Vehicle crash rates caused by distracted driving have been rising in recent years. Hence, safety while driving on roads is today a crucial concern across the world. Some of the reasons due to which drivers may lose attention include the use of mobile phones, speaking with passengers, and reaching behind to grab something while driving. There are various types of distractions, out of which we focus on manual ones based on the posture of the driver. This work presents a review on open problems and challenges concerning the vision-based distraction recognition of drivers. Types of experimental environments are described; and image modalities, public and custom datasets and body parts inspected during distraction recognition are explored. Besides, a comparative review of different works on vision-based recognition of driver's manual distractions and limitations for each dataset is presented. Main approaches of vision-based manual distractive driving recognition can be categorized into conventional and deep learning methods. These approaches are compared and classified based on whether using temporal information or not. Finally, we give some suggestions for improvement, and look forward to future development directions of the vision-based recognition of driver's manual distractions. K E Y W O R D Sdistraction recognition, driver dataset, driver gesture, driver monitoring, driving simulator INTRODUCTIONDistractions decrease driver's awareness and decision-making; lead to increased workload, delayed responses to driving events, speed and lane maintenance disruptions. 1 Distracted drivers would lose their vehicle control, resulting in crashing into vehicles or stationary objects. 2 In other words, such a driver loses vigilance regarding the driving situation, leading to a higher collision risk 3 ; then the safety of the driver and other passengers are affected. The National Highway Traffic Safety Administration (NHTSA) 4 has reported three activities, including dialing, browsing, and texting, which make drivers have their eyes off the road for a longer time, lead to threefold crash risk. Performing high cognitive load tasks while driving impacts the driver's visual behavior and driving performance. Drivers under high cognitive loads spend less time checking mirrors, traffic lights instruments, and areas around intersections. 5 This results in unintended lane drifts, overlooking other traffic participants and, in the worst scenario, crashes. 6 Rapid improvements in computers and wireless technologies have made complex and various devices that people can use while driving.Notably, in the field of entertainment, information and driver assistance, studies have proposed numerous functions. However, using such functions during driving could distract the driver from focusing on driving itself. 6 The widespread usage of cell phones, navigation, and infotainment systems has escalated the problem of distraction. Therefore, the cognitive load caused by secondary tasks has increased. Multitasking is today an essential element of ind...
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