Predicting Driver’s Work Performance in Driving Simulator Based on Physiological Indices

Abstract

Abstract.Developing an early warning model based on mental workload (MWL) to predict the driver's performance is critical and helpful, especially for new or less experienced drivers. This study aims to investigate the correlation between human's MWL and work performance and develop the predictive model in the driving task using driving simulator. The performance measure (number of errors), subjective rating (NASA Task Load Index) as well as six physiological indices were assessed and measured. Additionally, the group method of data handling (GMDH) was used to establish the work performance model. The results indicate that different complexity levels of driving task have a significant effect on the driver's performance, and the predictive performance model integrates different physiological measures shows the validity of the proposed model is well with R 2 = 0.781. The proposed model is expected to provide a reference value of their work performance by giving physiological indices. Based on this model, the driving lesson plans will be proposed to sustain the appropriate MWL as well as improve work performance.

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

“…Such studies have integrated data from more than one measure by conducting multi-modal analysis to extract the relevant features to capture the psychological phenomena at hand. A comparison of multiple classifiers to train & optimize machine learning algorithms can help determine the best fitting model to represent changes in cognitive states that can explain driving performance (Nadeau and Bengio, 2000; Fairclough et al, 2015; Balters and Steinert, 2017; Tran et al, 2017). Thus, utilizing multi-modal physiological signals, models could be trained to learn and predict motorists' sub-optimal cognitive states associated with unsafe-driving behavior.…”

Assessing effectiveness of wood spray painting system model for undergraduate engineering education

“…They are used to reflect the amount of information used in working memory . In spite of the rating scale results can be affected by characteristics of respondents, like biases, response sets, errors, and protest attitudes [5,14], with the low cost and the ease of administration, as well as adaptability, have been found highly useful in a variety of domains such as industrial system , office working environment , and learning evaluation .…”

“…They are used to reflect the amount of information used in working memory [28]. In spite of the rating scale results can be affected by characteristics of respondents, like biases, response sets, errors, and protest attitudes [5,14], with the low cost and the ease of administration, as well as adaptability, have been found highly useful in a variety of domains such as industrial system [1,6,18,24,33], office working environment [3], and learning evaluation [4,11,15,30]. MWL assessment based on subjective ratings, such as the NASA-task load index (NASA-TLX) [8], subjective workload assessment technique (SWAT) [19], subjective workload dominance [27], workload profile (WP), etc., are becoming increasingly important as evaluation tools and have been widely used to assess the human MWL.…”

Adaptive Driver Training With the Use of Simulator Systems

“…This allows adaptive response of AIS simulator to learning capabilities of a trainee. The importance of account for personal driver's performance expressed with physiological indices as well as subjective rating and a specific performance measure is discussed in [15].…”