Modeling and Prediction of Driver-Vehicle-Unit Velocity Using Adaptive Neuro-Fuzzy Inference System in Real Traffic Flow

Moghaddam, Iman Tahbaz-zadeh; Ayati, Moosa; Taghavipour, Amir; Marzbanrad, Javad

doi:10.24191/jmeche.v16i3.15351

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Consequently, in this scenario, the road grade varies with time and the EV mass is higher than its nominal value. For implementing the proposed LLC that OWK-GRIV estimator compensates, the OWK-GRIV estimator is implemented on the regression model of EV longitudinal dynamics expressed in equations ( 23) and (24). The required procedure for implementing OWK-GRIV estimator to an intended regression model is delineated in study.…”

Section: Simulation Results Of Predictive Deep Rl Algorithm Compensat...mentioning

confidence: 99%

“…Employing artificial neural network (ANN) and fuzzy-logic methods to model and predict the acceleration behavior of the driver has been the focus of many researchers. [22][23][24] Studies and Sun et al 22 have proposed artificial neural network models to predict vehicle speed profiles to improve the performance of hybrid electric vehicle control system and reduce fuel consumption and vehicle emissions. These studies have used only momentary kinematic features to train the predictive model and ignored the time history of these features.…”

Section: Development Of Deep Learning-based Speed Prediction (Spdlear...mentioning

confidence: 99%

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Cooperative adaptive cruise control system for electric vehicles through a predictive deep reinforcement learning approach

Moghaddam

Ayati

Taghavipour

2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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In this paper, a predictive deep reinforcement learning-based control algorithm is introduced for the cooperative adaptive cruise control (CACC) system. The proposed control algorithm achieves an optimal response in the interaction of the host vehicle with the leading vehicles. For this purpose, at first, a new algorithm based on deep neural networks is proposed for modeling and predicting the driving behavior of the leading vehicle, according to the traffic behavior of its surrounding vehicles. Then, the outputs of the predictive model are used as additional states for the upper-level controller of the CACC system, which is designed based on a deep reinforcement learning approach. Therefore, the proposed controller uses conventional states such as relative distance and speed of the host vehicle along with the leading vehicles and employs the speed profile of the leading vehicle in the future time horizons to train the reinforcement learning agent. Thus, the upper-level controller learning process is improved. In addition, for regulating the lower-level controller of CACC system against parametric uncertainties in electric vehicle dynamics, a deep reinforcement learning-based controller is proposed, which can be retuned according to the real-time estimated parameters of vehicle mass and road grade. Simulation results represent the promising performance of the proposed control algorithm for maintaining a balance between travel safety, passenger comfort, and reducing vehicle energy consumption.

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Section: Simulation Results Of Predictive Deep Rl Algorithm Compensat...mentioning

confidence: 99%

Section: Development Of Deep Learning-based Speed Prediction (Spdlear...mentioning

confidence: 99%

Cooperative adaptive cruise control system for electric vehicles through a predictive deep reinforcement learning approach

Moghaddam

Ayati

Taghavipour

2023

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

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“…The pattern learning ability of ANN makes it a very powerful predictive modelling tool. In other words, artificial neural network can be classified as a black box model which provides information behind the processing physics explicitly [16].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Predictive Modelling of Laser Micro-Grooving for Commercial Pure Titanium (CP Ti) Grade 2

Sivarao¹,

Zuhair²,

Ali³

et al. 2021

JMechE

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Grooving is the process of making a narrow channel on a surface of flat or cylindrical workpiece. Groove is precisely made to parts used in automotive, biomedical, and electronics industries. In automotive industries, groove plays an important role especially on mechanical parts to precisely locate seal (o-ring) to prevent gas/oil leakage between dynamic mating parts. On the other hand, artificial neural network (ANN) has been widely used in developing predictive models of various manufacturing processes to save huge amount of production time and money for industries. Unfortunately, very limited research has been investigated on micro groove quality employing ANN predictive models. Therefore, this research work presents on how the Artificial Neural Network (ANN) predictive model has been established, optimised and utilised to predict the laser micro-grooving quality of commercial pure titanium grade 2 material. A 3KW CO2 laser cutting machine was employed considering laser power, gas pressure, cutting speed, depth of cut and focal distance as the design parameters for modelling. On the other hand, three significant responses namely groove depth, groove width and groove corner radius were investigated. Experimental results were fed to establish the ANN predictive model, which then its parameters were optimized to gain high level prediction accuracy. The predicted results of ANN model presented the mean absolute percentage error for groove depth, groove width and groove corner radius at about 7.29%, 10.93% and 11.96% respectively. The obtained predictive results were found quite promising with the average of mean absolute percentage error (MAPE) for quality predictions which falls between 10 to 15%, concluding the validity of the developed ANN predictive model.

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Modeling and Prediction of Driver-Vehicle-Unit Velocity Using Adaptive Neuro-Fuzzy Inference System in Real Traffic Flow

Cited by 2 publications

References 9 publications

Cooperative adaptive cruise control system for electric vehicles through a predictive deep reinforcement learning approach

Cooperative adaptive cruise control system for electric vehicles through a predictive deep reinforcement learning approach

Artificial Neural Network Predictive Modelling of Laser Micro-Grooving for Commercial Pure Titanium (CP Ti) Grade 2

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