A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Driver Fatigue <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 Fatigue <s> Fatigue driving is an important cause of traffic accidents, the key to avoid fatigue driving is to detect the driver’s fatigue condition in real time. Driving behaviors such as accelerate???brake???shift and steer, continuous change when drivers become more and more fatigue. By detecting and analyzing the changing signals of them, it can show whether the driver is fatigue or not. This fatigue driving detection system is composed of driving simulator, data acquisition system, PC serial communication System and fatigue identification model. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Driver Fatigue <s> This paper presents a non-intrusive approach for monitoring driver drowsiness, based on driver and driving data fusion. The Percentage of Eye Closure (PERCLOS) is used to estimate the driver's state. The PERCLOS is computed on real time using a stereo vision-based system. The driving information used is the lateral position, the steering wheel angle and the heading error provided by the CAN bus. These three signals have been studied in the time and frequency domain. A multilayer perceptron neural network has been trained to fetch an optimal performance score. This system was installed in a naturalistic driving simulator. For evaluation purposes, several experiments were designed by psychologists and carried out with professional drivers. As ground truth, subjective experts' manual annotation of the driver video sequences and driving signals was used. A detection rate of 70% using individual indicators was raised up to 94% with the combination of indicators. An explanation about these results and some conclusion are presented. <s> BIB003
Driver fatigue is a measurable element of inattentive driving. Most drowsy drivers will try to fight against falling asleep in an attempt to prevent causing an accident, with deviations in sequenced physiological events, and durations of these events, as compared to similar non-sleepy behaviour. Symptoms observable in the event of a fatigued driver include repeated yawning, confusion, feeling depressed and irritable, delayed reactions and responses, daydreaming, lazy steering, difficulty keeping the eyes open and burning sensation in the eyes, lapses in concentration, swaying of the head, shallow breathing, racing heart beats, and the vehicle wandering between lanes or from the road BIB001 . A report on the influence of driver fatigue on road accidents conducted by the European Transport Safety Council (ETSC) mentioned that fatigue "concerns the inability or disinclination to continue an activity, generally because the activity has been going on for too long"  . It was concluded that central nervous fatigue (sleepiness) and mental fatigue (lack of energy to do anything) of the driver, were more dangerous towards driving than physical fatigue. There is a clear indication that 15%-20% of all vehicle accidents are estimated to be related to sleepiness and that fatigue driving increases the risk of an accident occurring by four to six times BIB002 . The prevalence of accidents are higher at night than during the day BIB003 , which also confirms the consequences of fatigue driving on accident occurrence. Bad lighting conditions at night time naturally also contributes to this phenomenon.