A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Intelligent Vehicles Systems and Autonomous Vehicles <s> This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston area. Data from 24 drives of at least 50-min duration were collected for analysis. The data were analyzed in two ways. Analysis I used features from 5-min intervals of data during the rest, highway, and city driving conditions to distinguish three levels of driver stress with an accuracy of over 97% across multiple drivers and driving days. Analysis II compared continuous features, calculated at 1-s intervals throughout the entire drive, with a metric of observable stressors created by independent coders from videotapes. The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level. These findings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such a metric could be used to help manage noncritical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Intelligent Vehicles Systems and Autonomous Vehicles <s> This paper describes an overview of the UTDrive project. UTDrive is part of an on-going international collaboration to collect and research rich multi-modal data recorded for modeling driver behavior for in-vehicle environments. The objective of the UTDrive project is to analyze behavior while the driver is interacting with speech-activated systems or performing common secondary tasks, as well as to better understand speech characteristics of the driver undergoing additional cognitive load. The corpus consists of audio, video, gas/brake pedal pressure, forward distance, GPS information, and CAN-Bus information. The resulting corpus, analysis, and modeling will contribute to more effective speech interactive systems with are less distractive and adjustable to the driver's cognitive capacity and driving situations. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Intelligent Vehicles Systems and Autonomous Vehicles <s> In-Vehicle Corpus and Signal Processing for Driver Behavior is comprised of expanded papers from the third biennial DSPinCARS held in Istanbul in June 2007. The goal is to bring together scholars working on the latest techniques, standards, and emerging deployment on this central field of living at the age of wireless communications, smart vehicles, and human-machine-assisted safer and comfortable driving. Topics covered in this book include: improved vehicle safety; safe driver assistance systems; smart vehicles; wireless LAN-based vehicular location information processing; EEG emotion recognition systems; and new methods for predicting driving actions using driving signals. In-Vehicle Corpus and Signal Processing for Driver Behavior is appropriate for researchers, engineers, and professionals working in signal processing technologies, next generation vehicle design, and networks for mobile platforms. <s> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Intelligent Vehicles Systems and Autonomous Vehicles <s> This paper gives a review of the literature on driver inattention monitoring system for the purpose of active safe driving. In this paper driving inattention is classified into two categories: fatigue and distraction, while fatigue and distraction can also contain many types and levels. Individual difference on inattention phenomenon makes it more complicated to correctly detect and recognize driving inattention. Driver attention monitoring has been intensively researched in recent years and many approaches have been proposed, which include biological signal (EEG, ECG, EOG and sEMG) processing method, subjective report method, and behavior analysis method. This survey reviews a number of promising approaches and provides an overview of recent developments in this domain. The emphasis of this paper is to discuss the various methodologies to monitor driving inattention. We conclude with some thoughts about future directions. <s> BIB004 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Intelligent Vehicles Systems and Autonomous Vehicles <s> The first part of this study develops a general architecture for estimation and prediction of hybrid-state systems. The proposed system utilizes the hybrid characteristics of decision-behaviour coupling of many systems such as the driver and the vehicle; uses estimates of observable parameters to track instantaneous discrete state and predicts the most likely outcome, depending on the discrete model and the observed behaviour of the continuous subsystem. The proposed method is suitable for the scenarios that involve unknown decisions of other individuals, such as lane changes or intersection precedence/access. In the second part, this paper specifically deals with the implementation of the proposed methodology on an intersection safety system, predicting the vehicle behaviours and potential outcomes through traffic intersection scenarios. Driver intentions are tracked and predicted through vehicle behaviour, and possible combinations of intention predictions for different vehicles are interpreted for the safety of the situation. <s> BIB005 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Intelligent Vehicles Systems and Autonomous Vehicles <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> BIB006 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Intelligent Vehicles Systems and Autonomous Vehicles <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> BIB007 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Intelligent Vehicles Systems and Autonomous Vehicles <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> Intelligent Vehicles Systems and Autonomous Vehicles <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> BIB009 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Intelligent Vehicles Systems and Autonomous Vehicles <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> BIB010 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Intelligent Vehicles Systems and Autonomous Vehicles <s> A prototype of a longitudinal driving-assistance system, which is adaptive to driver behavior, is developed. Its functions include adaptive cruise control and forward collision warning/avoidance. The research data came from driver car-following tests in real traffic environments. Based on the data analysis, a driver model imitating the driver's operation is established to generate the desired throttle depression and braking pressure. Algorithms for collision warning and automatic braking activation are designed based on the driver's pedal deflection timing during approach (gap closing). A self-learning algorithm for driver characteristics is proposed based on the recursive least-square method with a forgetting factor. Using this algorithm, the parameters of the driver model can be identified from the data in the manual operation phase, and the identification result is applied during the automatic control phase in real time. A test bed with an electronic throttle and an electrohydraulic brake actuator is developed for system validation. The experimental results show that the self-learning algorithm is effective and that the system can, to some extent, adapt to individual characteristics. <s> BIB011 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Intelligent Vehicles Systems and Autonomous Vehicles <s> In this work, we propose a framework for the recognition and prediction of driver maneuvers by considering holistic cues. With an array of sensors, driver's head, hand, and foot gestures are being captured in a synchronized manner together with lane, surrounding agents, and vehicle parameters. An emphasis is put on real-time algorithms. The cues are processed and fused using a latent-dynamic discriminative framework. As a case study, driver activity recognition and prediction in overtaking situations is performed using a natu- ralistic, on-road dataset. A consequence of this work would be in development of more effective driver analysis and assistance systems. I. INTRODUCTION On-road driving behavior consists of intricate, multi- dimensional dynamics. It is the outcome of many variables that interact at a certain place and time. This motivates the study in this paper, which pursues a holistic approach using multiple cues in the scene. When fusing the different modal- ities in a temporal modeling framework, activity recognition and critical situation prediction can be made more effectively. Furthermore, through studying of temporally discriminative cues, we gain insight into the processes that characterize driver behavior. <s> BIB012
Intelligent vehicle technologies consist of electronic, electromechanical and electromagnetic devices that are controlled by software-based control systems. These intelligent vehicle technologies are applied to vehicle safety systems and self-evaluated feedback systems BIB008 BIB004 . State of the art CAN vehicle bus standards using OBD data and telematics sensors collect as much information from the current vehicle's movement and surrounding vehicles' movement as possible, and estimate real-time feedback to provide solutions and suggestions to the driver. Intelligent vehicles are usually equipped with multiple sensors to enable various processing with ample and representative data collection from various components of the vehicle and the driver behaviour. An example of an intelligent vehicle equipped with multiple sensors is illustrated in Figure 5 , which contains in-vehicle camera sensors, vehicle dynamics sensors, range sensors and a GPS unit BIB012 . Information assessed by intelligent vehicle systems (IVS) might include vehicle orientation estimation using GPS data, relationships between the yaw angle and the steering wheel angle using inertial measurement units (IMU) and lane changing trajectories BIB009 . Driving experience is improved in BIB001 , through the automated calculation of physiological features while driving. This information is obtained by continuously recording electrocardiogram, electromyogram, skin conduction and respiration, and used in IVS to provide feedback to the driver suggesting behaviour to improve relief and relaxation. In order to prevent risk, IVS can divert an incoming cell phone call to the mail box if it is determined that the mental or emotional load of the driver is above average, for instance during high stress, and address the needs of a driver who is more prone to distraction in low stress situations. Many interesting capabilities exist for features of IVS, but driver behaviour modelling and driver style classification play an important role in autonomous vehicle research, which also forms part of vehicle intelligence. In order for an autonomous vehicle to travel safely, the autonomous vehicle should interact with the environment as well as with other vehicles. It is crucial that the autonomous vehicle should interact with human driven vehicles, and therefore the future driver behaviour prediction or driving style information of the approaching human driven vehicle can prove valuable for movement and steering decisions performed by the autonomous vehicle in order to avoid traffic accidents BIB006 . In BIB005 , a hybrid-state system is developed and tested by decision-behaviour coupling of different systems, in this case, coupling of the driver and the autonomous vehicle. Inter vehicle communication is essential for IVS enhancement. As a result, vehicular ad hoc networks (VANETs) originated as an application of mobile ad hoc networks (MANETs), which allow vehicles to communicate with each other in short range proximity. This technology already addresses road safety through feedback to the driver of risky surrounding activities, applying driver behaviour analysis applications using VANET technology, and is implemented in BIB008 . Autonomous vehicles and intelligent vehicle systems make use of vehicle following models for navigation as well as driver safety applications through dangerous following distance detection BIB010 . A driver assistance application which evaluates vehicle following models and patterns recorded for a driver, to provide functionality such as adaptive cruise control and forward collision warning, is introduced in BIB011 . IVS show promising development potential for state of the art driver behaviour analysis applications. Amalgamated results and findings obtained by collaborating researchers from three different regions (Asia, America, and Europe) in the area of driver behaviour analysis, were merged into one product to serve as a global solution BIB007 . Intelligent vehicle systems were used for data collection in their research, and an illustration of the NU-Drive, Uyanik BIB003 , and UTDrive BIB002 vehicles and the equipment(distance sensors, skin conductance sensors, heart rate sensors, cameras, pressure sensors, GPS, steering sensors, EEG sensors, sonar sensors, laser sensors and IMU sensors) fitted to these vehicles are illustrated in Figure 6 . 