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.

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.

scholarly journals A Transformer-Based Hierarchical Variational Autoencoder Combined Hidden Markov Model for Long Text Generation

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1277
Author(s):  
Kun Zhao ◽  
Hongwei Ding ◽  
Kai Ye ◽  
Xiaohui Cui
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Latent Variables ◽  
Latent Variable ◽  
Hidden Markov ◽  
Variable Structure ◽  
Text Generation ◽  
Short Text ◽  
Variational Autoencoder ◽  
Generation Network

The Variational AutoEncoder (VAE) has made significant progress in text generation, but it focused on short text (always a sentence). Long texts consist of multiple sentences. There is a particular relationship between each sentence, especially between the latent variables that control the generation of the sentences. The relationships between these latent variables help in generating continuous and logically connected long texts. There exist very few studies on the relationships between these latent variables. We proposed a method for combining the Transformer-Based Hierarchical Variational Autoencoder and Hidden Markov Model (HT-HVAE) to learn multiple hierarchical latent variables and their relationships. This application improves long text generation. We use a hierarchical Transformer encoder to encode the long texts in order to obtain better hierarchical information of the long text. HT-HVAE’s generation network uses HMM to learn the relationship between latent variables. We also proposed a method for calculating the perplexity for the multiple hierarchical latent variable structure. The experimental results show that our model is more effective in the dataset with strong logic, alleviates the notorious posterior collapse problem, and generates more continuous and logically connected long text.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing
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