The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Parking Path Planning For Autonomous Vehicle Based on Deep Learning Model

자율주행차량의 주차를 위한 딥러닝 기반 주차경로계획 수립연구

  • Ji hwan Kim (Transportation Planning & Management, Korea National University of Transportation) ;
  • Joo young Kim (Transportation Planning & Management, Korea National University of Transportation)
  • 김지환 (국립한국교통대학교 교통정책학과) ;
  • 김주영 (국립한국교통대학교 교통정책학과)
  • Received : 2024.06.19
  • Accepted : 2024.08.01
  • Published : 2024.08.31
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Abstract

Several studies have focused on developing the safest and most efficient path from the current location to the available parking area for vehicles entering a parking lot. In the present study, the parking lot structure and parking environment such as the lane width, width, and length of the parking space, were vaired by referring to the actual parking lot with vertical and horizontal parking. An automatic parking path planning model was proposed by collecting path data by various setting angles and environments such as a starting point and an arrival point, by putting the collected data into a deep learning model. The existing algorithm(Hybrid A-star, Reeds-Shepp Curve) and the deep learning model generate similar paths without colliding with obstacles. The distance and the consumption time were reduced by 0.59% and 0.61%, respectively, resulting in more efficient paths. The switching point could be decreased from 1.3 to 1.2 to reduce driver fatigue by maximizing straight and backward movement. Finally, the path generation time is reduced by 42.76%, enabling efficient and rapid path generation, which can be used to create a path plan for autonomous parking during autonomous driving in the future, and it is expected to be used to create a path for parking robots that move according to vehicle construction.

자율주차의 요소 중 하나인 경로계획(Path-planning)을 제안한다. 실제 주차장을 참고하여 수직주차와 수평주차로 주차장의 차로 너비, 주차 공간의 너비, 길이 등 주차장 구조와 주차 환경을 다양하게 설정한다. 출발점와 도착지점 등 각도와 환경을 다양하게 설정하여 경로데이터를 수집하고 수집한 데이터를 Deep Learning model에 넣어 학습시켜 자동주차경로계획 모델을 제안한다. 분석결과, 기 알고리즘(Hybrid A-star, Reeds-Shepp Curve)과 딥러닝 모델 모두 장애물에 충돌하지 않고 비슷한 경로를 생성하지만, 거리와 소모시간이 각각 0.59%, 0.61% 감소하여 효율적인 경로가 생성되었다. 또한, Switching point도 1.3개에서 1.2개로 감소하여 직진과 후진을 최대한으로 줄여 운전자의 피로를 줄일 수 있을거라 생각된다. 마지막으로 경로생성시간은 42.76% 감소하여 효율적이고 신속한 경로생성이 가능하여 향후 자율주행 중 자율주차의 경로 계획생성에 활용될 수 있으며, 차량작도에 따라 이동하는 주차로봇의 경로생성에도 활용될 수 있을 것으로 보인다.

Keywords

Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No.2022R1C1C1005640)

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