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

arXiv: https://arxiv.org/abs/2301.09178 · HTML: https://mlsanity.com/paper/2301.09178/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2023.

## Built on by (most cited first, 2 of 2)

- [Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving](https://mlsanity.com/paper/2401.15315/) (2024)
- [K-Level Policy Gradients for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.12117/) (2025)

## Abstract (arXiv)

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.