一个多类驾驶员行为数据集,用于实时检测和改善道路安全
Arafat Sahin Afridi1, Arafath Kafy1, Ms Nazmun Nessa Moon1
1Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Data in brief
|April 25, 2025
概括
一个新的数据集通过捕捉现实世界的行为来帮助AI驾驶员监控系统. 该资源支持开发更安全的智能运输系统,并减少分心驾驶事故.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 运输工程 运输工程
背景情况:
- 驾驶员监控系统 (DMS) 对道路安全至关重要.
- 现有的数据集可能缺乏现实世界驾驶条件和行为中的多样性.
- 分心驾驶仍然是交通事故的重要原因之一.
研究的目的:
- 引入一个新的,全面的数据集,用于培训和评估人工智能驱动的驾驶员监控系统.
- 促进智能交通系统 (ITS) 的发展,以提高道路安全.
- 为了支持实时驾驶员行为检测的研究.
主要方法:
- 在孟加拉国达卡收集了7286张不同条件下 (私家车辆,公共巴士) 驾驶员行为的高分辨率图像.
- 将图像分为五类:安全驾驶,打电话,发短信,转身和其他分散注意力的行为.
- 保证的数据集反映了照明,角度和车辆类型的自然变化,以便在现实世界中应用.
主要成果:
- 已经创建了一个公开可用的,对现实世界驾驶员行为进行注释的数据集.
- 该数据集捕捉了强大的AI模型培训所必需的自然变化.
- 它为推进驾驶员监控中的AI提供了宝贵的资源.
结论:
- 这一新型数据集对人工智能驱动的驾驶员监控系统领域作出了重大贡献.
- 这一数据集的可用性将加速开发更安全的智能运输系统.
- 预计这笔资金将有助于减少因驾驶分心造成的事故.
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