Driving as well as on a Sunny Day? Predicting Driver's Fixation in Rainy Weather Conditions via a Dual-Branch Visual Model

Abstract

Dear Editor, Traffic driving is a dynamic and complicated task in which drivers are required to pay close attention to the important targets or regions to maintain safe margins. Rainy weather conditions make it more challenging with factors such as low visibility, raindrops, pedestrian with umbrellas, wipers, etc. Studies showed that rainy condition affects driving safety significantly [1], [2]. It is reported that, in raining weather condition, the odds for a fatal accident are 3.340 times higher on highways than on streets [3]. An investigation of the relationship between rainfall and fatal crashes in Texas from 1994 to 2018 on fatality analysis reporting system (FARS) database illustrated that rain-related fatal crashes represented about 6.8% of the total fatal crashes on average, moreover, the proportion showed high variability at the annual, monthly, and hourly time scales [4]. Therefore, raining is a complex and critical factor for road safety planning and management. In fact, the traffic environment is a dynamic scene with multiple sources of information, including important targets that are highly relevant to the current driving task as well as irrelevant targets that may distract the driving task [5]. Driven by the visual selective attention mechanism, experienced drivers often focus their attention on the most important regions and only show concern for objects related to driving safety in those salient regions. This selective attention mechanism [6], [7] helps drivers reduce the interference of irrelevant scene information and guarantee the driving safety. Understanding the selective attention mechanism of experienced drivers and then simulating the efficient saliency detection process in rainy conditions may help driving a car in rainy conditions as well as on a sunny day.