Qi Liu

Orcid: 0000-0001-5172-0989

Affiliations:
  • Beijing Institute of Technology, School of Mechanical Engineering, Beijing, China


According to our database1, Qi Liu authored at least 11 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Online presence:

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Bibliography

2024
Rate GQN: A Deviations-Reduced Decision-Making Strategy for Connected and Automated Vehicles in Mixed Autonomy.
IEEE Trans. Intell. Transp. Syst., January, 2024

2023
Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends.
Sensors, October, 2023

Hierarchical Motion Planning and Tracking for Autonomous Vehicles Using Global Heuristic Based Potential Field and Reinforcement Learning Based Predictive Control.
IEEE Trans. Intell. Transp. Syst., August, 2023

2022
Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments.
CoRR, 2022

Occlusion Handling and Multi-Scale Pedestrian Detection Based on Deep Learning: A Review.
IEEE Access, 2022

A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles.
Proceedings of the 2022 IEEE Intelligent Vehicles Symposium, 2022

An Ensemble Learning Framework for Vehicle Trajectory Prediction in Interactive Scenarios.
Proceedings of the 2022 IEEE Intelligent Vehicles Symposium, 2022

Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Interactive Traffic Scenarios.
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2022

2021
Review on Vehicle Detection Technology for Unmanned Ground Vehicles.
Sensors, 2021

Decision-Making Technology for Autonomous Vehicles: Learning-Based Methods, Applications and Future Outlook.
Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021

2020
A Survey on Sensor Technologies for Unmanned Ground Vehicles.
CoRR, 2020


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