A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <s> In considering the human contribution to accidents, it seems necessary to make a distinction between errors and violations; two forms of aberration which may have different psychological origins and demand different modes of remediation. The present study investigated whether this distinction was justified for self-reported driver behaviour. Five hundred and twenty drivers completed a driver behaviour questionnaire (DBQ) which asked them to judge the frequency with which they committed various types of errors and violations when driving. Three fairly robust factors were identified: violations, dangerous errors, and relatively harmless lapses, respectively. Violations declined with age, errors did not. Men of all ages reported more violations than women. Women, however, were significantly more prone to harmless lapses (or more honest) than men. These findings were consistent with the view that errors and violations are indeed mediated by different psychological mechanisms. Violations require explanation in terms of social and motivational factors, whereas errors (slips, lapses, and mistakes) may be accounted for by reference to the information-processing characteristics of the individual. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <s> In an exploratory postal survey of 711 drivers stratified by age, sex, annual mileage, and accident involvement, decision-making style was measured using a Decision-Making Questionnaire (DMQ) and driving style was assessed using a Driving Style Questionnaire (DSQ). Responses to 21 items of the DMQ formed seven independent and internally coherent dimensions according to a principal components (PC) analysis. These were labelled: control, thoroughness, instinctiveness, social resistance, hesitancy, perfectionism, and idealism. PC analysis also revealed that responses to IS items of the DSQ formed six independent dimensions of driving style. These were labelled: speed, calmness, social resistance, focus, planning, and deviance. Multiple regression analysis indicated that drivers of 60 years and under who scored lower on thoroughness were at greater risk of a traffic accident and that this relationship was mediated by faster driving. This relationship was independent of age, sex, annual mileage, and all other ... <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <s> Motor vehicles greatly influence human life but are also a major cause of death and road congestion, which is an obstacle to future economic development. We believe that by learning driving patterns, useful navigation support can be provided for drivers. In this paper, we present a simple and reliable method for the recognition of driving events using hidden Markov models (HMMs), popular stochastic tools for studying time series data. A data acquisition system was used to collect longitudinal and lateral acceleration and speed data from a real vehicle in a normal driving environment. Data were filtered, normalized, segmented, and quantified to obtain the symbolic representation necessary for use with discrete HMMs. Observation sequences for training and evaluation were manually selected and classified as events of a particular type. An appropriate model size was selected, and the model was trained for each type of driving events. Observation sequences from the training set were evaluated by multiple models, and the highest probability decides what kind of driving event this sequence represents. The recognition results showed that HMMs could recognize driving events very accurately and reliably. <s> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <s> This paper presents investigations into the role of computer-vision technology in developing safer automobiles. We consider vision systems, which cannot only look out of the vehicle to detect and track roads and avoid hitting obstacles or pedestrians but simultaneously look inside the vehicle to monitor the attentiveness of the driver and even predict her intentions. In this paper, a systems-oriented framework for developing computer-vision technology for safer automobiles is presented. We will consider three main components of the system: environment, vehicle, and driver. We will discuss various issues and ideas for developing models for these main components as well as activities associated with the complex task of safe driving. This paper includes a discussion of novel sensory systems and algorithms for capturing not only the dynamic surround information of the vehicle but also the state, intent, and activity patterns of drivers. <s> BIB004 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <s> INTRODUCTION: Through a meta-analysis, this study investigated the relation of errors and violations from the Driver Behaviour Questionnaire (DBQ) to accident involvement. METHOD: We identified 174 studies using the DBQ, and a correlation of self-reported accidents with errors could be established in 32 samples and with violations in 42 samples. RESULTS: The results showed that violations predicted accidents with an overall correlation of .13 when based on zero-order effects reported in tabular form, and with an overall correlation of .07 for effects reported in multivariate analysis, in tables reporting only significant effects, or in the text of a study. Errors predicted accidents with overall correlations of .10 and .06, respectively. The meta-analysis also showed that errors and violations correlated negatively with age and positively with exposure, and that males reported fewer errors and more violations than females. Supplementary analyses were conducted focusing on the moderating role of age, and on predicting accidents prospectively and retrospectively. Potential sources of bias are discussed, such as publication bias, measurement error, and consistency motif. IMPACT ON INDUSTRY: The DBQ is a prominent measurement scale to examine drivers' self-reported aberrant behaviors. The present study provides information about the validity of the DBQ and therefore has strong relevance for researchers and road safety practitioners who seek to obtain insight into driving behaviors of a population of interest. Language: en <s> BIB005 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <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> BIB006 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <s> Currently there are many research focused on using smartphone as a data collection device. Many have shown its sensors ability to replace a lab test bed. These inertial sensors can be used to segment and classify driving events fairly accurately. In this research we explore the possibility of using the vehicle's inertial sensors from the CAN bus to build a profile of the driver to ultimately provide proper feedback to reduce the number of dangerous car maneuver. Braking and turning events are better at characterizing an individual compared to acceleration events. Histogramming the time-series values of the sensor data does not help performance. Furthermore, combining turning and braking events helps better differentiate between two similar drivers when using supervised learning techniques compared to separate events alone, albeit with anemic performance. <s> BIB007 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <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> BIB008 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <s> Traffic safety and energy efficiency of vehicles are strictly related to driver’s behavior. The scientific literature has investigated on some specific dynamic parameters that, among the others, can be used as a measure of unsafe or aggressive driving style such as longitudinal and lateral acceleration of vehicle. Moreover, the use of modern mobile devices (smartphones and tablets), and their internal sensors (GPS receivers, three-axes accelerometers), allows road users to receive real time information and feedback that can be useful to increase awareness of drivers and promote safety. This paper focuses on the development of a prototype mobile application that can evaluate the grade of safety that drivers are keeping on the road by measuring of accelerations (longitudinal and lateral) and warning for users when it can be convenient to correct their driving style. The aggressiveness is evaluated by plotting vehicle’s acceleration on a g-g diagram specially studied and designed, where horizontal and lateral acceleration is displayed inside areas of “Good Driving Style”. Several experimental tests were carried out with different drivers and cars in order to estimate the system accuracy and the usability of the application. This work is part of the wider research project M2M, Mobile to Mobility: Information and communication technology systems for road traffic safety (PON National Operational Program for Research and Competitiveness 2007-2013) which is based on the use of mobile sensor computing systems for giving real-time information in order to reduce risks and to make the transportation system more safe and comfortable. <s> BIB009 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <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> BIB010 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <s> Transport is a key variable in the growth of the Kenyan economy. With such a rapid increase in urban population, there has been an increase in demand for mobility, and with it, an increase in motorized vehicle ownership. Much of the developing world is experiencing rapid economic growth. Motor vehicle fleet is increasing at a rapid pace in many cities around the world creating an infrastructure backlog and imposing constraint on economic development. The general objective of this study was to assess the effect of the motor vehicle congestion on the economy of Kenya. A case study was made of Nairobi City County. The objectives of the study was to establish the effect of transport infrastructure and Government Policy on the economy as influenced by traffic congestion in Nairobi County. The study was limited to the residents of Nairobi County. The study targeted city county planners, city county traffic office and city economic and finance office. The study was targeting the years between 2011 and 2013.The Study used the descriptive research design.The study population were respondents from city county planner’s office, city county traffic office and city economic and finance office which had 108 respondents and sample size of 54 which is 50% of the total target population. Stratified random sampling was used as a sampling design then simple random sampling to ensure that all the subgroups were included in the study. The data collection instrumentused was the questionnaire. Then data wasanalysed by use of the tables, and pie charts, that are qualitative in nature.The study concluded that motor vehicle congestion has a direct effect on the economy of Kenya. The research concluded that the lost man-hours due to people sitting in traffic was causing a slowdown in the economy’s growth and that investors are relocating because of the high cost of labour in Kenya. The study re affirmed the importance on public transport to the Kenyan economy and that it is the nerve center that holds it together, however it is poorly managed and governed and thus the stakeholders need to come up with better strategies to make it viable. Key Words: Motor Vehicle, Congestion, Economic Performance <s> BIB011 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <s> Road accidents cause more deaths than homicides in Latin America, nevertheless it is not highlighted as a major concern by media and society. World Health Organization put this issue in high priority by releasing the Decade of Action in Road Safety that establishes five pillars to guide national road safety plans and activities. This paper addresses the drawbacks in the implementation of these actions in Latin American countries and its implications to achieve a sustainable development. The main concerns are: lack of empowerment of the road safety management organisations; lower vehicular standards; corruption related to the enforcement of traffic safety laws to and to the construction of safer roads; absence of safety vehicular inspections; vehicle fleet increase, decrease of public transportation demand; and the absence of a safety culture. Without facing these problems, sustainable development in Latin America will be impaired, once road safety is a fundamental link to achieve sustainability. <s> BIB012 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Introduction <s> The Driver Behavior Questionnaire (DBQ) is a self-report measure of driving behavior that has been widely used over more than 20 years. Despite this wealth of evidence a number of questions remain, including understanding the correlation between its violations and errors sub-components, identifying how these components are related to crash involvement, and testing whether a DBQ based on a reduced number of items can be effective. We address these issues using a bifactor modeling approach to data drawn from the UK Cohort II longitudinal study of novice drivers. This dataset provides observations on 12,012 drivers with DBQ data collected at .5, 1, 2 and 3 years after passing their test. A bifactor model, including a general factor onto which all items loaded, and specific factors for ordinary violations, aggressive violations, slips and errors fitted the data better than correlated factors and second-order factor structures. A model based on only 12 items replicated this structure and produced factor scores that were highly correlated with the full model. The ordinary violations and general factor were significant independent predictors of crash involvement at 6 months after starting independent driving. The discussion considers the role of the general and specific factors in crash involvement. <s> BIB013
Driving-and road safety are current and growing problems with global dimensions. According to the global status report on road safety conducted by the World Health Organisation (WHO) in 2013, 1.24 million traffic-related fatalities occur annually worldwide, currently the leading cause of death for people aged 15-29 years  . As a result of the increased need for mobility in developing countries the continuous expansion of vehicle manufacturing is evident. The evolution of the global vehicle fleet causes an infrastructure backlog BIB011 , which in turn is responsible for increased traffic safety risks and accident prevalence. Driver assistance and safety awareness programmes have been an area of focus to minimise road safety incidents, and since the WHO launched their "Decade of Action in Road Safety (2011-2020)" programme, a remarkable improvement in road safety has been noticeable BIB012 . Despite the growth of 15% in the annual number of registered vehicles from 2007 to 2013, the annual fatalities remained stable in the region of 1.2 million over the same period BIB004 . However, a saturation in the number of fatalities is not good enough and a reduction should be observed instead. High priority is given to traffic safety improvements by government agencies and major automobile manufacturers across the globe to address this problem, and innovation in driver assistance is currently in demand. The contribution of human behaviour towards traffic accidents is an important area of interest in the remedial attempts to address the global road safety problem. A clear distinction needs to be made between errors and violations to ameliorate traffic accidents, due to the different psychological origins of these components of human behaviour, as well as the dissimilar forms of remediation BIB001 . The Driver Behaviour Questionnaire (DBQ) is a metric widely used in driver behaviour studies over the past few decades, and focus falls on the correlation between the sub-components of both violations and errors, and the degree to which these components are related to crash involvement BIB013 . Around 12 ,000 novice drivers were tested repeatedly with the DBQ in the first six months after they passed their driver's licence tests and analysis of the data accumulated from this experiment proved the trustworthiness of the DBQ as a driver behaviour measure in traffic accident prediction BIB013 . The integrity and validity of the DBQ has been confirmed BIB005 . The statistical correlation between driver behaviour and crash involvement is particularly related to individual variability associated with numerous parameters such as age, gender and geographic locations BIB009 . Interestingly, DBQ results shown that violations declined with age as opposed to errors and the prevalence of violation is higher in men than in woman BIB001 BIB005 . Results obtained using the DBQ are used as predictor of self-reported traffic accidents, but the question regarding the trustworthiness of the metric is raised due to the reporting bias and the non-real-time recording of the measurement, which serves a remedial instead of a reactive purpose, and therefore real-time evaluation of driver behaviour information and patterns are investigated as a solution to mitigate risk through driver assistance and early warning of predicted dangerous situations. One of the primary focusses of this investigation is to analyse research in the area of driver behaviour solutions in order to address road safety problems. This is achieved through the identification and understanding of the relationship between driver behaviour and road safety, and the classification of drivers using their propensity to risk BIB010 . The relationship between the driver and the vehicle control, and the sensitivity of the driver to complex driving environments or situations (e.g., weather, traffic) are major contributing factors towards traffic accidents BIB003 . According to BIB002 , traffic accident involvement is more closely related to human judgement and decision-making than the mere inability to control the vehicle, and therefore, the focus of driver behaviour and decision-making patterns became a popular research area in road-safety applications. The authors in BIB002 approached the study of decision-making in two different ways: To analyse the beliefs and values that constitute the decision process, and to evaluate the style of decision-making based on individual habits and personality traits. The second approach proved to be representative and measurable in applications through the classification of drivers into predefined driving styles. The aim of the study is to investigate the possibilities of utilizing the aforementioned driving style information or intelligence, in order to construct driver profiles that might be able to uniquely represent and identify one driver from another in the same vehicle based upon their driver behaviour differences in similar situations. Therefore, four main driving styles were distinguished from the various interpretations of driving styles found in the literature based on the measurable differences between them. This is done in an attempt to move beyond the mere classification of a driver in a certain "risk profile" or "behavioural group" and to combine driving style information in different situations and phases of driving to construct a unique "driver behaviour fingerprint". The unique identification of a driver can be useful in many commercial applications, especially in the insurance sector where a driver's risk profile based upon his driving behaviour can be extrapolated to different environments that account for prospective personal risk, such as insurance and loan qualification, as well as insurance premium calculation and adjustment over time. Vehicle theft and dishonesty during insurance claims can also be addressed with the introduction of unique driver profile construction and accurate driver distinguishing applications. The four types of driving styles investigated in this study namely, aggressive driving style, inattentive driving style, drunk driving style and "normal" or safe driving style, were particularly identified due to the differences between the classification processes implemented, the input data analysed and the algorithms used to predict and label driver behaviour into specific driving styles. These driving styles are adopted from well-researched driver behaviour types but specific focus fell on the interpretations of BIB007 BIB006 BIB008 . Research in the areas of driver behaviour monitoring, navigation task detection, driving style recognition and prediction are constantly growing as the need for innovative, immediate and accurate solutions develop with the excel in vehicle fleet and road infrastructure industries. The body of knowledge around driver behaviour research is subdivided in this study according to differences in application and sensor platforms over a range of fields and industries. This review further categorises the driver behaviour application areas into principal groups of which methods and results are compared in order to enhance the understanding of the implementation and application of driver behaviour analysis. The artificial intelligence (AI) and machine learning (ML) algorithms applied in advanced and recent driver behaviour applications are defined properly and categorized according to their intended application in order to identify correlations between the type of algorithms applied and the applications they serve. This was performed in order to compile a scope of possible algorithms to investigate in the proposed problem of unique driver identification. The difficulties experienced in conducting this study included the scope of the research area and the depth of investigation that were necessary, as well as the categorization of algorithms according to the respective applications they serve. The reason therefore, was due to the use of similar solutions, concepts and design architectures to a range of different industries and applications. The techniques followed and algorithmic uses differ depending on the type of input data the authors had access to and the accuracy of the data that was required in different approaches, and therefore, identifying the studies from which techniques could be adopted for further research and experimental purposes, posed further challenges. This study was further complicated due to many applications only utilizing the state of the art technologies equipped in the latest vehicles, and neglects to propose similar solution for existing vehicles with outdated technologies which might be the origin of the road safety problems. Smartphone applications or after-market products might be possible equipment to investigate further to address this complication. Research solutions and experiments are very limited to the specific problem they address and due to the great variability of driver behaviour, assumptions and results need to be substantially motivated and proven. Driver behaviour is a well-researched area with some unexplored possibilities but complications such as complexity of models and real-time data acquisition and processing need to be taken into account. Fuzzy logic systems seem to be a valuable area for initial investigation as scoring techniques can easily be extended to evaluate multiple areas of driver behaviour to infer conclusions (in the form of average scores) in a certain driving situation.