通过驾驶员认知因素推断,个性化驾驶员安全接口
Emily S Sumner1,2, Jonathan DeCastro3,4, Jean Costa5
1Toyota Research Institute, Los Altos, CA, USA. emily.sumner@tri.global.
Scientific reports
|August 5, 2024
概括
这项研究通过从驾驶行为中推断诸如冲动性之类的认知状态来个性化先进的驾驶辅助系统 (ADAS). 这种人工智能驱动的方法增强了驾驶员安全接口,有效地减少了关键区域的危险行为.
科学领域:
- 人工智能的人工智能
- 智能运输系统 智能运输系统
- 认知心理学 认知心理学
背景情况:
- 先进的驾驶辅助系统 (ADAS) 提供了更安全的移动潜力,但往往缺乏个性化.
- 认知因素,包括冲动性和抑制性控制,显著影响危险的驾驶行为.
- 目前的ADAS并没有充分将个体认知状态纳入其辅助策略中.
研究的目的:
- 为高级驾驶辅助系统 (ADAS) 开发和评估个性化的驾驶员安全接口.
- 从实时驾驶行为推断潜在的认知状态 (冲动性,抑制性控制).
- 根据推断的驾驶员认知概况,动态地进行安全干预.
主要方法:
- 一种基于数据的方法,使用在人类驾驶数据上训练的循环神经网络 (RNN).
- 从最近的驾驶行为中推断出冲动性和抑制性控制,使用训练好的RNN.
- 由推断的认知状态触发的个性化驾驶员安全接口的实时参与.
主要成果:
- 循环神经网络成功地推断出高保真模拟器中的驱动行为的冲动性和抑制性控制.
- 开发和测试了一个个性化的驾驶员安全接口,由推断的认知状态提供信息.
- 个性化界面在影响驾驶员行为方面表现出更高的有效性,特别是在模拟的黄色灯区减少红灯运行.
结论:
- 从驾驶行为推断出认知状态,可以实现个性化的驾驶员辅助.
- 人工智能驱动的ADAS接口的个性化可以带来更有效的干预,对危险的驾驶.
- 这种方法有望通过适应个体驾驶员的认知特征来提高道路安全.
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