Recent research progress on the approach of visual attention modeling for mediated perception to advanced driver assistance system (ADAS) has drawn the attention of computer and human vision researchers. However, it is still debatable whether the actual driver’s eye fixation locations (EFLs) or the predicted EFLs which are calculated by computational visual attention models (CVAMs) are more reliable for safe driving under real-life driving conditions. We analyzed the suitability of the following two EFLs using ten typical categories of natural driving video clips: the EFLs of human drivers and the EFLs predicted by CVAMs. In the suitability analysis, we used the EFLs confirmed by two experienced drivers as the reference EFLs. We found that both approaches alone are not suitable for safe driving and EFL suitable for safe driving depends on the driving conditions. Based on this finding, we propose a novel strategy for recommending one of the EFLs to the driver for ADAS under predefined 10 real-life driving conditions. We propose to recommend one of the following 3 EFL modes for different driving conditions: driver’s EFL only, CVAM’s EFL only, and interchangeable EFL. In interchangeable EFL mode, driver’s EFL and CVAM’s EFL are interchangeable. The selection of two EFLs is a typical binary classification problem, so we apply support vector machines (SVMs) to solve this problem. We also provide a quantitative evaluation of the classifiers. The performance evaluation of the proposed recommendation method indicates that it is potentially useful to ADAS for future safe driving.