Hybrid Intelligent System for Driver Workload Estimation for tailored vehicle-driver communication and interaction

Advanced vehicle cabin technologies provide drivers infotainment, navigation, and enhanced convenient driving experiences. As interaction between the driver and in cabin technologies increases it is beneficial to provide tailored driver communication for an improved cabin experience. Assessment of the driving demand is of particular value to assist in modulating communication and vehicle system interactions with the driver. The complex vehicle, driver, and environment driving contexts require innovative prognostic approaches to driver workload inference. This paper presents a Hybrid Intelligent System for Driver Workload Estimation (HWLE). The real-time HWLE soft computing modules incorporate expert models, model-driven reasoning, and specialized computational intelligence techniques to compute an aggregated WLE-Index. The context depended WLE-Index facilitates tailoring vehicle-driver communication and interaction based on the driving demand. Results from application of the HWLE system under real-time conditions are presented.