Ziwei Yi
Southeast University
14 Papers
Ziwei Yi is an academic researcher from Southeast University. The author has contributed to research in topics: Computer science & Stability (learning theory). The author has an hindex of 2, co-authored 6 publications.
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Papers
Risk perception and the warning strategy based on safety potential field theory.
TL;DR: Comparisons with some classic risk indicators indicate that the proposed PFI can more accurately reflect the actual driving risk faced by vehicles under different vehicle motion states and thus is more suitable for driving risk assessment in the CAVs environment.
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An alternative method for traffic accident severity prediction: using deep forests algorithm
TL;DR: Wang et al. as discussed by the authors employed the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm, which was proved to be more accurate and robust in comparison with other machine learning algorithms.
A Novel Graph and Safety Potential Field Theory-Based Vehicle Platoon Formation and Optimization Method
TL;DR: This study proposes a novel platoon formation and optimization model combining graph theory and safety potential field (G-SPF) theory for connected and automated vehicles (CAVs) under different vehicle distributions and innovatively incorporate the concept of the safety Potential field to better describe the actual driving risk of vehicles and ensure their absolute safety.
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Using Artificial Potential Field Theory for a Cooperative Control Model in a Connected and Automated Vehicles Environment
TL;DR: A vehicle cooperative control model for avoiding collision in the connected and autonomous vehicles (CAVs) environment is presented, using artificial potential field theory, and the proposed model is significantly superior to the human driving environment whether in free or congested situations.
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Traffic speed forecasting for urban roads: A deep ensemble neural network model
TL;DR: Wang et al. as mentioned in this paper proposed a deep ensemble neural network (DENN) model to improve the accuracy of urban traffic state forecasting by forming the road sections with high relevance into a virtual graph.
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