An Iterative Quadratic Method for General-Sum Differential Games with Feedback Linearizable Dynamics
This work addresses algorithmic reliability and runtime issues in interactive motion planning for traffic scenarios, representing an incremental improvement.
The paper tackles the sensitivity of iterative linear-quadratic methods in multiplayer differential games by focusing on feedback linearizable dynamics, resulting in improved convergence rates and reliability in traffic scenarios.
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.