Interaction-Aware Planning With Deep Inverse Reinforcement Learning for Human-Like Autonomous Driving in Merge Scenarios-Reference-Cited by-同舟云学术

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Interaction-Aware Planning With Deep Inverse Reinforcement Learning for Human-Like Autonomous Driving in Merge Scenarios

Published:2023 Issue: Volume: Page:1-12
ISSN:2379-8904
Container-title:IEEE Transactions on Intelligent Vehicles
language:
Short-container-title:IEEE Trans. Intell. Veh.

Author:

Nan Jiangfeng¹^ORCID,Deng Weiwen¹^ORCID,Zhang Ruzheng²,Wang Ying³^ORCID,Zhao Rui¹^ORCID,Ding Juan⁴

Affiliation:

1. School of Transportation Science and Engineering, Beihang University, Beijing, China

2. Horizon Robotics, Beijing, China

3. College of Computer Science and Technology, Jilin University, Jilin, China

4. PanoSim Technology Limited Company, Jiaxing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/7274857/7448921/10195221.pdf?arnumber=10195221

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey;Transportation Research Part C: Emerging Technologies;2024-07

2. Deep Reinforcement Learning-Based Autonomous Driving Strategy under On-Ramp Merge Scenario;Transaction of the Korean Society of Automotive Engineers;2024-07-01

3. Human Knowledge Enhanced Reinforcement Learning for Mandatory Lane-Change of Autonomous Vehicles in Congested Traffic;IEEE Transactions on Intelligent Vehicles;2023

4. Driver Modeling Using a Continuous Policy Space: Theory and Traffic Data Validation;IEEE Transactions on Intelligent Vehicles;2023

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