Behavioural modelling of automated to manual control transition in conditionally automated driving

Human-vehicle interaction in the presence of automated driving features (ADFs) poses significant challenges in behavioural adaptation. At the current level of automation, the mixed right-of -control of connected and automated vehicle (CAV) with partial human involvement is of interest. This study investigates the factors affecting the successful take over of the control by a human driver in the conditionally automated vehicle upon operational design domain (ODD) exit under different sociodemographic, mental workload, traffic flow, weather, and lighting conditions. Data collected in Virtual and Immersive Reality Environment (VIRE) are used to study the successful taking of control back from automated driving. Apart from estimating the binary and mixed logit models, a latent structure was developed to incorporate the attitudinal indicators in the integrated choice and latent variable (ICLV) model. Results indicate that almost 80 percent of participants were successful in safely regaining control. However, participants with more sensitive attitudes about CAV safety, were more likely to fail. Heavy congestion positively impacts situational awareness in taking back control safely from a CAV. Moreover, multi-tasking based on non -driving related secondary task (NDRT) engagement in a rainy night scenario, mental workload, and reaction time were significantly positive indicators of unsafe control transition. The statistics suggest that the driver's familiarity with the concept of CAVs is not enough for the safe transition of control.