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.

A survey on socially aware robot navigation: Taxonomy and future challenges

“…In addition to these aspects, negotiation in socially aware robot navigation may also involve resolving conflicts or obstacles that arise during task execution (Toghi et al 2021), as well as coordinating actions and sharing resources with other robots or autonomous systems. By effectively negotiating with these parties, the robot can facilitate cooperation and coordination, enabling it to achieve its goals and to complete tasks more effectively (Evens et al 2022).…”