드라이브 ㆍ 컨트롤 (Journal of Drive and Control)

  • 제19권4호
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  • Pages.110-116
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  • 2022
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  • 2671-7972(pISSN)
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  • 2671-7980(eISSN)
유공압건설기계학회 (The Korean Society for Fluid Power and Construction Equipment)
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가상 환경에서의 강화학습 기반 긴급 회피 조향 제어

Reinforcement Learning based Autonomous Emergency Steering Control in Virtual Environments

  • Lee, Hunki (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Kim, Taeyun (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Kim, Hyobin (Department of Mechanical Engineering, Sungkyunkwan University) ;
  • Hwang, Sung-Ho (Department of Mechanical Engineering, Sungkyunkwan University)
  • 투고 : 2022.11.08
  • 심사 : 2022.11.28
  • 발행 : 2022.12.01
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초록

Recently, various studies have been conducted to apply deep learning and AI to various fields of autonomous driving, such as recognition, sensor processing, decision-making, and control. This paper proposes a controller applicable to path following, static obstacle avoidance, and pedestrian avoidance situations by utilizing reinforcement learning in autonomous vehicles. For repetitive driving simulation, a reinforcement learning environment was constructed using virtual environments. After learning path following scenarios, we compared control performance with Pure-Pursuit controllers and Stanley controllers, which are widely used due to their good performance and simplicity. Based on the test case of the KNCAP test and assessment protocol, autonomous emergency steering scenarios and autonomous emergency braking scenarios were created and used for learning. Experimental results from zero collisions demonstrated that the reinforcement learning controller was successful in the stationary obstacle avoidance scenario and pedestrian collision scenario under a given condition.

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과제정보

본 연구는 국토교통부/국토교통과학기술진흥원 교통물류연구사업의 연구비지원 (22TLRP-C152478-04)과 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터육성지원사업의 연구결과로 수행된 결과물입니다. (IITP-2022-2018-0-01426)

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