Game-Theoretic Lane-Changing Decision Making and Payoff Learning for Autonomous Vehicles

Author:

Lopez Victor1ORCID,Lewis Frank1ORCID,Liu Mushuang2ORCID,Wan Yan2ORCID,Nageshrao Subramanya3,Filev Dimitar4ORCID

Affiliation:

1. UTA Research Institute, University of Texas at Arlington, Fort Worth, TX, USA

2. Electrical Engineering Department, University of Texas, Arlington, TX, USA

3. Ford Greenfield Labs, Palo Alto, CA, USA

4. Ford Research and Innovation Center, Dearborn, MI, USA

Funder

Ford Contract Fast Autonomous Driving Decision

ARO

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Cited by 34 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. Game-Theoretic Lane Change Decision-Making Method Considering Traffic Trend;IEEE Transactions on Industrial Electronics;2024-11

2. A Game-Based Hierarchical Model for Mandatory Lane Change of Autonomous Vehicles;IEEE Transactions on Intelligent Transportation Systems;2024-09

3. Human-Like Decision Making for Autonomous Driving With Social Skills;IEEE Transactions on Intelligent Transportation Systems;2024-09

4. Dynamic Event-Triggered Safe Control for Nonlinear Game Systems With Asymmetric Input Saturation;IEEE Transactions on Cybernetics;2024-09

5. A Comprehensive Driving Decision-Making Methodology Based on Deep Reinforcement Learning for Automated Commercial Vehicles;International Journal of Automotive Technology;2024-08-26

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