A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Hidden Markov Models <s> We propose a solution for recognizing driving events by hidden Markov models. We believe that temporal patterns of driving events could be used to support the driver in executing a number of driving navigation tasks. In this paper we demonstrate recognition of driving events using hidden Markov models. Recognized events can further used to provide long-term predictions of driving events. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Hidden Markov Models <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> Hidden Markov Models <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> Hidden Markov Models <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> Hidden Markov Models <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> BIB005 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Hidden Markov Models <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> Hidden Markov Models <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> BIB007 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Hidden Markov Models <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> BIB008 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Hidden Markov Models <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> BIB009
According to BIB001 , a HMM is a double stochastic process and is capable of learning structure intelligence from a set of observation sequences. A HMM is therefore suitable for the problem of driver behaviour estimation, which requires a model that can represent the relationship between continuous and discrete state observations for instance the relationship between the driver's actions (changes in orientation) and the actual movement of the vehicle. According to BIB006 , a HMM can satisfy this requirement due to its architecture by completely specifying these dual states, in the instance where actual driving data is used for training the HMM. In BIB004 , HMMs are used in the final phase of the driving manoeuvre recognition and driver fault diagnosis application performed in an urban road scenario. Classification cannot be performed with linear approaches and therefore an ANN is implemented, with the capabilities to recognise and classify driving manoeuvres if the ANN is trained prior to classification. This system is capable of vehicle's manoeuvre sequence identification, test appropriateness for a certain urban road driving event, differentiate between an inattentive, or drunk driver and a normal driver. Simulator data were acquired in the tests performed in this study, and in future research conducted by BIB004 real driving data will be used. The labels classified by the ANN are used to train the HMM. Another driving manoeuvre and driver's performance evaluation application was conduced in BIB002 , this system however used real-time data acquisition in contrast to the prior solution which made use of driving simulator data. In BIB002 video signals of the traffic, the driver's head and the driver's viewpoint, were used in conjunction with signals obtained from the vehicle (vehicle brake, gear, steering wheel angle and speed signals). In this application the predictive power of this solution is 1 s, which means that the HMM can predict the manoeuvre accurately one second prior to the actual action is performed. This is an important area of interest in autonomous vehicle studies. A top-to-bottom (TtB) HMM approach was implemented in BIB005 , by identifying a major manoeuvre and then the discovered manoeuvre is parsed into finer more characteristic manoeuvres. A hierarchical framework is proposed and it is believed that the framework that combines the bottom-to-top (BtT) and TtB approaches, will produce significant recognition improvements. Other applications of HMMs include the addressing of the temporal aspect of multi-level classification to adapt varying driving styles and road conditions, to improve detection rates BIB007 , and driver behaviour estimation, in which continuous vehicle observations are modelled as GMMs for the learning and evaluation of HMMs BIB009 . Improving performance by introducing a HMM as an extent to previous work performed by the authors in BIB008 , lead to a detection rate ranging from 80% to 90% for driver distraction detection and driver behaviour estimation, in which continuous vehicle observations are modelled as GMMs for the learning and evaluation of HMMs BIB009 . A simple and reliable model for driving event recognition applying HMMs was performed in BIB003 , using longitudinal-and lateral acceleration data.