驱动QA:基于视觉数据的数据集,用于在驾驶场景中视觉回答问题
Kaavya Rekanar1, John M Joyce2, Martin Hayes1
1Department of Electronic and Computer Engineering, University of Limerick, Limerick V94 T9PX, Ireland.
Data in brief
|March 3, 2025
概括
驱动VQA数据集在驾驶场景中捕捉了目光数据,以训练视觉问答 (VQA) 模型. 这使得自动驾驶汽车能够学习类似人类的注意力,以提高决策和安全性.
科学领域:
- 计算机视觉 计算机视觉
- 人与计算机的交互
- 认知科学 认知科学
背景情况:
- 视觉问题答案 (VQA) 对于自动驾驶汽车的决策至关重要,需要可解释和可信赖的AI.
- 在驾驶场景中理解人类的视觉注意力是开发类似人类AI感知的关键.
研究的目的:
- 介绍DriVQA数据集,这是一个新的资源,结合了驾驶场景图像,VQA数据和详细的眼睛跟踪信息 (凝视图,热图).
- 促进对VQA主观性的研究,并训练人工智能模型以模拟人类注意力,以提高视觉理解.
主要方法:
- 在模拟驾驶场景中使用Tobii Pro X3-120眼球追踪设备收集的目光数据.
- 配对的驾驶图像与参与者生成的问题和答案,以及精确的凝视图和热图.
主要成果:
- 驱动QA数据集提供了对人类视觉注意力的全面映射,以响应驾驶刺激.
- 为个人如何感知和解释复杂的视觉场景提供了独特的见解.
结论:
- 驱动VQA是开发先进的VQA模型的重要资源,这些模型可以从类似人类的注意力模式中学习.
- 该数据集支持自动驾驶,驾驶辅助系统和认知科学研究的进步,通过实现更好的视觉解释和互动.
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