A Novel Car-Following Control Model Combining Machine Learning and Kinematics Models for Automated Vehicles

2019 ◽  
Vol 20 (6) ◽  
pp. 1991-2000 ◽  
Author(s):  
Da Yang ◽  
Liling Zhu ◽  
Yalong Liu ◽  
Danhong Wu ◽  
Bin Ran
Keyword(s):  
Machine Learning ◽  
Control Model ◽  
Automated Vehicles ◽  
Car Following ◽  
Model Combining

On multi-class automated vehicles: Car-following behavior and its implications for traffic dynamics

2021 ◽  
Vol 128 ◽  
pp. 103166
Author(s):  
Wissam Kontar ◽  
Tienan Li ◽  
Anupam Srivastava ◽  
Yang Zhou ◽  
Danjue Chen ◽  
...  
Keyword(s):  
Automated Vehicles ◽  
Traffic Dynamics ◽  
Car Following

Intelligent control of complex traffic conditions based on machine learning

2021 ◽  
pp. 1-12
Author(s):  
Zhe Li
Keyword(s):  
Machine Learning ◽  
Simulation Model ◽  
Traffic Simulation ◽  
Learning Algorithms ◽  
Control Model ◽  
Machine Learning Algorithms ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Traffic Conditions ◽  
Simulation Control

 In order to improve the simulation effect of complex traffic conditions, based on machine learning algorithms, this paper builds a simulation model. Starting from the macroscopic traffic flow LWR theory, this paper introduces the process of establishing the original CTM mathematical model, and combines it with machine learning algorithms to improve it, and establishes the variable cell transmission model VCTM ordinary transmission, split transmission, and combined transmission mathematical expressions. Moreover, this paper establishes a road network simulation model to calibrate related simulation parameters. In addition, this paper combines the actual needs of complex traffic conditions analysis to construct a complex traffic simulation control model based on machine learning, and designs a hybrid microscopic traffic simulation system architecture to simulate all relevant factors of complex road conditions. Finally, this paper designs experiments to verify the performance of the simulation model. The research results show that the simulation control model of complex traffic conditions constructed in this paper has certain practical effects.

Machine learning-based steering control for automated vehicles utilizing V2X communication

2021 ◽  
Author(s):  
Sergei S. Avedisov ◽  
Chaozhe R. He ◽  
Denes Takacs ◽  
Gabor Orosz
Keyword(s):  
Machine Learning ◽  
Steering Control ◽  
Automated Vehicles

Predicting Takeover Quality in Conditionally Automated Vehicles Using Machine Learning and Genetic Algorithms

2021 ◽  
pp. 84-89
Author(s):  
Emmanuel de Salis ◽  
Quentin Meteier ◽  
Marine Capallera ◽  
Leonardo Angelini ◽  
Andreas Sonderegger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Automated Vehicles

Modeling and Control Using Connected and Automated Vehicles with Chained Asymmetric Driver Behavior under Stop-and-Go Oscillations

2020 ◽  
pp. 036119812096249
Author(s):  
Anupam Srivastava ◽  
Danjue Chen ◽  
Soyoung Ahn
Keyword(s):  
Order Effect ◽  
Behavior Model ◽  
Automated Vehicles ◽  
Empirical Observation ◽  
Modeling And Control ◽  
Car Following ◽  
Asymmetric Behavior ◽  
Stop And Go ◽  
Car Following Model ◽  
And Control

This paper presents a behavioral car following model, named the chained asymmetric behavior model, that improves on the asymmetric behavior model. This model is inspired by the empirical observation that vehicles react proportionately to the magnitude of disturbance experienced when traversing through a stop-and-go oscillation, deviating from a constant following behavior observed in equilibrium conditions. Findings from simulation experiments suggest that this “second-order” effect significantly affects traffic throughput and evolution under disturbances. Knowledge obtained from the model is leveraged toward designing control for connected automated vehicles in mixed traffic streams.

scholarly journals Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers

2018 ◽  
Vol 19 (3) ◽  
pp. 733-744 ◽  
Author(s):  
Ding Zhao ◽  
Xianan Huang ◽  
Huei Peng ◽  
Henry Lam ◽  
David J. LeBlanc
Keyword(s):  
Automated Vehicles ◽  
Car Following

scholarly journals Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Daowei Li ◽  
Qiang Zhang ◽  
Yue Tan ◽  
Xinghuo Feng ◽  
Yuanyi Yue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Laboratory Tests ◽  
Operating Characteristic ◽  
Clinical Laboratory ◽  
Severe Disease ◽  
Ct Images ◽  
Model Combining ◽  
Machine Learning Model ◽  
Machine Learning Approach

BACKGROUND Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. OBJECTIVE This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. METHODS A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients’ CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. RESULTS We present a prediction model combining patients’ radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F<sub>1</sub> score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients’ laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. CONCLUSIONS To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.

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