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.

The automotive industry is rapidly shifting from hardware-focused design to Software Defined Vehicles (SDVs), where functions are flexibly updated through software. Embedded systems are central to this transition, ensuring real-time data processing and control across sensors, actuators, and controllers. Yet, most autonomous driving education and competitions have been designed for senior students, creating high entry barriers for early undergraduates. This study proposes an embedded practice-based education model for lower-year students, implemented through an autonomous driving competition. Arduino was adopted as an accessible embedded platform, enabling rapid prototyping and intuitive learning of sensor–controller–actuator integration. The curriculum was structured to advance from interrupt-based programming to Real-Time Operating System (RTOS)-based task scheduling, providing stepwise exposure to core SDV concepts. The model was validated through a mission-oriented competition that included line following, obstacle avoidance, and stop-line detection tasks. Dual assessment—combining technical performance indicators with rubric-based educational outcomes— demonstrated both algorithmic feasibility and pedagogical effectiveness. This work highlights that early undergraduates can gain meaningful SDV-oriented embedded control experience through lightweight competitions. The proposed framework offers an effective pathway for cultivating the next-generation mobility workforce, bridging the gap between theoretical education and practical implementation in the SDV era.