Merging Vehicles at Junctions using Mixed-Integer Model Predictive Control

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

A method is proposed for vehicle merging scenarios in junctions with relative cost prioritization. The method is based on Model Predictive Control, employing Mixed Integer Quadratic Program optimization. The scheme provides optimal control properties while maintaining safety and recursive feasibility. The latter properties are ensured through positive control invariance of simple time headway constraints. For examples with two vehicles, tunable prioritization and gap acceptance are verified and presented on a decision graph. Priorities are then demonstrated to be respected in an example with four vehicles.
Original languageUndefined/Unknown
Title of host publication2018 European Control Conference (ECC)
Pages1740-1745
Number of pages6
DOIs
Publication statusPublished - 1 Jun 2018

Keywords

  • graph theory
  • integer programming
  • optimal control
  • position control
  • predictive control
  • quadratic programming
  • road traffic control
  • vehicles
  • merging vehicles
  • junctions
  • mixed-integer model predictive control
  • relative cost prioritization
  • optimal control properties
  • recursive feasibility
  • positive control invariance
  • tunable prioritization
  • gap acceptance
  • mixed integer quadratic program optimization
  • time headway constraints
  • decision graph
  • Junctions
  • Merging
  • Predictive control
  • Automobiles
  • Optimal control
  • Safety
  • Sequential analysis

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