Abstract

Institut Teknologi Sepuluh Nopember (ITS) has successfully implemented an Autonomous Car (i-CAR) as a mode of commuter transportation within the campus. One of the difficulties in implementing the maneuver is when passing roundabouts which are scattered on the i-CAR route. This paper discusses i-CAR's maneuvers on roundabouts by implementing Deep Reinforcement Learning. The i-CAR vehicle is modeled in the CARLA simulation environment, then tested in a virtual environment in roundabouts with intersections and roundabouts without intersections that simulate U-turn maneuvers. The Deep Reinforcement method used is Deep Que Network (DQN) using various reward function configurations. Through experiments using CARLA simulations, it was obtained that Icar was able to pass roundabouts with and without intersections with an average deviation angle of 27.011 degrees and 30.068 degrees, respectively. The average time needed to pass the roundabout is 13.3 seconds and 7.9 seconds, with an average speed of 27.0 kmph and 28.5 kmph. This speed is still acceptable on campus, where the driving speed inside is limited to 40 kmph.

Original languageEnglish
Title of host publication2023 International Seminar on Intelligent Technology and Its Applications
Subtitle of host publicationLeveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages473-478
Number of pages6
ISBN (Electronic)9798350313956
DOIs
Publication statusPublished - 2023
Event24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023 - Hybrid, Surabaya, Indonesia
Duration: 26 Jul 202327 Jul 2023

Publication series

Name2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding

Conference

Conference24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023
Country/TerritoryIndonesia
CityHybrid, Surabaya
Period26/07/2327/07/23

Keywords

  • Autonomous Vehicle
  • Deep Learning
  • Reinforcement Learning
  • Simulation

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