Ensemble of Experts for Generating Digital Parking Maps from Remote Sensing Images Toyota InfoTech Labs USA

Format:

Book

Conference/Event

Author/Creator:

Shukla, Ajitesh, author.

Cao, Xiaofei, author.

Liu, Yongkang, author.

Takeuchi, Yusuke, author.

Sisbot, Akin, author.

Conference Name:

WCX SAE World Congress Experience (2026-04-14 : Detroit, Michigan, United States)

Language:

English

Subjects (All):

Remote sensing.

Machine learning.

Imaging and visualization.

Optimization.

Local Subjects:

Remote sensing.

Machine learning.

Imaging and visualization.

Optimization.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

A digital parking map with precise parking spot geospatial information is crucial for tasks such as automatic valet parking, parking spot recommendations, and parking route optimization. This paper presents a parking map generation scheme that extracts high-definition parking spot geometry from remote sensing images. These images often suffer from occlusion, inconsistent resolution, and varying luminosity conditions. The proposed scheme utilizes a model ensemble paradigm, integrating multiple machine learning models to enhance the accuracy and quality of the generation of parking maps. The experiments demonstrate that the proposed scheme achieves an 80.5% parking spot detection precision and a center-to-center geometric representation error of 0.93 meters

Notes:

Vendor supplied data

Access Restriction:

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