The Impacts of Temporal Variation and Individual Differences in Driver Cognitive Workload on ECG-Based Detection

Assessment of Drivers' Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios

Assessing drivers' mental workload is crucial for reducing road accidents. This study examined drivers' mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers' mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers' mental states.

Understanding the domain of driving distraction with knowledge graphs

This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs were constructed to demonstrate bibliometric information and domain-specific research content respectively. In terms of bibliometric analysis, the evolution of publication and citation numbers reveals the accelerated development of this domain, and trends of multidisciplinary and global participation could be identified according to knowledge graphs from Vosviewer. In terms of research content analysis, a new framework consisting of five dimensions was clarified, including "objective factors", "human factors", "research methods", "data" and "data science". The main entities of this domain were identified and relations between entities were extracted using Natural Language Processing methods with Python 3.9. In addition to the knowledge graph composed of all the keywords and relationships, entities and relations under each dimension were visualized, and relations between relevant dimensions were demonstrated in the form of heat maps. Furthermore, the trend and significance of driving distraction research were discussed, and special attention was given to future directions of this domain.

High expectancy influences the role of cognitive load in inattentional deafness during landing decision-making

Neglecting a critical auditory alarm is a major obstacle to maintaining a safe environment, especially in aviation. Earlier studies have indicated that tasks with a higher perceptual or cognitive load in the visual modality influence the processing of auditory stimuli. It is unclear, however, whether other factors, such as memory failure, active neglect, or expectancy influence the effect of cognitive load on auditory alarm detection sensitivity during aeronautical decision-making. In this study, we investigated this issue in three laboratory experiments using the technique of signal detection analysis, in which participants were asked to make a landing decision based on indicators of the instrument landing system while also trying to detect an audible alarm. We found that the sensitivity of auditory alarm detection was reduced under conditions of high cognitive load and that this effect persisted even when the auditory detection response occurred first (before the landing decision response) and when the probability of an auditory alarm was 40%. However, the sensitivity of auditory detection was not influenced by cognitive load under high expectancy conditions (60% probability of alarm presentation). Furthermore, the value of the response bias was reduced under high cognitive load conditions when the probability of an auditory alarm was low (20%). With an increase in the level of expectancy (40% and 60% probability of alarm presentation), it was found that cognitive load did not influence the response bias. These findings indicate that visual cognitive load affects the sensitivity to an auditory alarm only at a low expectancy level (20% and 40% probability of alarm presentation). The effect of cognitive load on the sensitivity to an auditory alarm was not due to memory failure or active neglect and the response bias was more sensitive to the expectancy factor.