A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Gaussian Mixture 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> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Gaussian Mixture 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> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Gaussian Mixture Models <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> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Gaussian Mixture 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> BIB004
A mixture model is used in order to make statistical inferences about the properties of a sub-populations in the case where observations are made only on the pooled population, without sub-population identity information. GMMs are the mixture of Gaussian probability distribution functions (PDFs). In BIB004 , a GMM was applied of the form of to represent such PDFs of continuous vehicle data, where c represents the mixture coefficient for the k-th mixture in the j-th state. N represents the pdf of a normal distribution with mean µ and covariance U measured at observation o. The mixture coefficient c satisfies the constraints in such that the pdf is normalised to GMMs are used to perform driver distraction detection as a binary decision problem BIB002 and driver manoeuvre detection (optimal using a GMM with 64 mixtures) BIB001 . Road condition monitoring applications also apply GMM to classify road surface as either even or uneven using the yaw angle of the vehicle to measure the road surface BIB003 .