论文标题
你在想什么？驾驶时，心理和感知负载估计框架朝着自适应的车载相互作用
What's on your mind? A Mental and Perceptual Load Estimation Framework towards Adaptive In-vehicle Interaction while Driving
论文作者
论文摘要
一些研究人员专注于研究驾驶时驾驶员认知行为和精神负荷。随着精神和感知负荷水平而变化的自适应界面可以帮助减少事故并增强驾驶员体验。在本文中，我们分析了心理工作量和感知负荷对心理生理学维度的影响，并在双重车辆相互作用的双重任务方案（https://github.com/amrgoma/amrgomaaelhady/mrgomaaelhady/mwl-pl-pl-pl-imator）为基于机器学习的框架提供了基于机器学习的框架。我们使用现成的非侵入性传感器，可以轻松地集成到车辆系统中。我们的统计分析表明，尽管心理工作负载影响了一些心理生理方面，但感知负荷几乎没有影响。此外，我们通过融合这些测量值对心理和感知负载水平进行了分类，朝着个性化的用户行为和驾驶条件个性化的实时自适应车载界面迈进。我们报告多达89％的心理工作负载分类准确性，并提供实时最低侵入的解决方案。
Several researchers have focused on studying driver cognitive behavior and mental load for in-vehicle interaction while driving. Adaptive interfaces that vary with mental and perceptual load levels could help in reducing accidents and enhancing the driver experience. In this paper, we analyze the effects of mental workload and perceptual load on psychophysiological dimensions and provide a machine learning-based framework for mental and perceptual load estimation in a dual task scenario for in-vehicle interaction (https://github.com/amrgomaaelhady/MWL-PL-estimator). We use off-the-shelf non-intrusive sensors that can be easily integrated into the vehicle's system. Our statistical analysis shows that while mental workload influences some psychophysiological dimensions, perceptual load shows little effect. Furthermore, we classify the mental and perceptual load levels through the fusion of these measurements, moving towards a real-time adaptive in-vehicle interface that is personalized to user behavior and driving conditions. We report up to 89% mental workload classification accuracy and provide a real-time minimally-intrusive solution.
