# Game Projection and Robustness for Game-Theoretic Autonomous Driving

arXiv: https://arxiv.org/abs/2311.18074 · HTML: https://mlsanity.com/paper/2311.18074/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2023.

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

- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (2024)
- [Markov Potential Game and Multi-Agent Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2603.19188/) (2026)

## Abstract (arXiv)

Game-theoretic approaches are envisioned to bring human-like reasoning skills and decision-making processes for autonomous vehicles (AVs). However, challenges including game complexity and incomplete information still remain to be addressed before they can be sufficiently practical for real-world use. Game complexity refers to the difficulties of solving a multi-player game, which include solution existence, algorithm convergence, and scalability. To address these difficulties, a potential game based framework was developed in our recent work. However, conditions on cost function design need to be enforced to make the game a potential game. This paper relaxes the conditions and makes the potential game approach applicable to more general scenarios, even including the ones that cannot be molded as a potential game. Incomplete information refers to the ego vehicle's lack of knowledge of other traffic agents' cost functions. Cost function deviations between the ego vehicle estimated/learned other agents' cost functions and their actual ones are often inevitable. This motivates us to study the robustness of a game-theoretic solution. This paper defines the robustness margin of a game solution as the maximum magnitude of cost function deviations that can be accommodated in a game without changing the optimality of the game solution. With this definition, closed-form robustness margins are derived. Numerical studies using highway lane-changing scenarios are reported.