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arXiv · 2111.08331

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

Brecht Evens·Mathijs Schuurmans·Panagiotis Patrinos
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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.

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Brecht Evens, Mathijs Schuurmans, Panagiotis Patrinos. 2021-11-16. Learning MPC for Interaction-Aware Autonomous Driving: A Game-Theoretic Approach. https://doi.org/10.23919/ecc55457.2022.9838517

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