An MPC-based Task Priority Management Approach for Connected and Automated Vehicles Reference Tracking with Obstacle Avoidance

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

5 Citations (Scopus)

Abstract

We present a reference tracking control problem with obstacle avoidance for connected and automated vehicles. The proposed approach allows to deal with obstacles in the control loop in the first instance, coping with real-time operations while safely waiting for an eventually new planned trajectory from the guidance loop. An obstacle avoidance algorithm is designed to produce suitable constraints for an optimization control problem to be solved via Nonlinear Model Predictive Control. Such an algorithm is based on task priority management, so that the reference tracking task is handled as a lower priority task with respect to the obstacle avoidance task. Automated vehicles are managed in a decentralized fashion, so that they can process independently any sensed potential obstacles, including conventional vehicles. In the presence of vehicle connectivity, vehicles may exchange information about their states to make decisions based on more accurate predictions. The proposed method is evaluated via simulation experiments, for a set of scenarios in the context of urban traffic.

Original languageEnglish
Title of host publication2021 European Control Conference, ECC 2021
PublisherTU Delft Open
Pages813-819
Number of pages7
ISBN (Electronic)978-94-6384-236-5
DOIs
Publication statusPublished - 2021
MoE publication typeA4 Conference publication
EventEuropean Control Conference - Delft, Netherlands
Duration: 29 Jun 20212 Jul 2021
Conference number: ECC

Conference

ConferenceEuropean Control Conference
Abbreviated titleECC
Country/TerritoryNetherlands
CityDelft
Period29/06/202102/07/2021

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