Coordinating Cognitive Assistance With Cognitive Engagement Control Approaches in Human–Machine Collaboration

In human–machine collaboration, automated machines may assist operators in a variety of ways. However, chaotic assistance may lead to negative consequences, which makes the achievement of effective coordination of the different types of assistance all the more important. This paper discusses the classification of assistance on a cognitive basis and a method of coordinating assistance. Cognitive assistance is viewed as a 2-D problem, consisting of when to provide assistance (a control problem) and what assistance to provide (an interface problem). This paper further proposes dynamically controlling cognitive engagement levels to meet the demands of maintaining performance. Cognitive engagement control determines the appropriate moment to provide the proper level of cognitive assistance. To validate the above approach, a driving assistance experiment was conducted on a driving simulator. In the experiment, an intelligent assistance system monitored the real-time driving performance of human drivers, e.g., time headway and lateral deviation. Because of the importance of visual attention in driving performance, the system monitored the cognitive engagement status of drivers by measuring their eye movements with an eye tracker. Through five sessions of car-following driving tests, the coordinated cognitive assistance (named adaptive assistance) was compared with four other types of cognitive assistance: no aid, soft aid , soft intervention, and hard intervention. The experimental results confirmed that coordinated cognitive assistance is the most effective approach to provide assistance in both primary and secondary tasks. It also appears to be more enjoyable and less intrusive when compared with other individual types of cognitive assistance.