Modern complex systems often involve multiple interacting agents in a shared environment, e.g., transportation systems, power systems, swarm robotics, and human-robot interactions. Controlling these multi-agent systems (MASs) requires the characterization of agents’ interactions to account for their interdependent self-interests and coupled agents’ constraints such as collision avoidance and/or limited shared resources. To enable interaction awareness and human-like reasoning processes, game-theoretic control has been explored in the recent development of autonomous systems operating in multi-agent environments. However, fundamental challenges, including solution existence, algorithm convergence, scalability, and incomplete information, still remain to be addressed before the game-theoretic approaches could be sufficiently practical to be employed in a broad range of autonomous system applications. Possible solutions to addressing these challenges will be discussed in this talk, using autonomous driving as an application example.