By combining concepts from game theory and estimation, Stanford researchers created LUCIDGames, a computational technique that can predict and plan adaptive trajectories for autonomous vehicles. LUCIDGames, an inverse optimal control algorithm is able to estimate the other agents' objective functions in real time, and incorporate those estimates online into a receding-horizon game-theoretic planner.