A Multiple-Goal Reinforcement Learning Method for Complex Vehicle Overtaking Maneuvers
By Daniel Chi Kit Ngai, Nelson Hong Ching Yung. 2011
123 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Automotive Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ahmad El-Sallab
- Ibrahim Sobh
- B. R. Kiran
- Patrick Pérez
- Guozhen Tan
- Xin Xu
- Dominique Gruyer
- Chunming Liu
In Automotive Engineering
In Computer Vision and Pattern Recognition
- Senthil Kumar Yogamani
- Ibrahim Sobh
- Patrick Pérez
- Chen Lv
- Xin Xu
- Dominique Gruyer
- Chunming Liu
- Chao Yu
Citations in the corpus
- Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, background): “Recently, authors demonstrated an application of DRL (DDPG) for AD using a full-sized autonomous vehicle Authors [85] propose Multi-goal RL (MGRL) framework to learn overtaking policy while avoiding collisions & maintain steady speed.”
- A Review of Motion Planning for Highway Autonomous Driving (2020, methodology): “Reinforcement learning is used in [133] to determine the policies for each agent in the scene representation, or for multi-goal overtaking maneuvers in [144] and automated lane change maneuvers in [145].”
- Multiobjective Reinforcement Learning: A Comprehensive Overview (2015, methodology): “In [29], the weighted sum approach was employed to combine seven vehicle overtaking objectives, and there are three navigation modes that were used to tune the weights.”
- Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach (2020, methodology): “A Q-learning (QL) decision-making method is proposed based on RL in [15].”
- Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs (2020, background): “from the vehicle during overtaking [7].”
- Exploring applications of deep reinforcement learning for real-world autonomous driving systems (2019, methodology): “Multiple-goal RL for overtaking : In (Ngai and Yung, 2011) a multiple-goal reinforcement learning (MGRL) framework is used to solve the vehicle overtaking problem.”