activity
20082021
most citedRobust Platoon Control in Mixed Traffic Flow Based on Tube Model Predictive Control

167 citations · 343 across the 16 of their papers we have counts for

collaborators

16 papers

eess.SY2021★ 5 cited

Distributed Cooperative Driving in Multi-Intersection Road Networks

Huaxin Pei, Yi Zhang, Qinghua Tao +2

Cooperative driving at isolated intersections attracted great interest and had been well discussed in recent years. However, cooperative driving in multi-intersection road networks…

eess.SY2020★ 8 cited

Comparison of Cooperative Driving Strategies for CAVs at Signal-Free Intersections

Huile Xu, Christos G. Cassandras, Li Li +1

The properties of cooperative driving strategies for planning and controlling Connected and Automated Vehicles (CAVs) at intersections range from some that achieve highly efficient…

eess.SY2020

A General Framework for Decentralized Safe Optimal Control of Connected and Automated Vehicles in Multi-Lane Intersections

Huile Xu, Wei Xiao, Christos G. Cassandras +2

We address the problem of optimally controlling Connected and Automated Vehicles (CAVs) arriving from four multi-lane roads at an intersection where they conflict in terms of safel…

cs.MA2019★ 5 cited

A Bi-Level Cooperative Driving Strategy Allowing Lane Changes

Huile Xu, Yi Zhang, Christos G. Cassandras +2

This paper studies the cooperative driving of connected and automated vehicles (CAVs) at conflict areas (e.g., non-signalized intersections and ramping regions). Due to safety conc…

eess.SY2019★ 167 cited

Robust Platoon Control in Mixed Traffic Flow Based on Tube Model Predictive Control

Shuo Feng, Ziyou Song, Zhaojian Li +2

The design of cooperative adaptive cruise control is critical in mixed traffic flow, where connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) coexist. Compare…

eess.SY2019★ 21 cited

Cooperative Lane Changing via Deep Reinforcement Learning

Guan Wang, Jianming Hu, Zhiheng Li +1

In this paper, we study how to learn an appropriate lane changing strategy for autonomous vehicles by using deep reinforcement learning. We show that the reward of the system shoul…