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M

Meng Li

southeast university - china

H指数:11

被引数:377

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Bi-level optimization of heterogeneous autonomous vehicle driving modes for balancing traffic performance and user satisfaction

双层优化异构自动驾驶车辆驾驶模式以平衡交通性能和用户满意度

Transportation Research Part C-Emerging Technologies

IF7.9

2026-09-220

PRE

Serial distributed reinforcement learning for enhanced multi-objective platoon control in curved road coordinates

序列分布式强化学习在曲线道路坐标系中增强多目标编队控制

EXPERT SYSTEMS WITH APPLICATIONS

IF7.5

2025-04-012

PRE

Virtual car following based cooperative control of connected automated vehicles in complex scenarios: A roundabout example

基于车联网自动驾驶车辆在复杂场景下的协同控制：以环形交叉路口为例

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

IF7.9

2025-04-010

PRE

Augmented Mixed Vehicular Platoon Control With Dense Communication Reinforcement Learning for Traffic Oscillation Alleviation

具有密集通信强化学习的增强混合车辆排控制，用于缓解交通振荡

IEEE INTERNET OF THINGS JOURNAL

IF8.9

2024-11-151

PRE

A Bounded Rationality-Aware Car-Following Strategy for Alleviating Cut-In Events and Traffic Disturbances in Traffic Oscillations

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

IF8.4

2024-11-010

PRE

Enhancing Car-Following Performance in Traffic Oscillations Using Expert Demonstration Reinforcement Learning

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

IF8.4

2024-07-014

PRE

Collision Avoidance Motion Planning for Connected and Automated Vehicle Platoon Merging and Splitting With a Hybrid Automaton Architecture

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

IF8.4

2024-02-0111

PRE

Anti-Disturbance Self-Supervised Reinforcement Learning for Perturbed Car-Following System

扰动跟车系统的抗干扰自监督强化学习

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

IF7.1

2023-09-019

PRE

Enhancing Cooperation of Vehicle Merging Control in Heavy Traffic Using Communication-Based Soft Actor-Critic Algorithm

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

IF8.4

2023-06-019

PRE

A Reinforcement Learning-Based Vehicle Platoon Control Strategy for Reducing Energy Consumption in Traffic Oscillations

基于强化学习的车辆队列控制策略，用于减少交通振荡中的能耗

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

IF8.9

2021-12-0160

PREAI