道路危险刺激:用于研究危险检测和场景感知的注释自然主义道路视频
Jiali Song1, Anna Kosovicheva2, Benjamin Wolfe2
1Department of Psychology, University of Toronto Mississauga, Mississauga, Canada. jiali.song@utoronto.ca.
Behavior research methods
|December 11, 2023
概括
研究人员开发了一套新的现实驾驶视频数据集,以研究视觉和认知如何支持安全驾驶. 本资源有助于理解危险感知和动态场景分析,以提高道路安全.
科学领域:
- 视觉科学 视觉科学 视觉科学
- 认知心理学 认知心理学
- 交通安全 交通安全
背景情况:
- 安全驾驶非常依赖于视觉和认知功能,但对它们相互作用的经验数据很少.
- 由于安全和道德限制,研究自然驾驶过程中的感知是具有挑战性的,模拟很难复制现实世界的复杂性.
研究的目的:
- 创建真实驾驶场景的强大数据集,以调查视觉感知和危险检测.
- 开发一种工具来研究动态场景感知及其在安全驾驶中的作用.
主要方法:
- 策划了750个来自真实驾驶场景的众筹视频片段的数据集,并对空间,时间和分类信息进行了注释.
- 在1356个视频剪辑中从48名观察员收集了连续的危险性评级,以捕捉细微的危险感知.
主要成果:
- 数据集包括空间,时间和分类注释的驾驶危险和非危险的视频片段.
- 观察者对感知危险性的评级显示了高的评级者之间的一致性和稳定性,覆盖了整个规模.
结论:
- 新型刺激套件为了解驾驶员危险检测能力和动态场景感知提供了宝贵的资源.
- 这一数据集可以促进交通安全,计算机视觉和视觉功能的基本方面的研究.
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