A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Drowsiness Detection <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> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Drowsiness Detection <s> Drowsy driving is a major cause of road accidents. This paper analyses the drivers' behavior in the state of fatigue driving and introduces the latest developments of drowsy driving detection technology. In this study we also propose a drowsy driving detection based on the driver's physiological signals such as eye activity measures, the inclination of the driver's head, sagging posture, heart beat rate, skin electric potential, and electroencephalographic (EEG) activities, as well as response characteristics, decline in gripping force on the steering wheel and lane keeping characteristics. By developing a hierarchical model that is able to collect the sensing data, analyze the driving behavior and the reactions to the driver, it can provide a safe and a comfortable driving environment. Combining different indications of drowsiness and processing the contextual information to predict whether a driver is drowsy, the system not only issues a warning for the driver, but also provides the drowsy driving information to transportation control center and other vehicles if necessary. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Drowsiness Detection <s> Vehicular ad hoc networks (VANETs) have emerged as an application of mobile ad hoc networks (MANETs), which use dedicated short-range communication (DSRC) to allow vehicles in close proximity to communicate with each other or to communicate with roadside equipment. Applying wireless access technology in vehicular environments has led to the improvement of road safety and a reduction in the number of fatalities caused by road accidents through development of road safety applications and facilitation of information sharing between moving vehicles regarding the road. This paper focuses on developing a novel and nonintrusive driver behavior detection system using a context-aware system in VANETs to detect abnormal behaviors exhibited by drivers and to warn other vehicles on the road to prevent accidents from happening. A five-layer context-aware architecture is proposed, which is able to collect contextual information about the driving environment, to perform reasoning about certain and uncertain contextual information, and to react upon that information. A probabilistic model based on dynamic Bayesian networks (DBNs) in real time, inferring four types of driving behavior (normal, drunk, reckless, and fatigue) by combining contextual information about the driver, the vehicle, and the environment, is presented. The dynamic behavior model can capture the static and the temporal aspects related to the behavior of the driver, thus leading to robust and accurate behavior detection. The evaluation of behavior detection using synthetic data proves the validity of our model and the importance of including contextual information about the driver, the vehicle, and the environment. <s> BIB003
A drowsy driving style is commonly analysed using advanced solutions that make use of multiple sensor data acquisition platforms. These advanced sensors provide intelligent information on the driver's physiological signals, which can include eye activity measures, the inclination of the driver's face, sagging posture, heart rate monitoring, skin electric potential, and electroencephalographic (EEG) activities BIB002 . Many possibilities exist for monitoring drowsiness, depending on the type of sensors used, which could include steering wheel gripping force measures, lane keeping drift and instantaneous swerving patterns. Specific research focus has fallen on sleepiness monitoring systems by dividing the techniques into three focal categories, namely driver biomedical signal based techniques, driver performance evaluation techniques based on steering behaviour, and driver visual analysis techniques using image processing and computer vision BIB001 . In the first category, driver biomedical evaluation can prove very effective in real-time determination of driver drowsiness, but requires for instance electrodes attached to the body of the driver. This approach is extremely invasive, and may cause irritation to the driver, regardless of the expensive installation and maintenance costs. Driver performance approaches in the second category involve variations in the lateral position of the vehicle, steering wheel angle, velocity, acceleration, as well as other controller-area-network (CAN) signals. This approach is ideal due to meaningful and easily accessible data, and has already entered commercial markets. However, numerous limitations exist for this approach which include vehicle type, driver experience and road conditions, and analysing deviations in vehicle signals might not account for a driver falling asleep on a straight road section without an explicit change in steering, heading or acceleration. This approach also finds application in monitoring and classifying other driving styles besides fatigue driving style. The third category includes techniques based on computer vision, which are natural and non-intrusive techniques that are effectively used in visual analysis systems. The reasons why these techniques are effective for drowsiness detection is due to the fact that facial appearance, head position and eye activity of a driver, are directly correlated to sleepiness and fatigue BIB003 . A dashboard mounted driver facing camera serves as the primary data acquisition sensor for these techniques, while the various solutions vary in the position and shot angle of these cameras as well as in the features extracted and applied in learning and classification.