2 citations · 2 across the 3 of their papers we have counts for
6 papers
An Opponent-Aware Reinforcement Learning Method for Team-to-Team Multi-Vehicle Pursuit via Maximizing Mutual Information Indicator
Qinwen Wang, Xinhang Li, Zheng Yuan +3
The pursuit-evasion game in Smart City brings a profound impact on the Multi-vehicle Pursuit (MVP) problem, when police cars cooperatively pursue suspected vehicles. Existing studi…
OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Area
Zheng Yuan, Tianhao Wu, Qinwen Wang +3
Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicl…
A Credibility-aware Swarm-Federated Deep Learning Framework in Internet of Vehicles
Zhe Wang, Xinhang Li, Tianhao Wu +2
Federated Deep Learning (FDL) is helping to realize distributed machine learning in the Internet of Vehicles (IoV). However, FDL's global model needs multiple clients to upload lea…
Density-Aware Federated Imitation Learning for Connected and Automated Vehicles with Unsignalized Intersection
Tianhao Wu, Mingzhi Jiang, Yinhui Han +2
Intelligent Transportation System (ITS) has become one of the essential components in Industry 4.0. As one of the critical indicators of ITS, efficiency has attracted wide attentio…
A Multi-intersection Vehicular Cooperative Control based on End-Edge-Cloud Computing
Mingzhi Jiang, Tianhao Wu, Zhe Wang +3
Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsig…
Value-Decomposition Networks based Distributed Interference Control in Multi-platoon Groupcast
Xiongfeng Guo, Tianhao Wu, Lin Zhang
Platooning is considered one of the most representative 5G use cases. Due to the small spacing within the platoon, the platoon needs more reliable transmission to guarantee driving…