Design and robustness analysis of autonomous vehicles in intersections

Péter Szilassy, Balázs Németh, Péter Gáspár

Research output: Contribution to journalConference article


The paper proposes the design of a neural-network-based control strategy of autonomous vehicles in intersections. The motivation of the neural network approach is to reduce the numerically-intensive computation of the optimization problem in which the motions of autonomous vehicles are formed. In the method the neural network is trained through a preliminary optimal off-line solution. Moreover, a robustness analysis on the neural network based control strategy is proposed. The focus of the analysis is to consider the impact of the position and speed estimation errors on the motion of vehicles. The design and the analysis are illustrated through CarSim simulation examples.

Original languageEnglish
Pages (from-to)13-18
Number of pages6
Issue number8
Publication statusPublished - Jan 1 2019
Event10th IFAC Symposium on Intelligent Autonomous Vehicles, IAV 2019 - Gdansk, Poland
Duration: Jul 3 2019Jul 5 2019



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

ASJC Scopus subject areas

  • Control and Systems Engineering

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