ALGAMES: A Fast Solver for Constrained Dynamic Games
This addresses real-time control challenges in autonomous driving by providing a fast solver for constrained multi-actor interactions, representing a strong specific gain rather than a broad paradigm shift.
The paper tackles trajectory optimization for multiple interacting actors in dynamic games, introducing ALGAMES, which solves complex autonomous driving scenarios like ramp merging three times faster than a state-of-the-art DDP-based approach and achieves real-time performance with update frequencies over 60 Hz.
Dynamic games are an effective paradigm for dealing with the control of multiple interacting actors. This paper introduces ALGAMES (Augmented Lagrangian GAME-theoretic Solver), a solver that handles trajectory optimization problems with multiple actors and general nonlinear state and input constraints. Its novelty resides in satisfying the first order optimality conditions with a quasi-Newton root-finding algorithm and rigorously enforcing constraints using an augmented Lagrangian formulation. We evaluate our solver in the context of autonomous driving on scenarios with a strong level of interactions between the vehicles. We assess the robustness of the solver using Monte Carlo simulations. It is able to reliably solve complex problems like ramp merging with three vehicles three times faster than a state-of-the-art DDP-based approach. A model predictive control (MPC) implementation of the algorithm demonstrates real-time performance on complex autonomous driving scenarios with an update frequency higher than 60 Hz.