Autism Spectrum Disorder (ASD) is a highly prevalent neurodevelopmental disorder with enormous individual and social cost. In this paper, a novel virtual reality (VR)-based driving system was introduced to teach driving skills to adolescents with ASD. This driving system is capable of gathering eye gaze, electroencephalography, and peripheral physiology data in addition to driving performance data. The objective of this paper is to fuse multimodal information to measure cognitive load during driving such that driving tasks can be individualized for optimal skill learning. Individualization of ASD intervention is an important criterion due to the spectrum nature of the disorder. Twenty adolescents with ASD participated in our study and the data collected were used for systematic feature extraction and classification of cognitive loads based on five well-known machine learning methods. Subsequently, three information fusion schemes—feature level fusion, decision level fusion and hybrid level fusion—were explored. Results indicate that multimodal information fusion can be used to measure cognitive load with high accuracy. Such a mechanism is essential since it will allow individualization of driving skill training based on cognitive load, which will facilitate acceptance of this driving system for clinical use and eventual commercialization.

中文翻译：

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用于自闭症干预的基于虚拟现实的驾驶系统中的认知负荷测量


自闭症谱系障碍（ASD）是一种非常普遍的神经发育障碍，给个人和社会带来巨大的损失。本文介绍了一种新颖的基于虚拟现实（VR）的驾驶系统，用于向患有自闭症谱系障碍（ASD）的青少年教授驾驶技能。除了驾驶表现数据外，该驾驶系统还能够收集眼睛注视、脑电图和周围生理学数据。本文的目的是融合多模态信息来测量驾驶过程中的认知负荷，以便个性化驾驶任务以实现最佳技能学习。由于自闭症谱系障碍的谱系性质，个体化干预是一个重要标准。二十名患有自闭症谱系障碍的青少年参与了我们的研究，收集的数据用于基于五种著名的机器学习方法进行系统的特征提取和认知负荷分类。随后，探索了三种信息融合方案——特征级融合、决策级融合和混合级融合。结果表明，多模态信息融合可用于高精度测量认知负荷。这种机制是必不可少的，因为它将允许基于认知负荷的个性化驾驶技能培训，这将有助于该驾驶系统被临床使用和最终商业化。