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Autonomous Overtaking Assistant for Country Road Scenarios

  • Fisnik Sulejmani
  • , Florian Reiterer
  • , Amin Assadi
  • , Luigi Del Re

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

While Advanced Driver Assistance Systems (ADAS) are becoming the longer the more common, most interest has been devoted either to highway traffic or to special situations like parking. Against this background, this paper focuses on overtaking maneuvers on country roads, and a stochastic model predictive control (MPC) algorithm is implemented to manage this task. The behavior of the surrounding vehicles is predicted stochastically by using Bayesian networks. The overtaking algorithm is tested for various country road traffic scenarios in a stochastic traffic environment. To compare the performance of the controller to that of a real human driver a dedicated study in this stochastic traffic environment is carried out. The results of this study show that the control algorithm provides the safest trip in acceptable travel time.
Original languageEnglish
Title of host publication2020 American Control Conference (ACC)
Pages1217-1222
Number of pages6
ISBN (Electronic)9781538682661
DOIs
Publication statusPublished - Jul 2020

Publication series

NameProceedings of the American Control Conference
Volume2020-July
ISSN (Print)0743-1619

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Fields of science

  • 206002 Electro-medical engineering
  • 207109 Pollutant emission
  • 202 Electrical Engineering, Electronics, Information Engineering
  • 202027 Mechatronics
  • 202034 Control engineering
  • 203027 Internal combustion engines
  • 206001 Biomedical engineering

JKU Focus areas

  • Digital Transformation
  • Sustainable Development: Responsible Technologies and Management

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