Learning MPC for Interaction-Aware Autonomous Driving: A Game-Theoretic Approach

Abstract

We consider the problem of interaction-aware motion planning for automated vehicles in general traffic situations. We model the interaction between the controlled vehicle and surrounding road users using a generalized potential game, in which each road user is assumed to minimize a common cost function subject to shared (collision avoidance) constraints. We propose a quadratic penalty method to deal with the shared constraints and solve the resulting optimal control problem online using an Augmented Lagrangian method based on PANOC. Secondly, we present a simple methodology for learning preferences and constraints of other road users online, based on observed behavior. Through extensive simulations in a highway merging scenario, we demonstrate the practical efficacy of the overall approach as well as the benefits of the proposed online learning scheme.

arXiv commentAccepted at 20th European Control Conference (ECC22); extended version

References in corpus (3)

• Continuous Inverse Optimal Control with Locally Optimal Examples
• Embedded nonlinear model predictive control for obstacle avoidance using PANOC
• ALGAMES: A Fast Solver for Constrained Dynamic Games

Cited by in corpus (2)

• A Survey on Socially Aware Robot Navigation: Taxonomy and Future Challenges
• Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenarios