Detecting and recognizing driver distraction through various data modality using machine learning: A review, recent advances, simplified framework and open challenges (2014–2021)

Driver distraction is one of the main causes of fatal traffic accidents. Therefore, the ability to detect driver inattention is essential in building a safe yet intelligent transportation system. Currently, the available driver distraction detection systems are not widely available or limited to specific class actions. Various research efforts have approached the problem through different techniques, including the usage of intrusive sensors, which are not feasible for mass production. Most of the work in early 2010s used traditional machine learning approaches to perform the detection task. With the emergence of deep learning algorithms, many research has been conducted to perform distraction detection using neural networks. Furthermore, most of the work in the field is conducted under simulation or lab environment, and did not validate the proposed system under naturalistic scenario. Most importantly, the research efforts in the field could be further subdivided into many subtasks. Thus, this paper aims to provide a comprehensive review of approaches used to detect driving distractions through various methods. We review all recent papers from 2014–2021 and categorized them according to the sensors used. Based on the reviewed articles, a simplified framework to visualize the detection flow, starting from the used sensors, collected data, measured data, computed events, inferred behaviour, and finally its inferred distraction type is proposed. Besides providing an in-depth review and concise summary of various published works, the practicality and relevancy of driver distraction detection towards increasing vehicle automation are discussed. Further, several open research challenges and provide suggestions for future research directions are provided. We believe that this review will remain helpful despite the development towards a higher level of vehicle automation.