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对确定驾驶员注意力状态的算法进行探索性开发.

Eileen Herbers1,2, Marty Miller1, Luke Neurauter1

  • 1Virginia Tech Transportation Institute, Blacksburg, VA, USA.

Human factors
|September 21, 2023
PubMed
概括

开发驾驶员分心算法需要仔细考虑车辆的速度和驾驶员的目光. 算法必须考虑到在准确检测分心和专注的驾驶员方面的局限性.

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科学领域:

  • * 调查驾驶员监控系统和车辆动力学,以检测分心.
  • * 应用机器学习来分析驾驶员的注意力状态.

背景情况:

  • * 由于行为变化,精确检测分心驾驶是具有挑战性的.
  • * 增加数据可用性提高了实时注意力状态识别的潜力.

研究的目的:

  • * 开发和评估使用车辆和驾驶员数据的驾驶员分心算法.
  • * 确定实时驾驶员注意力评估的最佳指标.

主要方法:

  • * 开发了四个基于缓冲区的算法,使用基于摄像头的驾驶员监控系统 (DMS) 的驾驶员目光和车辆动力学.
  • * 在自然驾驶条件下对24名驾驶员进行测试的算法.

主要成果:

  • *最佳算法整合了未分组的目光位置和车辆速度.
  • * 提高对高度分心的司机的检测,导致识别专心的司机的准确性降低.

结论:

  • * 驾驶员的目光位置和车辆速度对于分心算法至关重要.
  • *算法必须承认在准确识别分心和专注的司机方面的局限性.
关键词:
自动化自动化自动化自动化自动驾驶自动驾驶的自动驾驶.认知 认知 认知分心,分散注意力.分心和中断的干扰和中断驾驶员行为 驾驶员行为专家系统专家系统眼睛的运动 眼睛的运动运动行为 运动行为表面运输 表面运输 表面运输追踪 追踪 追踪 追踪对自动化的信任.车辆自动化 车辆自动化 车辆自动化

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