An optimal trajectory planning for automated on‐ramp merging
Author:
Affiliation:
1. State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment China Nuclear Power Engineering Co., Ltd. Shenzhen China
2. College of Control Science and Engineering Zhejiang University Hangzhou China
Publisher
Institution of Engineering and Technology (IET)
Subject
Law,Mechanical Engineering,General Environmental Science,Transportation
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/itr2.12308
Reference36 articles.
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2. Learning All Dynamics: Traffic Forecasting via Locality-Aware Spatio-Temporal Joint Transformer
3. Lubars J. Gupta H. Chinchali S. Li L. Raja A. Srikant R. et al.:Combining reinforcement learning with model predictive control for on‐ramp merging. In:2021 IEEE International Intelligent Transportation Systems Conference (ITSC) pp.942–947.IEEE Piscataway(2021)
4. A Simulation-based Evaluation of Connected Vehicle Technology for Emissions and Fuel Consumption
5. Optimal vehicle trajectory planning in the context of cooperative merging on highways
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1. Deep Reinforcement Learning-Based Autonomous Driving Strategy under On-Ramp Merge Scenario;Transaction of the Korean Society of Automotive Engineers;2024-07-01
2. Cooperative Lane‐Changing Control for CAVs at Freeway On‐Ramps considering Vehicle Dynamics;Journal of Advanced Transportation;2024-01
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