The Improvement of Road Driving Safety Guided by Visual Inattentional Blindness

Abstract

The computational modeling of human visual attention has received much attention in recent decades. In advanced industrial applications, it has been demonstrated that computational visual attention models (CVAMs) can predict visual attention very similarly to human visual attention. However, it is controversial whether the driver’s eye fixation location (EFL) or the predicted eye fixation location of computational visual attention models is more reliable and helpful for actual driving. To address this issue, an open database of videos taken under the most common 18 driving conditions in everyday driving has been established. In experiments using this database, expert drivers found that it was not sufficient for drivers to rely on only one of the two EFLs. Based on this finding, a hybrid EFL recommendation strategy is proposed for improving driving safety. By extracting visual characteristics from human dynamic vision, the performance of the proposed recommendation method demonstrates its potential value in these collected driving tasks. In addition, the visual comfort of driving is further addressed to enhance the safety of driving. From the results of experiments on 108 driving video clips taken of the most common 18 real driving conditions, it is confirmed that the proposed EFL recommendation achieves an experience rating of driving comfort between 88.1 and 92.7 out of 100.