  Iterative linear-quadratic (ILQ) methods are widely used in the nonlinear
optimal control community. Recent work has applied similar methodology in the
setting of multiplayer general-sum differential games. Here, ILQ methods are
capable of finding local equilibria in interactive motion planning problems in
real-time. As in most iterative procedures, however, this approach can be
sensitive to initial conditions and hyperparameter choices, which can result in
poor computational performance or even unsafe trajectories. In this paper, we
focus our attention on a broad class of dynamical systems which are feedback
linearizable, and exploit this structure to improve both algorithmic
reliability and runtime. We showcase our new algorithm in three distinct
traffic scenarios, and observe that in practice our method converges
significantly more often and more quickly than was possible without exploiting
the feedback linearizable structure.
