Effects of IVIS touchscreen operation tasks on driver’s mental workload based on explainable CatBoost algorithm

Abstract

With the widespread adoption of touchscreen-based in-vehicle information systems (IVIS) in vehicles, a large amount of effective information is displayed on the system, resulting in a significant increase in the frequency of driver interaction with these systems. However, complex operational tasks can lead to elevated mental workload, thereby impacting driving safety. This study aims to investigate the effects of secondary tasks involving IVIS touchscreen operations on driver mental workload. Through driving simulation experiments and survey questionnaires, three car-following scenarios were designed at speed levels of 60, 40, and 20 km/h. A total of 36 participants completed the IVIS secondary task driving simulation tests. Using statistical analysis and interpretable machine learning methods, a driver mental workload prediction model based on the CatBoost algorithm was constructed. Shapley Additive exPlanations (SHAP), partial dependence plot (PDP), and individual conditional expectation (ICE) were used to comprehensively analyze the relationship between important driving behavior characteristics and mental workload. The results of the study indicate that as the number of manual operations of IVIS touchscreen secondary tasks increases, the driver’s mental workload, standard deviation of speed, standard deviation of Lateral offset distance, task completion time, and saccade number correspondingly significant increase (p < 0.05). With the increase of the driver’s mental workload, the speed of the following vehicle decreases (p < 0.01), along with a significant reduction in following distance (p < 0.05). When the number of IVIS secondary task manual operations exceeded 3, the probability of a high mental workload significantly increased. These findings provide a basis for designing safer IVIS, contributing to enhanced driving safety and improved driver experience, holding significant theoretical and practical application value.