Hierarchical Game-Based Multi-Agent Decision-Making for Autonomous Vehicles

TL;DR

This paper introduces a hierarchical game-theoretic decision-making framework for autonomous vehicles that efficiently manages multi-agent interactions, reducing computational complexity while improving decision quality in traffic scenarios.

Contribution

It presents a novel hierarchical game-based framework with an interaction graph that selectively considers key agents, enhancing decision efficiency over traditional multi-player and pairwise game approaches.

Findings

Hierarchical game reduces computational complexity.

Improved hierarchical game decomposes into sub-games for efficiency.

Simulation shows effective and safe decision-making in intersection scenarios.

Abstract

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.