A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Aggressive Driving Style <s> Driving style can characteristically be divided into two categories: “typical” (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Aggressive Driving Style <s> Detection and classification of aggressive driving can be based on the use of physiological signals or biometric information like electrocardiogram, electro dermal activity, and respiration. This research proposes a driving performance inference system based on the signature of acceleration in the two dimensions and speed. Driving style can be categorized to: below normal, normal, aggressive, and very aggressive. One of the targets of this paper is to recognize the driving events that fall into each of these categories. Many of driving styles are a major cause of traffic crashes. Many of these styles can be detected with reasonable accuracy using driving performance. The main idea is to utilize the 2-axis accelerometer that is embedded in most of the GPS tracker devices that are used in vehicle tracking and fleet management to recognize the driving styles. This method can be utilized for vehicle active safety purpose. Fuzzy logic inference system is used in classification of the extracted feature to the predefined driving styles. the power of the Euclidean norm of longitudinal and lateral is used as input to the fuzzy inference system in addition to the speed. Classification of the driving style is real time and almost cost nothing. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Aggressive Driving Style <s> In this paper, we develop a smart phone-based driving behavior evaluation system, named Join Driving, which helps drivers notice how aggressive their driving behaviors are and be aware of the riding comfort level of passengers. The proposed evaluation system is made of two parts: driving events detection and evaluation part and riding comfort level evaluation part. In driving events detection and evaluation part, the proposed system, Join Driving, first presents a model to detect drivers' driving events, based on the data collected from the acceleration, orientation and GPS sensors in smart phones. Then, based on the detected drivers' driving events, Join Driving implements a novel scoring mechanism to quantitatively evaluate how aggressive these driving events are. In riding comfort level evaluation part, the proposed system gives the specific scores to rate passengers' riding comfort level based on ISO 2631. Finally, several practical experiments are conducted to evaluate the effectiveness of the proposed scoring system. <s> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Aggressive Driving Style <s> Driving aggressively increases the risk of accidents. Assessing a person's driving style is a useful way to guide aggressive drivers toward having safer driving behaviors. A number of studies have investigated driving style, but they often rely on the use of self-reports or simulators, which are not suitable for the real-time, continuous, automated assessment and feedback on the road. In order to understand and model aggressive driving style, we construct an in-vehicle sensing platform that uses a smartphone instead of using heavyweight, expensive systems. Utilizing additional cheap sensors, our sensing platform can collect useful information about vehicle movement, maneuvering and steering wheel movement. We use this data and apply machine learning to build a driver model that evaluates drivers' driving styles based on a number of driving-related features. From a naturalistic data collection from 22 drivers for 3 weeks, we analyzed the characteristics of drivers who have an aggressive driving style. Our model classified those drivers with an accuracy of 90.5% (violation-class) and 81% (questionnaire-class). We describe how, in future work, our model can be used to provide real-time feedback to drivers using only their current smartphone. <s> BIB004 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Aggressive Driving Style <s> Surveys of 1217 undergraduate students supported the reliability (inter-item and test–retest) and validity of the Prosocial and Aggressive Driving Inventory (PADI). Principal component analyses on the PADI items yielded two scales: Prosocial Driving (17 items) and Aggressive Driving (12 items). Prosocial Driving was associated with fewer reported traffic accidents and violations, with participants who were older and female, and with lower Boredom Susceptibility and Hostility scores, and higher scores on Agreeableness, Conscientiousness, Openness, and Neuroticism. Aggressive Driving was associated with more frequent traffic violations, with female participants, and with higher scores on Competitiveness, Sensation Seeking, Hostility, and Extraversion, and lower scores on Conscientiousness, Agreeableness, and Openness. The theoretical and practical implications of the PADI's dual focus on safe and unsafe driving are discussed. <s> BIB005
Risky driving and to a large extent risk induced through aggressive driving, has been identified as a major contributing behavioural characteristic that influences road safety for the driver personally, as well as for other drivers travelling in close proximity to the aggressive driver. A study conducted by the American Automobile Association Foundation for Traffic Safety in 2009 found that "as many as 56% of deadly crashes between 2003 and 2007 involve one or more unsafe driving behaviors typically associated with aggressive driving" BIB001 BIB003 . An aggressive driving style is a behavioural pattern or classification of a driver which is associated with risky speeding profiles (irregular, instantaneous and abrupt changes in vehicle speed), improper vehicle position maintenance (quick changes in lateral vehicle position) and inconsistent or excessive acceleration and deceleration (harsh take-off and braking) BIB002 . Another definition of an aggressive style of driving is driver behaviour that intentionally increases the risk of collision and can be due to driver impatience, annoyance, hostility or an attempt to minimise travelling time BIB004 . The characteristics included in the classification of an aggressive driver depend on the application, and the learning or classification algorithm used to place a driver in a certain driving style, but more examples of aggressive behaviour may include tailgating and improper lane changes. The relationship between health risks and aggressive driving also resulted in meticulous research in the field of psychology, as it is believed that aggressive driving is a dysfunctional arrangement of social behaviours and a public safety threat. In  , a questionnaire assessment instrument was designed to measure aspects of aggressive driving on a scale called the aggressive driving behavior scale (ADBS). The ADBS was recently extended BIB005 , to a Prosocial and Aggressive Driving Inventory (PADI), which distinguishes between safe and unsafe driving behaviours. It was found that aggressive driving corresponds to a tendency of increased competitiveness, sensation or adrenalin pursuit, and hostility, and a negligence of conscientiousness, agreeableness, and tolerance. The differences in driving styles and behavioural patterns among drivers with unique personality traits and psychological frameworks are invaluable attributes to pursue in further studies related to unique driver identification.