This paper presents the design and experimental validation of an intelligent tire alignment and lifting control system for an under-vehicle autonomous parking robot. The proposed system enables the robot to autonomously enter beneath a vehicle, recognize tire positions using a LiDAR-based sensing module, and perform precise lifting through a fork-type mechanism. A YOLOv8 instance segmentation algorithm is employed to detect tire regions from LiDAR point cloud data and estimate their geometric centers. The detected tire positions are then matched with a vehicle database to determine the correct alignment for lifting. Experiments were conducted on three different vehicle types under various surface conditions. The results show that the proposed system achieved a tire recognition accuracy exceeding 95%, a lifting success rate of 100%, and an average lifting operation time of 12.3 seconds. These results demonstrate the reliability and practicality of the proposed method for real-world autonomous parking applications.

Abstract
1. 서 론
2. 시스템 구성
    2.1 전체 시스템
    2.2 구동부 및 리프팅 메커니즘
    2.3 제어부 구성
    2.4 인식부 구성
3. 라이다 기반 타이어 인식 알고리즘
    3.1 포인트 클라우드 데이터 전처리
    3.2 YOLOv8 기반 타이어 영역 분할 및 중심 추정
4. 라이다 기반 타이어 정렬 및 리프팅 제어알고리즘
    4.1 차량 하부 진입 경로 및 정렬 알고리즘
    4.2 리프팅 동작 제어 시퀀스
5. 실험 및 결과 분석
    5.1 인공지능 학습 데이터 구축 및 학습
    5.2 다양한 차종 및 주차 조건에서의 인식 성능평가
    5.3 로봇 진입 차량 리프팅 제어 결과
6. 결론 및 향후 과제
References

• 최승준(Halla University, Dept. of Future Mobility Engineering) | Seung Jun Choi
• 고국원(Dept. of Future Mobility Engineering) | Kuk Won Ko Corresponding author