A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> We propose that many human behaviors can be accurately described as a set of dynamic modes (e.g., Kalman filters) sequenced together by a Markov chain. We then use these dynamic Markov models to recognize human behaviors from sensory data and to predict human behaviors over a few seconds time. To test the power of this modeling approach, we report an experiment in which we were able to achieve 95% accuracy at predicting automobile drivers' subsequent actions from their initial preparatory movements. Language: en <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> In this paper we describe our SmartCar testbed: a real-time data acquisition system and a machine learning framework for modeling and recognizing driver maneuvers at a tactical level, with special emphasis on how the context affects the driver's performance. The perceptual input is multimodal: four video signals capture the contextual traffic, the driver's head and the driver's viewpoint; and a real-time data acquisition system records the car's brake, gear, steering wheel angle, speed and acceleration throttle signals. Over 70 drivers have driven the SmartCar for 1.25 hours in the greater Boston area. Graphical models, HMM and coupled HMM, have been trained using the experiment driving data to create models of seven different driver maneuvers: passing, changing lanes right and left, turning right and left, starting and stopping. We show that, on average, the predictive power of our models is of 1 second before the maneuver starts taking place. Therefore, these models would be essential to facilitate operating mode transitions between driver and driver assistance systems, to prevent potential dangerous situations and to create more realistic automated cars in car simulators. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <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> Underlying Algorithms <s> Hidden Markov models (HMM) are used to identify a vehicle's manoeuvre sequence and its appropriateness for a given urban road driving situation. One of the novel aspects of this work has been the development of an efficient signal modelling approach to form a context-aware, flexible system which proved to respond well in urban road scenarios, especially in situations where the driver is likely to have an accident due to impaired performance. Another contribution has been to clarify how HMMs can be used not just to recognize vehicle manoeuvres but also to distinguish an impaired driver from a normal one in complex driving contexts. The system has worked well on simulator data and is about to be implemented in the real conditions of an urban trajectory. <s> BIB004 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> Back-propagation trained neural networks, as well as extreme learning machine (ELM) were used to predict car driverpsilas steering behavior, based on road curvature, velocity and acceleration of a car. Predictions were performed using real-road data, obtained on a test car in a country-road scenario. We made a simplification using gyroscopically measured curvature of the road instead of visually extracted curvature measures. It was found that an optimum exists how far one has to look onto a curvature signal, according to neural network prediction accuracy. Velocity and acceleration did not improve steering signal prediction accuracy in our framework. Traditional neural networks and ELM performed similarly in terms of prediction errors. <s> BIB005 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> In this investigation, driver behavior signals are modeled using Hidden Markov Models (HMM) in two different and complementary approaches. The first approach considers isolated maneuver recognition with model concatenation to construct a generic route (bottom-to-top), whereas the second approach models the entire route as a dasiaphrasepsila and refines the HMM to discover maneuvers and parses the route using finer discovered maneuvers (top-to-bottom). By applying these two approaches, a hierarchical framework to model driver behavior signals is proposed. It is believed that using the proposed approach, driver identification and distraction detection problems can be addressed in a more systematic and mathematically sound manner. We believe that this framework and the initial results will encourage more investigations into driver behavior signal analysis and related safety systems employing a partitioned sub-module strategy. <s> BIB006 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> This paper presents a general approach for recognition of driving maneuvers in advanced driver assistance systems (ADAS). Such systems often rely on the identification of driving maneuvers (overtaking, left turn at intersections, etc.) to improve the prediction of potential collisions or to trigger appropriate support for the driver. The proposed maneuver recognition approach combines a fuzzy rule base to model basic maneuver elements and probabilistic finite-state machines to capture all possible sequences of basic elements that constitute a driving maneuver. The proposed method is specifically tailored to ADAS requirements because of its low computational complexity, its flexibility and its straight-forward design based on easily comprehensible logical rules. In addition, we propose a suitable training method to optimize the fuzzy rule base. Our approach is evaluated on the recognition of turn maneuvers. Experiments on real data from a test vehicle demonstrate the feasibility of the proposed method. <s> BIB007 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <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> BIB008 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> This study proposes a probabilistic decision-making model for driving decisions. The decision-making process that is modeled stochastically is part of the Human Driver Model developed in an earlier study, in which perception, world-model and reflexive behavior are represented as separate modules. Finite-state machine design guidelines for decision-making models are provided to maximize state observability and resolution while maintaining a manageable size for state-machine. Two decision-making models useful for estimation and prediction of driver behavior are presented and one scenario-safety estimation application that uses the proposed decision-making model is given to illustrate the proposed methodology. <s> BIB009 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> The authors present a cyber-physical systems related study on the estimation and prediction of driver states in autonomous vehicles. The first part of this study extends on a previously developed general architecture for estimation and prediction of hybrid-state systems. The extended system utilizes the hybrid characteristics of decision-behavior coupling of many systems such as the driver and the vehicle; uses Kalman Filter estimates of observable parameters to track the instantaneous discrete state, and predicts the most likely outcome. Prediction of the likely driver state outcome depends on the higher level discrete model and the observed behavior of the continuous subsystem. Two approaches to estimate the discrete driver state from filtered continuous observations are presented: rule based estimation, and Hidden Markov Model (HMM) based estimation. Extensions to a prediction application is described through the use of Hierarchical Hidden Markov Models (HHMMs). The proposed method is suitable for scenarios that involve unknown decisions of other individuals, such as lane changes or intersection precedence/access. An HMM implementation for multiple tasks of a single vehicle at an intersection is presented along with preliminary results. <s> BIB010 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> This paper considers a comprehensive and collaborative project to collect large amounts of driving data on the road for use in a wide range of areas of vehicle-related research centered on driving behavior. Unlike previous data collection efforts, the corpora collected here contain both human and vehicle sensor data, together with rich and continuous transcriptions. While most efforts on in-vehicle research are generally focused within individual countries, this effort links a collaborative team from three diverse regions (i.e., Asia, American, and Europe). Details relating to the data collection paradigm, such as sensors, driver information, routes, and transcription protocols, are discussed, and a preliminary analysis of the data across the three data collection sites from the U.S. (Dallas), Japan (Nagoya), and Turkey (Istanbul) is provided. The usability of the corpora has been experimentally verified with a Cohen's kappa coefficient of 0.74 for transcription reliability, as well as being successfully exploited for several in-vehicle applications. Most importantly, the corpora are publicly available for research use and represent one of the first multination efforts to share resources and understand driver characteristics. Future work on distributing the corpora to the wider research community is also discussed. <s> BIB011 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <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> BIB012 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <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> BIB013 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> In this paper, we propose an approach to understand the driver behavior using smartphone sensors. The aim for analyzing the sensory data acquired using a smartphone is to design a car-independent system which does not need vehicle mounted sensors measuring turn rates, gas consumption or tire pressure. The sensory data utilized in this paper includes the accelerometer, gyroscope and the magnetometer. Using these sensors we obtain position, speed, acceleration, deceleration and deflection angle sensory information and estimate commuting safety by statistically analyzing driver behavior. In contrast to state of the art, this work uses no external sensors, resulting in a cost efficient, simplistic and user-friendly system. <s> BIB014 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> With the proliferation of smart portable devices, more people have started using them within the vehicular environment while driving. Although these smart devices provide a variety of useful information, using them while driving significantly affects the driver's attention towards the road. This can in turn cause driver distraction and lead to increased risk of crashes. On the positive side, these devices are equipped with powerful sensors which can be effectively utilized towards driver behavior analysis and safety. This study evaluates the effectiveness of portable sensor information in driver assistance systems. Available signals from the CAN-bus are compared with those extracted from an off-the-shelf portable device for recognizing patterns in driving sub-tasks and maneuvers. Through our analysis, a qualitative feature set is identified with which portable devices could be employed to prune the search space in recognizing driving maneuvers and possible instances of driver distraction. An absolute improvement of 15% is achieved with portable sensor information compared to CAN-bus signals, which motivates further study of portable devices to build driver behavior models for driver assistance systems. <s> BIB015 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> There is accumulating evidence that driver distraction is a leading cause of vehicle crashes and incidents. In particular, increased use of so-called in-vehicle information systems (IVIS) and partially autonomous driving assistance systems (PADAS) have raised important and growing safety concerns. Thus, detecting the driver's state is of paramount importance, to adapt IVIS and PADAS accordingly, therefore avoiding or mitigating their possible negative effects. The purpose of this paper is to show a method for the nonintrusive and real-time detection of visual distraction, using vehicle dynamics data and without using the eye-tracker data as inputs to classifiers. Specifically, we present and compare different models that are based on well-known machine learning (ML) methods. Data for training the models were collected using a static driving simulator, with real human subjects performing a specific secondary task [i.e., a surrogate visual research task (SURT)] while driving. Different training methods, model characteristics, and feature selection criteria have been compared. Based on our results, using a support vector machine (SVM) has outperformed all the other ML methods, providing the highest classification rate for most of the subjects. Potential applications of this paper include the design of an adaptive IVIS and of a “smarter” PADAS. <s> BIB016 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> This paper presents an innovative idea for the classification of individual drivers. The classification is based on each driver's driving features like, ratio of indicators to turns, number of brakes, number of time horn used, average gear, average speed, maximum speed and gear. K-means and hierarchical clustering is used to separate out the slow, normal and fast driving styles based on recorded data. Experimental result shows that k-means outperformed hierarchical clustering for recorded multi-attribute data. <s> BIB017 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> Profiling driving behavior has become a relevant aspect in fleet management, automotive insurance and eco-driving. Detecting inefficient or aggressive drivers can help reducing fleet degradation, insurance policy cost and fuel consumption. In this paper, we present a Fuzzy-Logic based driver scoring mechanism that uses smartphone sensing data, including accelerometers and GPS. In order to evaluate the proposed mechanism, we have collected traces from a testbed consisting in 20 vehicles equipped with an Android sensing application we have developed to this end. The results show that the proposed sensing variables using smartphones can be merged to provide each driver with a single score. <s> BIB018 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <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> BIB019 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <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> BIB020 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> Nowadays, smartphones are widely used in the world, and generally, they are equipped with many sensors. In this paper we study how powerful the low-cost embedded IMU and GPS could become for Intelligent Vehicles. The information given by accelerometer and gyroscope is useful if the relations between the smartphone reference system, the vehicle reference system and the world reference system are known. Commonly, the magnetometer sensor is used to determine the orientation of the smartphone, but its main drawback is the high influence of electromagnetic interference. In view of this, we propose a novel automatic method to calibrate a smartphone on board a vehicle using its embedded IMU and GPS, based on longitudinal vehicle acceleration. To the best of our knowledge, this is the first attempt to estimate the yaw angle of a smartphone relative to a vehicle in every case, even on non-zero slope roads. Furthermore, in order to decrease the impact of IMU noise, an algorithm based on Kalman Filter and fitting a mixture of Gaussians is introduced. The results show that the system achieves high accuracy, the typical error is 1%, and is immune to electromagnetic interference. <s> BIB021 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <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> BIB022 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> Abstract This paper proposes a rule-based neural network model to simulate driver behavior in terms of longitudinal and lateral actions in two driving situations, namely car-following situation and safety critical events. A fuzzy rule based neural network is constructed to obtain driver individual driving rules from their vehicle trajectory data. A machine learning method reinforcement learning is used to train the neural network such that the neural network can mimic driving behavior of individual drivers. Vehicle actions by neural network are compared to actions from naturalistic data. Furthermore, this paper applies the proposed method to analyze the heterogeneities of driving behavior from different drivers’ data. Driving data in the two driving situations are extracted from Naturalistic Truck Driving Study and Naturalistic Car Driving Study databases provided by the Virginia Tech Transportation Institute according to pre-defined criteria. Driving actions were recorded in instrumented vehicles that have been equipped with specialized sensing, processing, and recording equipment. <s> BIB023 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> We present a framework for the estimation of driver behavior at intersections, with applications to autonomous driving and vehicle safety. The framework is based on modeling the driver behavior and vehicle dynamics as a hybrid-state system (HSS), with driver decisions being modeled as a discrete-state system and the vehicle dynamics modeled as a continuous-state system. The proposed estimation method uses observable parameters to track the instantaneous continuous state and estimates the most likely behavior of a driver given these observations. This paper describes a framework that encompasses the hybrid structure of vehicle-driver coupling and uses hidden Markov models (HMMs) to estimate driver behavior from filtered continuous observations. Such a method is suitable for scenarios that involve unknown decisions of other vehicles, such as lane changes or intersection access. Such a framework requires extensive data collection, and the authors describe the procedure used in collecting and analyzing vehicle driving data. For illustration, the proposed hybrid architecture and driver behavior estimation techniques are trained and tested near intersections with exemplary results provided. Comparison is made between the proposed framework, simple classifiers, and naturalistic driver estimation. Obtained results show promise for using the HSS-HMM framework. <s> BIB024 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> In recent year many researcher and industries are working on VANET and trying to implement the concepts in real world. Many VANET systems are proposed and tested on simulation but very few inventors implemented it. Vehicular ad hoc networks (VANETs) are being developed to provide on-demand wireless communication infrastructure among vehicles and authorities. Such an infrastructure is expected to deliver multiple road safety and driving assistance applications. Vehicles will be equipped with sensors and communication devices that will allow them to cooperate with each other. Vehicles can exchange different type of information as per requirements on demand for specified application. With the purpose of supporting and improving data collection and distribution, in this paper a smart phone-based platform is designed that exploits low-cost dedicated hardware to interact with sensors on board and in the vehicle surroundings. <s> BIB025 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> Nowadays more and more driver assistance systems are implemented in cars. By adapting the system to the driving style of the driver, the acceptance of the driver to such a system could be enhanced. In this paper a system for online driving style recognition is designed. It is implemented in Matlab/Simulink and uses fuzzy logic for identifying the current driving style. It is fully parameterisable via a central parameter file and could therefore be adapted to nearly every car. The recognition was tested by using a vehicle dynamics simulation with 68% correct classifications over time. <s> BIB026 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> Today's smartphones and mobile devices typically embed advanced motion sensors. Due to their increasing market penetration, there is a potential for the development of distributed sensing platforms. In particular, over the last few years there has been an increasing interest in monitoring vehicles and driving data, aiming to identify risky driving maneuvers and to improve driver efficiency. Such a driver profiling system can be useful in fleet management, insurance premium adjustment, fuel consumption optimization or CO2 emission reduction. In this paper, we analyze how smartphone sensors can be used to identify driving maneuvers and propose SenseFleet, a driver profile platform that is able to detect risky driving events independently from the mobile device and vehicle. A fuzzy system is used to compute a score for the different drivers using real-time context information like route topology or weather conditions. To validate our platform, we present an evaluation study considering multiple drivers along a predefined path. The results show that our platform is able to accurately detect risky driving events and provide a representative score for each individual driver. <s> BIB027 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Underlying Algorithms <s> Driving sleepiness is still a major causes of traffic accidents. Individual drivers, under various conditions, act and respond in different manners. This article presents the attempt of a straight-line driving simulator study that examined the effects of driver behavior style differences and individual differences on driver sleepiness detection which is based on driving performance measures. A total of 15 drivers who were classified into two categories through subjective assessment based on a Driver Behavior Questionnaire participated in driving simulator experiments. A total of 18 detection models, including 15 SE models for each subject, an A model for the aggressive drivers, an NA model for the non-aggressive drivers, and a G model for all experiment participants, were developed using support vector machine method based on driving performance characteristic parameters. The results show that the G model is not suitable for all drivers due to its lower mean accuracy of 69.88% (standard deviation = 7.70%) and higher standard deviation. The SE models for each subject show the best detection accuracy performance of 84.26% (standard deviation = 5.38%); however, it is impossible to set up a special detection model for every individual driver. The SD models on different style categories show an accuracy value of 77.54% (standard deviation = 5.78%). The results demonstrate that driver style differences as well as individual differences have great effects on driver sleepiness detection (F = 19.148, p \ 0.000). <s> BIB028
In this section the popular algorithms (AI algorithms, ML algorithms and statistical algorithms), and their roots and applications in driver behaviour and driving style studies are summarised. Significant results are provided when applicable, with the intention to identify suitable algorithms to pursue in unique driver identification problems. It was noticed that there is a relationship between the algorithm used and the application it serves. Artificial Neural Networks (ANNs) find its application in drowsiness detection BIB008 , driver distraction detection BIB016 and steering behaviour prediction BIB005 and is mainly based on computer vision techniques. A frequency domain solution to address temporal input complications, is performed using a Fast Fourier Transform (FFT) in a drowsiness detection problem BIB008 . Clustering techniques were used for driving style distinction and labelling in BIB027 and k-means clustering, in specific, for individual driver identification BIB017 and road condition monitoring  . State Machine approaches are performed in driver manoeuvre recognition BIB007 and driver decision making modelling BIB009 in the form of a Finite State Machine (FSM), and in autonomous vehicle studies BIB024 using a Hybrid State Machine (HSM) form. Fuzzy Logic implementations include driver fatigue and distraction identification applications in BIB016 , scoring applications in BIB027 BIB018 and driving style recognition applications in BIB013 BIB025 BIB026 . Hidden Markov Models (HMM) are primarily applied in driving behaviour estimation BIB010 BIB024 , driving manoeuvre recognition BIB003 BIB004 BIB002 , driving performance assessment BIB002 and driver distraction detection BIB011 studies. A Bayesian technique is applied in a driver behaviour detection application to address the problem of incomplete data in BIB019 . Decision Trees are utilized in BIB020 for result confidence estimation in an information fusion application for drowsiness detection. Gaussian Mixture Models (GMMs) was investigated in driver distraction detection BIB011 , driving manoeuvre detection BIB006 and road condition monitoring BIB021 applications. Dynamic Time Warping (DTW) is applied in driver risk profile classification BIB014 and driver assistance or safety awareness BIB012 applications. A Kalman filter was used in the process to predict and model human behaviour in BIB001 . Support Vector Machines (SVM) are used for driving event recognition BIB022 BIB015 systems, as well as in drowsiness detection BIB028 and vehicle state estimation  systems. Genetic Algorithms are applied in the calibration process of car following models as can be seen in BIB023 . Promising algorithms to further investigate in future research include Fuzzy Logic based techniques, HMM implementation and SVM based applications.