decision-making4 Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN) https://ieeexplore.ieee.org/abstract/document/9653809 Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN) Symbolic reasoning is a key component for enabling practical use of data-driven planners in autonomous driving. In that context, deterministic finite state automata (DFA) are often used to formalize the underlying high-level decision-making process. Manu.. 2024. 1. 16. Altruistic Maneuver Planning for Cooperate Autonomous Vehicles Using Multi-agent Advantage Actor-Critic https://arxiv.org/abs/2107.05664 Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic With the adoption of autonomous vehicles on our roads, we will witness a mixed-autonomy environment where autonomous and human-driven vehicles must learn to co-exist by sharing the same road infrastructure. To attain socially-desirable behaviors, autonomou ar.. 2024. 1. 13. InAction: Interpretable Action Decision Making for Autonomous Driving Paper link : https://www.semanticscholar.org/paper/InAction%3A-Interpretable-Action-Decision-Making-for-Jing-Xia/3944b9b00b69761c56d647e7425eea87e6e78cab https://www.semanticscholar.org/paper/InAction%3A-Interpretable-Action-Decision-Making-for-Jing-Xia/3944b9b00b69761c56d647e7425eea87e6e78cab www.semanticscholar.org Introduction 지난 포스트에서 소개한 OIA 모델의 후속작으로 InAction 모델이 제안되었다. (2022 ECCV) OIA 모델의.. 2024. 1. 12. Explainable Object-induced Action Decision for Autonomous Vehicles Paper link : https://arxiv.org/abs/2003.09405 Explainable Object-induced Action Decision for Autonomous Vehicles A new paradigm is proposed for autonomous driving. The new paradigm lies between the end-to-end and pipelined approaches, and is inspired by how humans solve the problem. While it relies on scene understanding, the latter only considers objects that could arxiv.org Introduction 이 논문에서.. 2024. 1. 9. 이전 1 다음
