Game Projection and Robustness for Game-Theoretic Autonomous Driving

no code implementations • 29 Nov 2023 • Mushuang Liu, H. Eric Tseng, Dimitar Filev, Anouck Girard, Ilya Kolmanovsky

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.

Autonomous Driving Decision Making

Safe and Human-Like Autonomous Driving: A Predictor-Corrector Potential Game Approach

no code implementations • 4 Aug 2022 • Mushuang Liu, H. Eric Tseng, Dimitar Filev, Anouck Girard, Ilya Kolmanovsky

To address the challenges caused by the complexity in solving a multi-player game and by the requirement of real-time operation, a potential game (PG) based decision-making framework is developed.

Autonomous Driving Decision Making

Potential Game-Based Decision-Making for Autonomous Driving

no code implementations • 16 Jan 2022 • Mushuang Liu, Ilya Kolmanovsky, H. Eric Tseng, Suzhou Huang, Dimitar Filev, Anouck Girard

Statistical comparative studies, including 1) finite potential game vs. continuous potential game, and 2) best response dynamics vs. potential function optimization, are conducted to compare the performances of different solution algorithms.

Autonomous Driving Decision Making