A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Kalman Filtering <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> Kalman Filtering <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> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Kalman Filtering <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> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Kalman Filtering <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> BIB004 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Kalman Filtering <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> BIB005 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Kalman Filtering <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> BIB006
The Kalman filter is used in driver behaviour studies and therefore a quick introduction to the concept will be provided in this section. The Kalman filter is defined bŷ The optimal linear estimateX k of the state vector is retrieved in the case where the Kalman gain matrix K k is chosen correctly. In BIB003 , a Kalman filter is used to estimate the vehicle output due to driver inputs and the prediction errors are fed into the HMMs. Kalman filters are similarly applied in the driver behaviour applications described in BIB004 BIB005 BIB006 BIB002 where a Kalman filter is used to classify input parameters. According to BIB001 , human behaviour is easily described as a set of dynamic models (Kalman filters) sequenced together by a Markov chain.