A Collaborative Decision Making Approach for Multi-Unmanned Combat Vehicles based on the Behaviour Tree
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[1] Xin Zhang,et al. End to End Learning for Self-Driving Cars , 2016, ArXiv.
[2] Mariette Awad,et al. Decision Making in Multiagent Systems: A Survey , 2018, IEEE Transactions on Cognitive and Developmental Systems.
[3] Marco Antonio Gómez-Martín,et al. Dynamic Expansion of Behaviour Trees , 2008, AIIDE.
[4] Dirk Haehnel,et al. Junior: The Stanford entry in the Urban Challenge , 2008 .
[5] David Isele,et al. Navigating Occluded Intersections with Autonomous Vehicles Using Deep Reinforcement Learning , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[6] Alexey Dosovitskiy,et al. End-to-End Driving Via Conditional Imitation Learning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[7] Martial Hebert,et al. Learning monocular reactive UAV control in cluttered natural environments , 2012, 2013 IEEE International Conference on Robotics and Automation.
[8] Luke Fletcher,et al. A perception‐driven autonomous urban vehicle , 2008, J. Field Robotics.
[9] Hani S. Mahmassani,et al. Modeling Lane-Changing Behavior in a Connected Environment: A Game Theory Approach , 2015 .
[10] Amnon Shashua,et al. Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving , 2016, ArXiv.
[11] Eder Santana,et al. Learning a Driving Simulator , 2016, ArXiv.
[12] Sebastian Thrun,et al. Junior: The Stanford entry in the Urban Challenge , 2008, J. Field Robotics.