Hierarchical Game-Based Multi-Agent Decision-Making for Autonomous Vehicles
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908471152082944 |
|---|---|
| author | Liu, Mushuang Wan, Yan Lewis, Frank Nageshrao, Subramanya Tseng, H. Eric Filev, Dimitar |
| author_facet | Liu, Mushuang Wan, Yan Lewis, Frank Nageshrao, Subramanya Tseng, H. Eric Filev, Dimitar |
| contents | This paper develops a game-theoretic decision-making framework for autonomous driving in multi-agent scenarios. A novel hierarchical game-based decision framework is developed for the ego vehicle. This framework features an interaction graph, which characterizes the interaction relationships between the ego and its surrounding traffic agents (including AVs, human driven vehicles, pedestrians, and bicycles, and others), and enables the ego to smartly select a limited number of agents as its game players. Compared to the standard multi-player games, where all surrounding agents are considered as game players, the hierarchical game significantly reduces the computational complexity. In addition, compared to pairwise games, the most popular approach in the literature, the hierarchical game promises more efficient decisions for the ego (in terms of less unnecessary waiting and yielding). To further reduce the computational cost, we then propose an improved hierarchical game, which decomposes the hierarchical game into a set of sub-games. Decision safety and efficiency are analyzed in both hierarchical games. Comprehensive simulation studies are conducted to verify the effectiveness of the proposed frameworks, with an intersection-crossing scenario as a case study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Game-Based Multi-Agent Decision-Making for Autonomous Vehicles Liu, Mushuang Wan, Yan Lewis, Frank Nageshrao, Subramanya Tseng, H. Eric Filev, Dimitar Systems and Control This paper develops a game-theoretic decision-making framework for autonomous driving in multi-agent scenarios. A novel hierarchical game-based decision framework is developed for the ego vehicle. This framework features an interaction graph, which characterizes the interaction relationships between the ego and its surrounding traffic agents (including AVs, human driven vehicles, pedestrians, and bicycles, and others), and enables the ego to smartly select a limited number of agents as its game players. Compared to the standard multi-player games, where all surrounding agents are considered as game players, the hierarchical game significantly reduces the computational complexity. In addition, compared to pairwise games, the most popular approach in the literature, the hierarchical game promises more efficient decisions for the ego (in terms of less unnecessary waiting and yielding). To further reduce the computational cost, we then propose an improved hierarchical game, which decomposes the hierarchical game into a set of sub-games. Decision safety and efficiency are analyzed in both hierarchical games. Comprehensive simulation studies are conducted to verify the effectiveness of the proposed frameworks, with an intersection-crossing scenario as a case study. |
| title | Hierarchical Game-Based Multi-Agent Decision-Making for Autonomous Vehicles |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2507.21941 |