EEG-Driven Classification of Driver Mental Workload in Diverse Environments: A Dual-Branch Network for Efficient In-Vehicle Applications

The mental load of drivers can profoundly affect their driving performance, to the extent that it affects traffic safety. Therefore, monitoring mental workload has become a crucial aspect of sensor-based driver monitoring systems, especially in the context of the Industrial Internet of Things (IIoT), where driver status information can be exchanged between vehicles to enhance safety. However, the substantial energy consumption and transmission latency associated with traditional central-server-based IoT systems are prominent issues that necessitate the development of lighter algorithms for edge computing in individual vehicles. In this article, we focus on the impact of external traffic events and environmental changes on the mental load of drivers, as well as effective classification algorithms applied in monitoring systems. To analyze the physiological responses of drivers to road events and non driving related tasks under different weather conditions, we proposed a dual branch model, DMW-Net, based on attention mechanism branches and graph attention modules to discriminate the mental load level of drivers from physiological signals. The proposed method was validated on the manD dataset and achieved an accuracy of 90.07% in physiological signals of three different load levels, which is higher than the comparison models. This study provides innovative methods for driver monitoring systems, contributing to advanced driving assistance systems (ADAS) and traffic safety.