Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control
This work addresses the problem of safe and efficient autonomous vehicle control in traffic interactions, but it is incremental as it combines existing methods without a major breakthrough.
The paper tackled autonomous decision-making in dynamic interactive environments by integrating cognitive hierarchy theory, Bayesian inference, and predictive control into a two-player game framework, with simulation results in three traffic scenarios showing improved performance.
In this paper, we describe an integrated framework for autonomous decision making in a dynamic and interactive environment. We model the interactions between the ego agent and its operating environment as a two-player dynamic game, and integrate cognitive behavioral models, Bayesian inference, and receding-horizon optimal control to define a dynamically-evolving decision strategy for the ego agent. Simulation examples representing autonomous vehicle control in three traffic scenarios where the autonomous ego vehicle interacts with a human-driven vehicle are reported.