Vision-Language Models (VLMs) are becoming increasingly powerful, demonstrating strong performance on a variety of tasks that require both visual and textual understanding. Their strong generalisation abilities make them a promising component for automated driving systems, which must handle unexpected corner cases. However, to be trusted in such safety-critical applications, a model must first possess a reliable perception system. Moreover, since critical objects and agents in traffic scenes are often at a distance, we require systems that are not "shortsighted", i.e., systems with strong perception capabilities at both close (up to 20 meters) and long (30+ meters) range. With this in mind, we introduce Distance-Annotated Traffic Perception Question Answering (DTPQA), the first Visual Question Answering (VQA) benchmark focused solely on perception-based questions in traffic scenes, enriched with distance annotations. By excluding questions that require reasoning, we ensure that model performance reflects perception capabilities alone. Since automated driving hardware has limited processing power and cannot support large VLMs, our study centers on smaller VLMs. More specifically, we evaluate several state-of-the-art (SOTA) small VLMs on DTPQA and show that, despite the simplicity of the questions, these models significantly underperform compared to humans (~60% average accuracy for the best-performing small VLM versus ~85% human performance). However, it is important to note that the human sample size was relatively small, which imposes statistical limitations. We also identify specific perception tasks, such as distinguishing left from right, that remain particularly challenging for these models.

翻译：视觉语言模型（VLMs）正变得越来越强大，在多种需要视觉与文本理解的任务中展现出卓越性能。其强大的泛化能力使其成为自动驾驶系统中极具前景的组成部分，因为这类系统必须处理各种意外极端情况。然而，要在如此安全关键的应用中获得信任，模型首先必须具备可靠的感知系统。此外，由于交通场景中的关键物体与交通参与者通常位于远处，我们需要系统具备“非短视”特性，即在近距离（20米以内）和远距离（30米以上）均具备强大的感知能力。基于此，我们提出了距离标注交通感知问答（DTPQA），这是首个专注于交通场景中纯感知问题的视觉问答（VQA）基准，并增加了距离标注信息。通过排除需要推理的问题，我们确保模型性能仅反映其感知能力。鉴于自动驾驶硬件算力有限，无法支持大型VLMs，本研究聚焦于小规模VLMs。具体而言，我们在DTPQA上评估了多种先进的小规模VLMs，结果表明：尽管问题本身较为简单，但这些模型的性能显著低于人类水平（表现最佳的小规模VLM平均准确率约60%，而人类表现约85%）。但需注意，人类样本量相对较小，这带来了统计上的局限性。我们还发现某些特定感知任务（例如区分左右）对这些模型而言尤其具有挑战性。