Modeling of takeover variables with respect to driver situation awareness and workload for intelligent driver assistance

The situation awareness of drivers during takeover from autonomous to manual mode is important for avoidance of accidents. Previous studies have revealed that takeover time and general performance vary strongly in different situations. The studies also revealed that the variation is due to surrounding traffic conditions, complexity of the driving scenario, secondary tasks, speed of ego vehicle, and takeover request experience. The aim of this study is to further explore the scope and dependencies of the aforementioned variables to better equip driver assistance and supervision systems with the necessary framework to suitably assist drivers during takeovers. In other words, the intention is to define a formal set of rules to enable the automated driving system determine a suitable takeover request time for different scenarios. First, this contribution discusses the design of takeover variables such that the effects of the variables are systematically varied to generate different driving situations. Afterwards, experimental results under different variable combinations are discussed. The results include a comparison of objective measures and subjective measures. An initial set of rules are established to model the interaction to improve intelligent driver assistance systems.