Key result
KNN algorithm predicts driver psychophysiological workload and evaluates driving safety using vehicle maneuvering data.
Why the study?
Evaluating driving safety via EEG-measured psychophysiological workload requires high-cost experiments and extensive data analysis, creating a need for predictive methods using vehicle maneuvering data.
A novel methodology using vehicle maneuvering data and KNN algorithms can predict driver psychophysiological workload to evaluate driving safety without requiring high-cost EEG experiments.
May enable low-cost workload monitoring in safety research; leaves open validation before informing driver assessments.
Evaluating driving safety according to road alignment conditions is a fundamental for safe road design and traffic operations to prevent traffic crashes. A significant intrinsic factor affecting driving safety is the driver’s psychophysiological workload generated in response to various external environmental changes during road driving. Electroencephalogram (EEG) is a well-known measure to evaluate the driver workload resulting from the response to the internal and external stimuli experienced while driving vehicles. However, the use of EEG in research of the transportation engineering domain requires not only high-cost experiments but also great efforts in data analysis. To address these issues, this study developed a promising method to predict the psychophysiological workload affecting driving safety using vehicle maneuvering data. A total of 30 drivers participated in driving simulation experiments using a high-fidelity motion-based driving simulator to obtain both psychophysiological workload data and vehicle maneuvering data to be used for developing the methodology. The proposed methodology consists of two parts: the prediction of the level of psychophysiological workload using a K-nearest neighbor (KNN) algorithm and the derivation of the alertness risk index (ARI) based on the spatiotemporal aggregation of the prediction results. Additionally, evaluating driving safety by different road conditions is conducted based on the proposed methodology.
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Jang et al. (2023) studied Driving safety and psychophysiological workload (n=30). Vehicle maneuvering data-based prediction model (KNN algorithm) was evaluated on Prediction of psychophysiological workload level and derivation of alertness risk index (ARI). A methodology using a K-nearest neighbor algorithm and vehicle maneuvering data was developed to predict driver psychophysiological workload and evaluate driving safety.
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