Design, Implementation, and Experimental Validation of a Sensor-Fusion-Based Autonomous Parking Platform

This paper reports the design, implementation, and experimental evaluation of a laboratory autonomous parking platform that supports both perpendicular and parallel parking. The platform integrates three components. First, a depth-image processing pipeline comprising grayscale conversion, Gaussian filtering, Canny edge detection, and Hough transform line extraction is used for parking space detection, together with a pixel-width criterion for distinguishing perpendicular from parallel spaces. Second, a turning-radius trajectory planning strategy based on Ackermann steering geometry determines the steering positions for each parking mode. Third, a fuzzy correction scheme, using membership functions for lateral position and heading angle estimated from web camera imagery, refines the final parking pose. Throughout all maneuvers, LiDAR provides 360° environmental scanning for obstacle detection and emergency stop. Seven test cases covering all supported parking modes were carried out; the parking mode was selected correctly and the maneuver was completed in all seven cases. The evaluation is qualitative, and the design parameters reported here are specific to the hardware configuration used. The contribution is accordingly a transparent and fully documented reference implementation rather than a performance advance.