.png){kind=icon}
返回
Driver Modeling Using a Continuous Policy Space: Theory and Traffic Data Validation
.png){kind=icon}
PRE
AI
DOI：10.1109/TIV.2023.3333337.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this article, we present a continuous-policy-space game theoretical method for modeling human driver interactions on highway traffic. The proposed method is based on Gaussian Processes and developed as a refinement of the hierarchical decision-making concept called level- k reasoning that conventionally assigns discrete levels of behaviors to agents. Conventional level-k reasoning approach may pose undesired constraints for predicting human decision making due to a limited number (usually 2 or 3) of driver policies it provides. To fill this gap in the literature, we expand the framework to a continuous domain that enables a continuous-policy-space, consisting of infinitely many driver policies. Through the approach detailed in this article, more accurate and realistic driver models can be obtained and employed for creating high-fidelity simulation platforms for the validation of autonomous vehicle control algorithms. We validate the proposed method on a traffic dataset and compare it with the conventional level-k approach to demonstrate its contributions and implications.
Keyword:
Vehicles
Cognition
Hidden Markov models
Reinforcement learning
Computational modeling
Gaussian processes
Games
Level-k reasoning
human driver modeling
reinforcement learning
gaussian processes
期刊
I
IF:
14.3
论文数:
1.3K
被引数:
1.2W
机构
引用论文
Driver Modeling Through Deep Reinforcement Learning and Behavioral Game Theory基于深度强化学习和行为博弈论的驾驶员建模
OAAI
OAAI分享收藏Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏Cutaneous, systemic features and laboratory characteristics of late- versus adult-onset systemic lupus erythematosus in 1006 Thai patients.png)
Lupus
LupusIF0
PREAI
分享收藏Modeling cyber-physical human systems via an interplay between reinforcement learning and game theory通过强化学习和博弈论之间的相互作用对网络物理人类系统进行建模OAAI
OAAI分享收藏Interaction-Aware Planning With Deep Inverse Reinforcement Learning for Human-Like Autonomous Driving in Merge Scenarios融合场景中类人自动驾驶的深度逆强化学习交互感知规划
PREAI
分享收藏分享收藏
查看更多内容