Identification of Driving Workload in Plateau Environment: A Naturalistic Driving Study

Abstract

Maintaining driving workload (DWL) at an appropriate level is crucial for preventing driver‐related crashes. However, the unique conditions of plateau environments significantly impact DWL, increasing driving risks. Research on DWL identification, particularly in real‐world plateau driving scenarios, remains limited. This study recruited 27 participants for a naturalistic driving experiment on the Qinghai–Tibet Plateau, integrating psychological and physiological factors to assess DWL. Electrocardiogram (ECG) signals were collected using a wearable wireless physiological monitor, whereas driving video was recorded with two driving recorders. Participants reviewed driving scenarios and operations through recorded videos and rated their subjective DWL using the NASA Task Load Index (NASA‐TLX). The self‐reported NASA‐TLX scores were clustered by C‐mean fuzzy (FCM). The cluster results served as classification labels, whereas the corresponding ECG signals were used as features. Then, an extreme gradient boosting (XGBoost) model, optimized by the tree‐structured Parzen estimator (TPE) algorithm, classified DWL into three levels. Results show that the proposed model achieves 90.53% accuracy, with an F1 score of 0.91. Under real‐world plateau driving conditions, integrating ECG features with subjective workload ratings effectively classified DWL, particularly when using heart rate (HR) and the low‐to‐high frequency (LF/HF) power ratio. Although the medium level of DWL is more challenging to classify than the other two levels, incorporating multiple physiological features significantly improves the model’s performance in identifying it. These findings provide valuable insights into feature selection and model development for DWL assessment, contributing to optimized road design and enhanced driving safety management in plateau regions.