149 citations · 319 across the 13 of their papers we have counts for
13 papers
Performance Evaluation of Semi-supervised Learning Frameworks for Multi-Class Weed Detection
Jiajia Li, Dong Chen, Xunyuan Yin +1
Effective weed control plays a crucial role in optimizing crop yield and enhancing agricultural product quality. However, the reliance on herbicide application not only poses a cri…
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control
Dong Chen, Kaixiang Zhang, Yongqiang Wang +3
Connected and autonomous vehicles (CAVs) promise next-gen transportation systems with enhanced safety, energy efficiency, and sustainability. One typical control strategy for CAVs…
O2RNet: Occluder-Occludee Relational Network for Robust Apple Detection in Clustered Orchard Environments
Pengyu Chu, Zhaojian Li, Kaixiang Zhang +3
Automated apple harvesting has attracted significant research interest in recent years due to its potential to revolutionize the apple industry, addressing the issues of shortage a…
Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach
Dong Chen, Xinda Qi, Yu Zheng +2
Weed management plays an important role in many modern agricultural applications. Conventional weed control methods mainly rely on chemical herbicides or hand weeding, which are of…
Event-Triggered Model Predictive Control with Deep Reinforcement Learning for Autonomous Driving
Fengying Dang, Dong Chen, Jun Chen +1
Event-triggered model predictive control (eMPC) is a popular optimal control method with an aim to alleviate the computation and/or communication burden of MPC. However, it general…
Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancement
Tianyu Shi, Dong Chen, Kaian Chen +1
Reinforcement learning (RL) is a powerful data-driven control method that has been largely explored in autonomous driving tasks. However, conventional RL approaches learn control p…
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
Wei Zhou, Dong Chen, Jun Yan +3
Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits, including releasing drivers from exhausting driving an…
Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehicles
Zhitao Wang, Yuzheng Zhuang, Qiang Gu +3
For autonomous vehicles integrating onto roadways with human traffic participants, it requires understanding and adapting to the participants' intention and driving styles by respo…
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic
Dong Chen, Mohammad Hajidavalloo, Zhaojian Li +4
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate th…
A Mechanical System Inspired Microscopic Traffic Model: Modeling, Analysis, and Validation
Mohammad R. Hajidavalloo, Zhaojian Li, Dong Chen +3
In this paper, we develop a mechanical system inspired microscopic traffic model to characterize the longitudinal interaction dynamics among a chain of vehicles. In particular, we…
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control
Dong Chen, Kaian Chen. Zhaojian Li, Tianshu Chu +3
This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-base…
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
Ming Zhou, Jun Luo, Julian Villella +34
Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently i…
Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model
Dong Chen, Longsheng Jiang, Yue Wang +1
It is expected that many human drivers will still prefer to drive themselves even if the self-driving technologies are ready. Therefore, human-driven vehicles and autonomous vehicl…