Risk prediction model for drivers’ in-vehicle activities – Application of task analysis and back-propagation neural network

This study aims to develop a risk prediction model for in-vehicle tasks performed by drivers by using two methods: task analysis (TA) and back-propagation neural networks (BPNNs). Sixty-six participants volunteered to participate and were divided in two groups with different in-vehicle secondary tasks (traditional vs. in-vehicle information system/IVIS) and participated in a driving experiment simulating low/high driving load road conditions. We assessed driving performance (i.e. longitudinal velocity and lateral acceleration variance), hand movements (i.e. number of movements and movement durations), visual judgment behaviors (i.e. glance duration and glance frequency) and response time. Task analysis results allowed us to generate input and output variables for further BPNN modeling. The overall risk prediction accuracy rate of our model was as high as 60%. In addition, an analysis of variable importance demonstrated that the longitudinal velocity was the most important variable in predicting traditional in-vehicle tasks, whereas the number of glances was the most important variable for predicting IVIS in-vehicle tasks. This study may help researchers better understand safety considerations related to in-vehicle secondary tasks and in-vehicle interface design.

► We used task analysis to generate critical drivers’ in-vehicle activities.
► We developed a BPNN risk prediction model for drivers’ in-vehicle tasks.
► The accuracy of using the BPNN perdition model was as high as 60%.
► Longitudinal velocity was important in predicting traditional in-vehicle tasks.
► The number of glances was important for predicting IVIS in-vehicle tasks.