Zhigang Xu
Chang'an University
106 Papers
162 Citations
Zhigang Xu is an academic researcher from Chang'an University. The author has contributed to research in topics: Computer science & Platoon. The author has an hindex of 20, co-authored 85 publications. Previous affiliations of Zhigang Xu include China Mobile & Dalhousie University.
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Papers
Communication delay compensation for string stability of CACC system using LSTM prediction
TL;DR: This study proposes the use of a deep learning method, i.e., the long short-term memory (LSTM) neural network, to predict the acceleration of the preceding vehicle by using data from onboard radar sensors, which provides the CACC platoon with another option to obtain additional information if the quality of wireless communication worsens.
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A Fault Detection and Diagnosis System for Autonomous Vehicles Based on Hybrid Approaches
TL;DR: Experiments on the real autonomous vehicle platform ‘Xinda’ and performance comparison with other fault detectors validate the effectiveness of these methods and the usability of the fault detection and diagnosis system.
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Machines versus humans: People's biased responses to traffic accidents involving self-driving vehicles.
Peng Liu,Yong Du,Zhigang Xu +2 more
TL;DR: It is witnessed that participants had a tendency to perceive traffic crashes involving SDVs to be more severe than those involving conventionally human-driven vehicles (HDVs) regardless of their severity (injury or fatality) or cause (SDVs/HDVs or others).
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Modeling and field experiments on autonomous vehicle lane changing with surrounding human‐driven vehicles
TL;DR: A dynamic lane-changing model for AV incorporating human driver behavior in mixed traffic, which can complete lane- changing maneuvers efficiently when HVs are cooperative and can also robustly abort them when HV are uncooperative is presented.
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Trajectory Optimization for a Connected Automated Traffic Stream: Comparison Between an Exact Model and Fast Heuristics
TL;DR: This paper investigates a core trajectory optimization problem as a building block for numerous trajectory optimization problems, i.e., guiding movements of connected automated vehicles on a one-lane highway when the arrival and departure times and velocity are given.
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