65 citations · 87 across the 7 of their papers we have counts for
7 papers
Multi-vehicle experiment platform: A Digital Twin Realization Method
Chunying Yang, Jianghong Dong, Qing Xu +4
With the development of V2X technology, multiple vehicles cooperative control has been widely studied. However, filed testing is rarely conducted due to financial and safety consid…
Formation Control for Connected and Automated Vehicles on Multi-lane Roads: Relative Motion Planning and Conflict Resolution
Mengchi Cai, Qing Xu, Chaoyi Chen +4
Multi-vehicle coordinated decision making and control can improve traffic efficiency while guaranteeing driving safety. Formation control is a typical multi-vehicle coordination me…
A Graph-based Conflict-free Cooperation Method for Intelligent Electric Vehicles at Unsignalized Intersections
Chaoyi Chen, Qing Xu, Mengchi Cai +6
Electric, intelligent, and network are the most important future development directions of automobiles. Intelligent electric vehicles have shown great potentials to improve traffic…
Coordinated Formation Control for Intelligent and Connected Vehicles in Multiple Traffic Scenarios
Qing Xu, Mengchi Cai, Keqiang Li +3
In this paper, a unified multi-vehicle formation control framework for Intelligent and Connected Vehicles (ICVs) that can apply to multiple traffic scenarios is proposed. In the on…
Controllability Analysis and Optimal Control of Mixed Traffic Flow with Human-driven and Autonomous Vehicles
Jiawei Wang, Yang Zheng, Qing Xu +2
Connected and automated vehicles (CAVs) have a great potential to improve traffic efficiency in mixed traffic systems, which has been demonstrated by multiple numerical simulations…
Longitudinal Safety Analysis For Heterogeneous Platoon Of Automated And Human Vehicles
Zi Yang, Xinpeng Wang, Xin Pei +4
With the recent advancement in environmental sensing, vehicle control and vehicle-infrastructure cooperation technologies, more and more autonomous driving companies start to put t…
Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving
Xi Xiong, Jianqiang Wang, Fang Zhang +1
With the development of state-of-art deep reinforcement learning, we can efficiently tackle continuous control problems. But the deep reinforcement learning method for continuous c…