Flexible MPC-based Conflict Resolution Using Online Adaptive ADMM

2021-03-25 · Jerry An, Giulia Giordano, Changliu Liu

Decentralized conflict resolution for autonomous vehicles is needed in many places where a centralized method is not feasible, e.g., parking lots, rural roads, merge lanes, etc. However, existing methods generally do not fully utilize optimization in decentralized conflict resolution. We propose a decentralized conflict resolution method for autonomous vehicles based on a novel extension to the Alternating Directions Method of Multipliers (ADMM), called Online Adaptive ADMM (OA-ADMM), and on Model Predictive Control (MPC). OA-ADMM is tailored to online systems, where fast and adaptive real-time optimization is crucial, and allows the use of safety information about the physical system to improve safety in real-time control. We prove convergence in the static case and give requirements for online convergence. Combining OA-ADMM and MPC allows for robust decentralized motion planning and control that seamlessly integrates decentralized conflict resolution. The effectiveness of our proposed method is shown through simulations in CARLA, an open-source vehicle simulator, resulting in a reduction of 47.93% in mean added delay compared with the next best method.

📄 PDF Abstract BibTeX arXiv:2103.14118

Code (0)

등록된 구현이 없습니다.

Tasks

Autonomous VehiclesModel Predictive ControlMotion Planning

Methods 이 논문이 사용한 방법론

Entropy Regularization 설명 없음

PPO Proximal Policy Optimization, or PPO, is a policy gradient method for reinforcement learning. The motivation was to have an algorithm with the data efficiency and reliable…

CARLA CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban…

ADMM The alternating direction method of multipliers (ADMM) is an algorithm that solves convex optimization problems by breaking them into smaller pieces, each of which are…

An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM

2025-02-28 · Luke Lozenski, Michael T. McCann, Brendt Wohlberg

This work presents a new method for online selection of multiple penalty parameters for the alternating direction method of multipliers (ADMM) algorithm applied to optimization problems with multiple constraints or funct…

Adaptive Consensus ADMM for Distributed Optimization

2017-06-09 · ICML 2017 8 · Zheng Xu, Gavin Taylor, Hao Li, Mario Figueiredo 외

The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined penalty parameters. We study distributed…

Distributed Optimization

Adaptive ADMM with Spectral Penalty Parameter Selection

2016-05-24 · Zheng Xu, Mario A. T. Figueiredo, Tom Goldstein

The alternating direction method of multipliers (ADMM) is a versatile tool for solving a wide range of constrained optimization problems, with differentiable or non-differentiable objective functions. Unfortunately, its …

A Simple Adaptive Unfolding Network for Hyperspectral Image Reconstruction

2023-01-24 · Junyu Wang, Shijie Wang, Wenyu Liu, Zengqiang Zheng 외

We present a simple, efficient, and scalable unfolding network, SAUNet, to simplify the network design with an adaptive alternate optimization framework for hyperspectral image (HSI) reconstruction. SAUNet customizes a R…

Image Reconstruction

Learning context-aware adaptive solvers to accelerate quadratic programming

2022-11-22 · Haewon Jung, Junyoung Park, Jinkyoo Park

Convex quadratic programming (QP) is an important sub-field of mathematical optimization. The alternating direction method of multipliers (ADMM) is a successful method to solve QP. Even though ADMM shows promising result…