Year: 2022
Type: conference-paper
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.
Institution: KU Leuven
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: Learning to interact safely with humans: A game-theoretic control framework for next-generation autonomous systems, G2350939788, Structured Low-Rank matrix / tensor approximation: numerical optimization-based algorithms and applications, A unifying algorithmic framework for structured optimization, Learning to optimize nonconvex problems: provably and efficiently, G4829754272, Optimization frameworks for deep kernel machines, Risk-averse Control and Dynamic Optimization: Bridging the Gap Between Robust and Stochastic Control, G7564167306, G8258744815
OpenAlex ID: W3213700780