Autonomous Car Parking Assistant Using YOLOv11 and Route Optimization with Streamlit Interface

DOI

10.2991/978-94-6239-713-2_45How to use a DOI?

Keywords

YOLOv11; autonomous parking assistant; A* algorithm; route optimization; Streamlit; computer vision; smart cities

Abstract

Autonomous parking assistance systems are increasingly required to improve the efficiency of vehicle parking in organized environments while reducing the reliance on costly sensor infrastructure. This paper presents a vision-based autonomous parking assistant that detects parking slots and provides navigation guidance using computer vision and pathfinding techniques. The system utilizes a pretrained YOLO-based object detection model, loaded from the trained weight file (best.pt), to identify parking slot regions in top-view parking lot images. The detection model predicts bounding boxes representing parking slots and determines their occupancy status. From the detected bounding boxes, the center coordinates of available parking slots are computed and used as navigation targets. To guide vehicles to an available slot, the system uses the A* pathfinding algorithm to compute the shortest path from the parking entrance to a selected vacant slot in a grid-based representation of the parking layout. Occupied parking slots are treated as obstacles during route computation, enabling efficient path planning. The entire workflow is integrated into an interactive Streamlit application that allows users to upload parking lot images, visualize detection outputs, select available parking spaces, and observe the computed navigation path. Experimental evaluation demonstrates strong detection performance with a precision of 92.4%, a recall of 94.1%, and an F1-score of 93.2%, and route computation completes in approximately 200 ms. The proposed approach demonstrates the feasibility of combining vision-based parking slot detection with grid-based navigation for smart parking assistance.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - J. Jalil Fasith
AU  - R. Sriram
AU  - Rs. Vignesh
PY  - 2026
DA  - 2026/06/25
TI  - Autonomous Car Parking Assistant Using YOLOv11 and Route Optimization with Streamlit Interface
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 607
EP  - 622
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_45
DO  - 10.2991/978-94-6239-713-2_45
ID  - Fasith2026
ER  -