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.

该研究分析了心理和感知负载对双重任务场景下的心理生理维度的影响，并提供了基于机器学习的框架来估计心理和感知负载。研究结果表明，精神负荷影响了某些心理生理维度，而感知负荷影响较小。此外，基于这些测量，分类了精神负荷和感知负荷水平，并提供了一个实时的自适应车内界面，以满足用户行为和驾驶条件的个性化需求。

心智和感知负荷估计框架：面向驾驶中自适应车内交互的研究