic benchmark (DTP-Synthetic) created using a simulator, and a real-world benchmark (DTP-Real) built on top of existing images of real traffic scenes. Additionally, DTPQA includes distance annotations, i.e., how far the object in question is from the camera. More specifically, each DTPQA sample consists of (at least): (a) an image, (b) a question, (c) the ground truth answer, and (d) the distance of the object in question, enabling analysis of how VLM performance degrades with increasing object distance. In this article, we provide the dataset itself along with the Python scripts used to create it, which can be used to generate additional data of the same kind.
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