Design of the optimal motions of autonomous vehicles in intersections through neural networks

Balázs Németh, P. Gáspár, Dávid Szőcs, András Mihály

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The handling of vehicle interactions is a challenge in the research into the traveling of autonomous vehicles. This paper focuses on collision-free motion design of autonomous vehicles to guarantee their minimum traveling time in intersections. First, a decision logic of the order of the vehicles in intersections is proposed. Based on the decision logic a constrained nonlinear optimization method is also proposed, with which the minimum traveling time of the vehicles without their collision is guaranteed. Since the on-line solution of the nonlinear optimization task can be numerically complex, a neural network based approximation of the optimal solution is developed. The efficiency of the method with various intersection scenarios is shown in the CarSim simulation environment.

Original languageEnglish
Pages (from-to)19-24
Number of pages6
JournalIFAC-PapersOnLine
Volume51
Issue number9
DOIs
Publication statusPublished - Jan 1 2018

Keywords

  • autonomous vehicles
  • constrained nonlinear optimization
  • intersections
  • neural networks

ASJC Scopus subject areas

  • Control and Systems Engineering

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