A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Driver Distraction <s> This paper gives a review of the literature on driver inattention monitoring system for the purpose of active safe driving. In this paper driving inattention is classified into two categories: fatigue and distraction, while fatigue and distraction can also contain many types and levels. Individual difference on inattention phenomenon makes it more complicated to correctly detect and recognize driving inattention. Driver attention monitoring has been intensively researched in recent years and many approaches have been proposed, which include biological signal (EEG, ECG, EOG and sEMG) processing method, subjective report method, and behavior analysis method. This survey reviews a number of promising approaches and provides an overview of recent developments in this domain. The emphasis of this paper is to discuss the various methodologies to monitor driving inattention. We conclude with some thoughts about future directions. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Driver Distraction <s> There is accumulating evidence that driver distraction is a leading cause of vehicle crashes and incidents. In particular, increased use of so-called in-vehicle information systems (IVIS) and partially autonomous driving assistance systems (PADAS) have raised important and growing safety concerns. Thus, detecting the driver's state is of paramount importance, to adapt IVIS and PADAS accordingly, therefore avoiding or mitigating their possible negative effects. The purpose of this paper is to show a method for the nonintrusive and real-time detection of visual distraction, using vehicle dynamics data and without using the eye-tracker data as inputs to classifiers. Specifically, we present and compare different models that are based on well-known machine learning (ML) methods. Data for training the models were collected using a static driving simulator, with real human subjects performing a specific secondary task [i.e., a surrogate visual research task (SURT)] while driving. Different training methods, model characteristics, and feature selection criteria have been compared. Based on our results, using a support vector machine (SVM) has outperformed all the other ML methods, providing the highest classification rate for most of the subjects. Potential applications of this paper include the design of an adaptive IVIS and of a “smarter” PADAS. <s> BIB002
Driver distraction is defined in BIB001 as "a diversion of attention away from activities critical for safe driving toward a competing activity". Potential distracting activities performed by drivers while driving may include eating or drinking, attention to a person, object or event outside the car, talking, texting or listening on a cellular phone, using in-vehicle-technologies, and distracting weather conditions (rain or mist for instance). The NHTSA classifies distractions from the viewpoint of the driver's functionality into four categories, namely visual distraction, cognitive distraction, auditory distraction and biomechanical distraction (e.g., manually adjusting radio volume). A distracting activity can include a combination of the above categories. Driver distraction has been playing a role in road safety since the introduction of vehicles. The problem of driver distraction is possibly increasing as technology evolves and more functionalities are introduced to modern vehicles, such as in-vehicle information systems (IVIS), for instance navigation systems, and entertainment systems BIB002 . Mobile technology also greatly increases the probability of causing distraction while driving. This can be noticed through laws introduced by numerous countries' governments, prohibiting the use of mobile hand-held phones while driving.