Cloud Model Approach for Lateral Control of Intelligent Vehicle Systems

Gao, Hongbo; Zhang, Xinyu; Liu, Yuchao; Li, Deyi

doi:10.1155/2016/6842891

Cited by 16 publications

(14 citation statements)

References 18 publications

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“…In [12], a novel integrated FMEA model based on the cloud model and hierarchical technique was developed to assess and rank the risk of failure modes. Based on the cloud model, Gao et al [13] proposed an intelligent lateral control algorithm, which was designed to calculate intelligent vehicle lateral offsets. Peng and Wang [14] proposed a multicriteria group decision-making method based on the normal cloud model with Zadeh's Z-numbers.…”

Section: Related Work a Cloud Model Applicationsmentioning

confidence: 99%

Trust Routing Protocol Based on Cloud-Based Fuzzy Petri Net and Trust Entropy for Mobile Ad hoc Network

Wang

Zhang

et al. 2020

IEEE Access

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The trust-based routing mechanisms are proposed to enhance the security of the mobile ad hoc network (MANET), which use the performance metrics of a node to evaluate the trust value of the node. However, some performance metrics are fuzzy, which are easier to be described qualitatively than to be expressed quantitatively. Therefore, the inability to quantitatively express these performance metrics leads to the inaccuracy in the calculation of the trust values of nodes. Meanwhile, some routing mechanisms add the path with the highest credibility to routing table without considering the hop counts of the route in route selection, which reduces quality of service (QoS) of the routing. Aiming at the above problems, firstly, we use cloud model to deal with the fuzziness of performance metrics. Specifically, a trust reasoning model based on cloud model and fuzzy Petri net (FPN) is presented to evaluate the credibility of nodes. Then we propose a routing algorithm based on trust entropy. Routes with the minimum trust entropy are selected to add to routing table. This routing algorithm can reflect the comprehensive effect of route hops and the trust values of nodes on routing selection, thus improving QoS in MANET. Finally, the TUE-OLSR protocol is established based on the trust entropy routing algorithm and the optimized link state routing (OLSR) protocol. What's more, the effectiveness of TUE-OLSR protocol is verified by simulation experiments, which illustrate that TUE-OLSR protocol performs better than existing trust-based OLSR protocols in terms of packet delivery ratio and average latency. INDEX TERMS Cloud model, fuzzy Petri net, mobile ad hoc network, trust entropy, TUE-OLSR.

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Section: Related Work a Cloud Model Applicationsmentioning

confidence: 99%

Trust Routing Protocol Based on Cloud-Based Fuzzy Petri Net and Trust Entropy for Mobile Ad hoc Network

Wang

Zhang

et al. 2020

IEEE Access

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“…Additionally, we will consider Convolutional Neural Network (CNN) [52] in our experimentation as this methodology assures negligible loss and best classification accuracy. Other approaches like the Dynamic Bayesian Network (DBN), Gauss Cloud Model, and Cloud Reasoning Algorithm will also be taken into consideration for best classifying accuracy and precision [53][54][55].…”

Section: Future Recommendationsmentioning

confidence: 99%

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Abidi

Hussain

et al. 2018

Sustainability

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Incorporating substantial, sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study was to identify the confused students who had failed to master the skill(s) given by the tutors as homework using the Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study, and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models including: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated, and tested learning algorithms, performed stratified cross-validation, and measured the performance of the models through various performance metrics, i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity, and Specificity. We found RF, GLM, XGBoost, and DL were high accuracy-achieving classifiers. However, other perceptions such as detecting unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students that were confused when attempting the homework exercise, to help foster their knowledge and talent to play a vital role in environmental development.

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“…13 Further research apply many intelligent algorithms to model driver behavior for intelligent vehicles such as fuzzy control theory, 14 neural network, 15,16 hidden Markov model, 17 game theory, 18 and other intelligent methods. [19][20][21] The longitudinal driver model mainly studies the speed tracking ability of intelligent vehicles, that is, how to control the vehicle to cruise at a predefined speed or keep a certain distance from the target vehicle ahead. In the actual driving process, the driver's speed control behavior will be affected by various factors such as the vehicle motion state, road environment and personal driving style.…”

Section: Introductionmentioning

confidence: 99%

Modeling human-like longitudinal driver model for intelligent vehicles based on reinforcement learning

Xie

Wang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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The driver model is the decision-making and control center of intelligent vehicle. In order to improve the adaptability of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the skilled driver under the driver-vehicle-road closed-loop system, a kind of human-like longitudinal driver model for intelligent vehicles based on reinforcement learning is proposed. This paper builds the lateral driver model for intelligent vehicles based on optimal preview control theory. Then, the control correction link of longitudinal driver model is established to calculate the throttle opening or brake pedal travel for the desired longitudinal acceleration. Moreover, the reinforcement learning agents for longitudinal driver model is parallel trained by comprehensive evaluation index and skilled driver data. Lastly, training performance and scenarios verification between the simulation experiment and the real car test are performed to verify the effectiveness of the reinforcement learning based longitudinal driver model. The results show that the proposed human-like longitudinal driver model based on reinforcement learning can help intelligent vehicles effectively imitate the speed control behavior of the skilled driver in various path-following scenarios.

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Cloud Model Approach for Lateral Control of Intelligent Vehicle Systems

Cited by 16 publications

References 18 publications

Trust Routing Protocol Based on Cloud-Based Fuzzy Petri Net and Trust Entropy for Mobile Ad hoc Network

Trust Routing Protocol Based on Cloud-Based Fuzzy Petri Net and Trust Entropy for Mobile Ad hoc Network

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Modeling human-like longitudinal driver model for intelligent vehicles based on reinforcement learning

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