Mingyu Wang

Orcid: 0000-0002-0063-7445

Affiliations:
  • Stanford University, Department of Mechanical Engineering, Stanford, CA, USA (PhD 2022)


According to our database1, Mingyu Wang authored at least 13 papers between 2015 and 2023.

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

Timeline

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Bibliography

2023
Maximum-Entropy Multi-Agent Dynamic Games: Forward and Inverse Solutions.
IEEE Trans. Robotics, June, 2023

2022
Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers.
Proceedings of the 2022 International Conference on Robotics and Automation, 2022

2021
Game-Theoretic Planning for Self-Driving Cars in Multivehicle Competitive Scenarios.
IEEE Trans. Robotics, 2021

Maximum-Entropy Multi-Agent Dynamic Games: Forward and Inverse Solutions.
CoRR, 2021

2020
Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments.
Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, 2020

Game-Theoretic Planning for Risk-Aware Interactive Agents.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Enhancing Game-Theoretic Autonomous Car Racing Using Control Barrier Functions.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

2019
Game Theoretic Planning for Self-Driving Cars in Competitive Scenarios.
Proceedings of the Robotics: Science and Systems XV, 2019

Distributed Collision Avoidance of Multiple Robots with Probabilistic Buffered Voronoi Cells.
Proceedings of the 2019 International Symposium on Multi-Robot and Multi-Agent Systems, 2019

2018
Safe Distributed Lane Change Maneuvers for Multiple Autonomous Vehicles Using Buffered Input Cells.
Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

2015
Approximation-Based Adaptive Tracking Control for MIMO Nonlinear Systems With Input Saturation.
IEEE Trans. Cybern., 2015

Kernel PLS based prediction model construction and simulation on theoretical cases.
Neurocomputing, 2015

Study on kernel partial least squares based key indicator prediction.
Proceedings of the IECON 2015, 2015


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