Results 231 to 240 of about 14,506 (292)

A Survey on Attack Detection and Resilience for Connected and Automated Vehicles: From Vehicle Dynamics and Control Perspective

IEEE Transactions on Intelligent Vehicles, 2022
Recent advances in attack/anomaly detection and resilience strategies for connected and automated vehicles (CAVs) are reviewed from vehicle dynamics and control perspective.
Zhiyang Ju, Hui Zhang, Xiang Li, Xiaoguang Chen, Jinpeng Han, Manzhi Yang   +5 more
semanticscholar   +1 more source

Impacts of Connected and Automated Vehicles on Road Safety and Efficiency: A Systematic Literature Review

IEEE transactions on intelligent transportation systems (Print), 2023
Connected and automated vehicles (CAVs), in the context of cooperative intelligent transportation systems (C-ITS), are capable of exchanging information with each other and the infrastructure.
A. Matin, H. Dia
semanticscholar   +1 more source

Safety-Critical and Flexible Cooperative On-Ramp Merging Control of Connected and Automated Vehicles in Mixed Traffic

IEEE transactions on intelligent transportation systems (Print), 2023
Cooperative on-ramp merging control for connected and automated vehicles (CAVs) can effectively improve traffic throughput and vehicle fuel efficiency at highway on-ramp merging bottlenecks.
Haoji Liu, Weichao Zhuang, Guo-dong Yin, Zhaojian Li, Dongpu Cao   +4 more
semanticscholar   +1 more source

A MAS-Based Hierarchical Architecture for the Cooperation Control of Connected and Automated Vehicles

IEEE Transactions on Vehicular Technology, 2023
Complex traffic scenarios pose a challenge to the cooperation control of connected and automated vehicles (CAVs). Conventional platoon control or advanced driver-assistance systems only provide an isolated solution due to their inadequacies in systematic
Jinhao Liang, Yanjun Li, Guo-dong Yin, Liwei Xu, Yanbo Lu, Jiwei Feng, T. Shen, Guoshun Cai   +7 more
semanticscholar   +1 more source

Dynamic Driving Risk Potential Field Model Under the Connected and Automated Vehicles Environment and Its Application in Car-Following Modeling

IEEE transactions on intelligent transportation systems (Print), 2022
This paper proposes a new dynamic driving risk potential field model under the connected and automated vehicles environment that fully considers the dynamic effect of the vehicle’s acceleration and steering angle.
Lin-heng Li, Jing Gan, Xinkai Ji, Xu Qu, B. Ran   +4 more
semanticscholar   +1 more source

Shared Perception for Connected and Automated Vehicles

2020 IEEE Intelligent Vehicles Symposium (IV), 2020
Connected and automated vehicles (CAVs) have the potential to improve the safety of automated driving by utilizing increased awareness about their surroundings in real time vehicle control. In this paper we propose a framework for a shared perception system suitable for CAVs and explain the algorithms used in the system.
Yeojun Kim, Luca Onesto, Samuel Tay, Lujie Yang, Jacopo Guanetti, Sergio M. Savaresi, Francesco Borrelli   +6 more
openaire   +1 more source

On-Ramp Merging Strategies of Connected and Automated Vehicles Considering Communication Delay

IEEE transactions on intelligent transportation systems (Print), 2022
Improper handling of on-ramp merging may cause severe decrease of traffic efficiency and contribute to lower fuel economy, even increasing the collision risk.
Yukun Fang, Haigen Min, Xia Wu, Wuqi Wang, Xiangmo Zhao, Guoqiang Mao   +5 more
semanticscholar   +1 more source

The Role of Digital Twins in Connected and Automated Vehicles

IEEE Intelligent Transportation Systems Magazine, 2022
Digital twins found their genesis in the halls of NASA and the methods of product lifecycle management. Rapidly evolving trends around the proliferation of sensors, the Internet of Things, Industry 4.0, and cyber-physical systems have spurred the growth ...
C. Schwarz, Ziran Wang
semanticscholar   +1 more source

Modeling Automated Vehicles and Connected Automated Vehicles on Highways

2021
Doctor of ...
openaire   +2 more sources

Fundamentals of Connected and Automated Vehicles

2022
The automotive industry is transforming to a greater degree that has occurred since Henry Ford introduced mass production of the automobile with the Model T in 1913. Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of ...
Jeffrey Wishart, Yan Chen, Steven Como, Narayanan Kidambi, Duo Lu, Yezhou Yang   +5 more
openaire   +1 more source