Game Theoretic Decision Making by Actively Learning Human Intentions Applied on Autonomous Driving

TL;DR

This paper introduces a game theoretic planning algorithm for autonomous vehicles that actively learns human intentions and models human reasoning to improve lane changing in dense traffic, demonstrating advantages over existing methods.

Contribution

It presents a novel game theoretic framework that combines iterative reasoning and active learning to estimate human intentions in autonomous driving scenarios.

Findings

Outperforms state-of-the-art algorithms in dense traffic lane changing.

Reduces overly conservative behaviors during interactions.

Achieves real-time decision making in a realistic simulator.

Abstract

The ability to estimate human intentions and interact with human drivers intelligently is crucial for autonomous vehicles to successfully achieve their objectives. In this paper, we propose a game theoretic planning algorithm that models human opponents with an iterative reasoning framework and estimates human latent cognitive states through probabilistic inference and active learning. By modeling the interaction as a partially observable Markov decision process with adaptive state and action spaces, our algorithm is able to accomplish real-time lane changing tasks in a realistic driving simulator. We compare our algorithm's lane changing performance in dense traffic with a state-of-the-art autonomous lane changing algorithm to show the advantage of iterative reasoning and active learning in terms of avoiding overly conservative behaviors and achieving the driving objective successfully.