A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Drunk Driving Style <s> This book is concerned with fatalities, injuries, and property damage from traffic crashes--their origin and nature, and ways to prevent their occurrence and reduce their severity. This subject is covered in the following 15 chapters: (1) Introduction; (2) Effects of Sex and Age; (3) An Overview of United States Traffic Fatalities; (4) Engineering, Roadway and Environmental Factors; (5) Driver Performance; (6) Driver Behavior; (7) Alcohol's Role in Traffic Crashes; (8) Drunk Driving Countermeasures; (9) Effectiveness of Occupant Protection Devices When They Are Used; (10) Restraint-Use Laws, Use Rates, and Field Effectiveness; (11) User Responses to Changes in Traffic Systems; (12) How You Can Reduce Your Risk; (13) An Attempt to Estimate the Relative Importance of Factors; (14) Traffic Safety in Broader Contexts; and (15) Conclusions. An Author Index and a Subject Index are provided. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Drunk Driving Style <s> Abstract This study examines the differences in alcohol-related accident involvement among different driver groups in the state of Florida. The driver characteristics considered in this study are: age, gender, race, and residency of the driver of a motor vehicle involved in an accident while under the influence of alcohol, drugs, or alcohol and drugs. The main objective of this study is to test whether there are associations between the different driver characteristics and alcohol involvement in traffic accidents, and to identify the high-risk group within each driver factor. This would improve our understanding of the relationship between alcohol involvement, accidents, and the four aforementioned driver factors. It would also enable us to better design educational and awareness programs targeting specific groups in the population to reduce drinking and driving in the state. The relationship between alcohol-related accident involvement and the driver factors are investigated using general descriptive statistics, conditional probabilities and log-linear models. The results showed that the 25–34 age group experience the highest rate of alcohol/drug involvement in accidents. The rates decline with the increase in the age of the drivers. The results also indicated that there are significant relationships between the driver characteristics and alcohol/drug involvement in accidents. Male, white, and in-state drivers were also more involved in alcohol/drugs-related traffic accidents. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Drunk Driving Style <s> Drunk driving, or officially Driving Under the Influence (DUI) of alcohol, is a major cause of traffic accidents throughout the world. In this paper, we propose a highly efficient system aimed at early detection and alert of dangerous vehicle maneuvers typically related to drunk driving. The entire solution requires only a mobile phone placed in vehicle and with accelerometer and orientation sensor. A program installed on the mobile phone computes accelerations based on sensor readings, and compares them with typical drunk driving patterns extracted from real driving tests. Once any evidence of drunk driving is present, the mobile phone will automatically alert the driver or call the police for help well before accident actually happens. We implement the detection system on Android G1 phone and have it tested with different kinds of driving behaviors. The results show that the system achieves high accuracy and energy efficiency. <s> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Drunk Driving Style <s> Road traffic accidents (RTAs) pose a public health and development challenge and greatly affect the human capital development of every nation. Main causes for a significant percentage of RTAs are recklessness, fatigue or stress, inexperienced driving and driving under the influence of alcohol. Reputed automobile companies and authorities have established standards and/or proactive/reactive solutions over the years to overcome this problem but the rates continue to grow each year. Software solutions that focus on preventing RTAs are difficult to be found in economy class automobiles that are used by the majority of consumers. The limited number of successful applications are either bound to a certain brand/model and/or focus on a specific task. SmartV is smart phone based vigilance monitoring system that focuses on the majority of factors contributing to RTAs. These include DUI (driving under the influence of alcohol), health issues and vehicle defects. The mobile application communicates with a heart rate monitor and a Bluetooth OBDII Adapter. These will continuously monitor the heart rate of the driver and the status of the vehicle respectively. During the course of the drive, the system monitors the behavior of the vehicle for existence of dangerous driving patterns that are defined in Visual Detection of DWI Motorists[8] a study conducted by U.S.A. N.H.T.S.A. The final product is a versatile, non-intrusive, flexible and most importantly an affordable mobile application that captures the vehicle's behavior, the health level of the driver and the status of the vehicle. Real environment operation of SmartV has yielded significant results proving itself to be a successful and an affordable Vigilance Monitoring System for the future. <s> BIB004 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Drunk Driving Style <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> BIB005 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Drunk Driving Style <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> BIB006
Another measurable area of driver behaviour is the influence of intoxication on an individual's normal driving behaviour. A clear deterioration to a whole range of tasks and behaviour is evident upon intoxication, due to intoxication having a degenerative influence on personal traits such as competence to perform a task, and reduction in self-discipline and concentration (ultimately leading to risky and out of the ordinary behaviour) BIB002 . A drunk driving style may include aspects of the aggressive driving style but due to the degenerative influence of alcohol the degradation of a driver's performance from his normal driving behaviour can be identified and this information can indicate that a driver might be driving under the influence of alcohol. The inattention introduced by intoxication will be best measured as part of the inattentive driving style. Road safety is greatly influenced by drivers that drive under the influence of alcohol. The US NHTSA have estimated the death toll caused by traffic accidents in the United States since 1966 to be more than a million people, and in 2007 and 2008 alone, 24,814 alcohol-impaired driving fatalities have been recorded and was responsible for approximately 32% of all driving fatalities for those years BIB003 . The presence of alcohol in a driver's bloodstream greatly reduces the ability of the driver to operate a vehicle safely BIB002 . Vehicle accident occurrence proliferation is not the only result of driving under the influence (DUI), but accident severity is greatly enhanced due to the increased likelihood of injury or death from the same impact occurring with increased blood alcohol concentration (BAC) in a person's bloodstream BIB001 . DUI of alcohol, is one of the prime causes of traffic accidents BIB004 , and due to the characteristic behaviour, observable from a driver who is driving drunk, the use of alcohol intoxication detection applications are actively being investigated through various research efforts. Drunk driving has formed a part of many context-aware driver behaviour systems BIB005 BIB006 and plays an important role in driver-feedback and driver assistance applications aiming to prevent accidents or to notify authorities of possible intoxicated drivers BIB003 .