Jing Gan
Southeast University
18 Papers
13 Citations
Jing Gan is an academic researcher from Southeast University. The author has contributed to research in topics: Computer science & Platoon. The author has an hindex of 3, co-authored 11 publications.
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Papers
A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory
TL;DR: Numerical simulation analysis of the model shows that the model can characterize the effects of various motion parameters on the lane change results and can provide some theoretical support for related researches such as vehicle lane changing, vehicle autonomous driving, and vehicle group optimization control in the intelligent networked environment in the future.
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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.
Injury severity analysis of two-vehicle crashes at unsignalized intersections using mixed logit models
TL;DR: In this article , the authors used mixed logit models to determine the factors that influence injury severity in the two-vehicle crash, taking into account the vehicle characteristics of different crash roles.
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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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