1 citations · 1 across the 8 of their papers we have counts for
8 papers
Parallel Dynamic Programming for Conic Linear Quadratic Control
Luyao Zhang, Gabriel Bravo-Palacios, Brian Plancher +1
Linear Quadratic (LQ) control problems are at the heart of linear control theory and Model Predictive Control (MPC). While performant, standard approaches to solving such problems…
Contingency Planning for Safety-Critical Autonomous Vehicles: A Review and Perspectives
Lei Zheng, Luyao Zhang, Peiqi Yu +4
Contingency planning is the architectural capability that enables autonomous vehicles (AVs) to anticipate and mitigate discrete, high-impact hazards, such as sensor outages and adv…
Wasserstein Distributionally Robust Nash Equilibrium Seeking with Heterogeneous Data: A Lagrangian Approach
Zifan Wang, Georgios Pantazis, Sergio Grammatico +2
We study a class of distributionally robust games where agents are allowed to heterogeneously choose their risk aversion with respect to distributional shifts of the uncertainty. I…
Non-Euclidean Enriched Contraction Theory for Monotone Operators and Monotone Dynamical Systems
Diego Deplano, Sergio Grammatico, Mauro Franceschelli
We adopt an operator-theoretic perspective to analyze a class of nonlinear fixed-point iterations and discrete-time dynamical systems. Specifically, we study the Krasnoselskij iter…
Parallel Branch Model Predictive Control on GPUs
Luyao Zhang, Chenghuai Lin, Sergio Grammatico
We present a GPU-based solver for trajectory planning problems using branch Model Predictive Control. Building on iterative LQR methods, we adopt a multiple-shooting formulation fo…
Nash equilibrium seeking for a class of quadratic-bilinear Wasserstein distributionally robust games
Georgios Pantazis, Reza Rahimi Baghbadorani, Sergio Grammatico
We consider a class of Wasserstein distributionally robust Nash equilibrium problems, where agents construct heterogeneous data-driven Wasserstein ambiguity sets using private samp…
An Efficient Risk-aware Branch MPC for Automated Driving that is Robust to Uncertain Vehicle Behaviors
Luyao Zhang, George Pantazis, Shaohang Han +1
One of the critical challenges in automated driving is ensuring safety of automated vehicles despite the unknown behavior of the other vehicles. Although motion prediction modules…
Automated Lane Merging via Game Theory and Branch Model Predictive Control
Luyao Zhang, Shaohang Han, Sergio Grammatico
We propose an integrated behavior and motion planning framework for the lane-merging problem. The behavior planner combines search-based planning with game theory to model vehicle…