scholarly journals A Driver’s Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2670 ◽  
Author(s):  
Yan Li ◽  
Fan Wang ◽  
Hui Ke ◽  
Li-li Wang ◽  
Cheng-cheng Xu
Keyword(s):  
Heart Rate ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Vehicle Dynamic ◽  
Dynamic Data ◽  
Lane Changing ◽  
Dangerous Driving ◽  
Proposed Model ◽  
Driving Risk

Lane changing is considered as one of the most dangerous driving behaviors because drivers have to deal with the traffic conflicts on both the current and target lanes. This study aimed to propose a method of predicting the driving risks during the lane-changing process using drivers’ physiology measurement data and vehicle dynamic data. All the data used in the proposed model were obtained by portable sensors with the capability of recording data in the actual driving process. A hidden Markov model (HMM) was proposed to link driving risk with drivers’ physiology information and vehicle dynamic data. The two-factor indicators were established to evaluate the performances of eye movement, heart rate variability, and vehicle dynamic parameters on driving risk. The standard deviation of normal to normal R–R intervals of the heart rate (SDNN), fixation duration, saccade range, and average speed were then selected as the input of the HMM. The HMM was trained and tested using field-observed data collected in Xi’an City. The proposed model using the data from the physiology measurement sensor can identify dangerous driving state from normal driving state and predict the transition probability between these two states. The results match the perceptions of the tested drivers with an accuracy rate of 90.67%. The proposed model can be used to develop proactive crash prevention strategies.

Hidden Markov Model of Lane-Changing-Based Car-Following Behavior on Freeways using Naturalistic Driving Data

2021 ◽  
pp. 036119812199938
Author(s):  
Li Zhao ◽  
Laurence Rilett ◽  
Mm Shakiul Haque
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Vehicle Body ◽  
Lane Change ◽  
Lane Changing ◽  
Car Following ◽  
Pilot Model ◽  
Naturalistic Driving

This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3 s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86 s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.

Quality Control of Information Engineering Surveillance Based on Baum-Welch Algorithm

2011 ◽  
Vol 63-64 ◽  
pp. 178-181
Author(s):  
Hong Zhi Liu ◽  
Li Gao
Keyword(s):  
Quality Control ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Random Process ◽  
Real Time ◽  
Software Quality ◽  
Hidden Markov ◽  
Proposed Model ◽  
Information Engineering ◽  
Five Elements

A new method of Quality Control for Information Engineering Surveillance based on Hidden Markov Model (HMM) has been proposed and the related model been built by us. The process of information engineering quality surveillance can be seen as a two-layered random process. The five elements of HMM correspond with the process of quality surveillance through abstracting the characteristics of the surveillance process. Software quality can be estimated under the model. In this paper, we divided the five elements. Therefore, the model was improved from single dimension to multi-dimension, trained by Baum-Welch algorithm. Experimental results show that the proposed model proves to be feasible and real-time when it is used for quality control.

scholarly journals An Enhanced Hidden Semi-Markov model for Outlier Detection in Multivariate Datasets

2021 ◽  
Author(s):  
G Manoharan ◽  
K Sivakumar
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Outlier Detection ◽  
Hidden Markov ◽  
Research Work ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Huge Data ◽  
Proposed Model

Outlier detection in data mining is an important arena where detection models are developed to discover the objects that do not confirm the expected behavior. The generation of huge data in real time applications makes the outlier detection process into more crucial and challenging. Traditional detection techniques based on mean and covariance are not suitable to handle large amount of data and the results are affected by outliers. So it is essential to develop an efficient outlier detection model to detect outliers in the large dataset. The objective of this research work is to develop an efficient outlier detection model for multivariate data employing the enhanced Hidden Semi-Markov Model (HSMM). It is an extension of conventional Hidden Markov Model (HMM) where the proposed model allows arbitrary time distribution in its states to detect outliers. Experimental results demonstrate the better performance of proposed model in terms of detection accuracy, detection rate. Compared to conventional Hidden Markov Model based outlier detection the detection accuracy of proposed model is obtained as 98.62% which is significantly better for large multivariate datasets.

scholarly journals Modeling Heart Rate Variability with a HMM-based Neural Network

2002 ◽  
Vol 14 (6) ◽  
pp. 625-632
Author(s):  
Osamu Fukuda ◽  
◽  
Yoshihiko Nagata ◽  
Keiko Homma ◽  
Toshio Tsuji ◽  
...  
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Wavelet Transform ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Mental Workload ◽  
Hidden Markov ◽  
Subjective Rating ◽  
Physical Conditions

This paper proposes a method of modeling heart rate variability combining wavelet transform with a neural network based on a hidden Markov model. The proposed method has the following features: 1. The wavelet transform is used for feature extraction to extract the local change of heart rate variability in the timefrequency domain. 2. A new recurrent neural network incorporating a hidden Markov model is used to model the different patterns of heart rate variability caused by individual variations, physical conditions and so on. In experiments, five subjects were subjected to a mental workload, and the proposed method was used map subjective rating scores of their mental stress and the pattern of heart rate variability. Experiments confirmed that the proposed method achieved highly accurate modeling.

scholarly journals Forecasting of Disassembly Waste Generation under Uncertainties Using Digital Twinning-Based Hidden Markov Model

2021 ◽  
Vol 13 (10) ◽  
pp. 5391
Author(s):  
Yinsheng Yang ◽  
Gang Yuan ◽  
Jiaxiang Cai ◽  
Silin Wei
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Prediction Models ◽  
Hidden Markov ◽  
Real Data ◽  
Gaussian Mixture ◽  
Waste Generation ◽  
Time Data ◽  
Proposed Model ◽  
Comparison Results

Disassembly waste generation forecasting is the foundation for determining disassembly waste treatment and process formulation and is also an important prerequisite for optimizing waste management. The prediction of disassembly waste generation is a complex process which is affected by potential time, environment, and economy characteristic variables. Uncertainty features, such as disassembly amount, disassembly component status, and workshop scheduling, play an important role in predicting the fluctuation of disassembly waste generation. We therefore focus on revealing the trend of waste generation in disassembly remanufacturing that faces significant influences of technology and economic changes to achieve circular industry sustainable development. To dynamically predict the generation of disassembly waste under uncertainty, this work proposes a statistical method driven by a probabilistic model, which integrates the digital twinning, Gaussian mixture, and the hidden Markov model (DG-HMM). First, digital twinning technology is used for real-time data interaction between simulation prediction and decision evaluation. Then, the Gaussian mixture and HMM are used to dynamically predict the generation of disassembly waste. In order to effectively predict the amount of disassembly waste generation, real data collected from a disassembly enterprise are used to train and verify the model. Finally, the proposed model is compared with other general prediction models to illustrate the correctness and feasibility of the proposed model. The comparison results show that DG-HMM has better prediction accuracy for the actual disassembly waste generation.

scholarly journals The Classification of Heartbeats from Two-Channel ECG Signals Using Layered Hidden Markov Model

2021 ◽  
Author(s):  
Azadeh Sadoughi ◽  
Mohammad Bagher Shamsollahi ◽  
Emad Fatemizadeh
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Heart Diseases ◽  
Classification Performance ◽  
Statistical Tool ◽  
Average Accuracy ◽  
Proposed Model

Purpose: Cardiac arrhythmia is one of the most common heart diseases that can have serious consequences. Thus, heartbeat arrhythmias classification is very important to help diagnose and treat. To develop the automatic classification of heartbeats, recent advances in signal processing can be employed. The Hidden Markov Model (HMM) is a powerful statistical tool with the ability to learn different dynamics of the real time-series such as cardiac signals. Materials and Methods: In this study, a hierarchy of HMMs named Layered HMM (LHMM) was presented to classify heartbeats from the two-channel electrocardiograms. For training in the first layer, the morphology of the heartbeats was used as observations, while observations in the second layer were the inference results of the first layer. The performance of the proposed LHMM was evaluated in classifying three types of heartbeat arrhythmias (Atrial premature beats (A), Escape beats (E), Left bundle branch block beats (L)) using fifteen records of the MIT-BIH arrhythmia database. Furthermore, the obtained results of the proposed model were compared with other HMM generalizations. Results: The best average accuracy was achieved 97.10±1.63%. The best sensitivity of 96.8±1.24%, 98.85±0.52%, and 95.64±1.41 were obtained for A, E, and L, respectively. Furthermore, the results of the proposed method were better than other HMM generalizations. Conclusion: Extracting information from time-series dynamics by HMM-based methods has good classification results. The proposed model shows that applying a two-layered HMM can lead to better extraction of information from the observations; therefore, the classification performance of cardiac arrhythmias has been improved using LHMM.

scholarly journals An Experimental Trial: Multi-Robot Therapy for Categorization of Autism Level Using Hidden Markov Model

2021 ◽  
pp. 073563312110404
Author(s):  
Sara Ali ◽  
Faisal Mehmood ◽  
Yasar Ayaz ◽  
Muhammad Sajid ◽  
Haleema Sadia ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Joint Attention ◽  
Rating Scale ◽  
Hidden Markov ◽  
Autism Spectrum ◽  
Childhood Autism ◽  
Childhood Autism Rating Scale ◽  
Proposed Model ◽  
Multi Robot

Several robot-mediated therapies have been implemented for diagnosis and improvement of communication skills in children with Autism Spectrum Disorder. The proposed research uses an existing model i.e., Multi-robot-mediated Intervention System (MRIS) in combination with Hidden Markov Model (HMM) to develop an infrastructure for categorizing the severity of autism in children. The observable states are joint attention type (low, delayed, and immediate) and imitation type (partial, moderate, and full) whereas the non-observable states are (level of autism i.e., (minimal, and mild). The research has been conducted on 12 subjects in which 8 children were in the training session with 72 experiments over 9 weeks, and the remaining 4 subjects were in the prediction test with 25 experiments for 6 weeks. The predicted category was compared with the actual category of autism assessed by the therapist using Childhood Autism Rating Scale. The accuracy of the proposed model is 76%. Further, a statistically significantly moderate Kappa measure of agreement between Childhood Autism Rating Scale and our proposed model has been performed in which n = 25, k = 0.52, and p = 0.009. This research contributes towards the usefulness of Hidden Markov Model integrated with joint attention and imitation modules for categorizing the level of autism using multi-robot therapies.

scholarly journals Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban Environments

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 311 ◽  
Author(s):  
Nyothiri Aung ◽  
Weidong Zhang ◽  
Sahraoui Dhelim ◽  
Yibo Ai
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Risk Prediction ◽  
Latent Variable ◽  
Ad Hoc ◽  
Hidden Markov ◽  
Urban Environments ◽  
Prediction System ◽  
Accident Prediction ◽  
Driving Risk

With the emergence of autonomous vehicles and internet of vehicles (IoV), future roads of smart cities will have a combination of autonomous and automated vehicles with regular vehicles that require human operators. To ensure the safety of the road commuters in such a network, it is imperative to enhance the performance of Advanced Driver Assistance Systems (ADAS). Real-time driving risk prediction is a fundamental part of an ADAS. Many driving risk prediction systems have been proposed. However, most of them are based only on vehicle’s velocity. But in most of the accident scenarios, other factors are also involved, such as weather conditions or driver fatigue. In this paper, we proposed an accident prediction system for Vehicular ad hoc networks (VANETs) in urban environments, in which we considered the crash risk as a latent variable that can be observed using multi-observation such as velocity, weather condition, risk location, nearby vehicles density and driver fatigue. A Hidden Markov Model (HMM) was used to model the correlation between these observations and the latent variable. Simulation results showed that the proposed system has a better performance in terms of sensitivity and precision compared to state of the art single factor schemes.

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