AI-Driven Mapping System for Smart Parking Management Applications Using an INS-GNSS-Solid-State LiDAR-Monocular Camera Fusion Engine Empowered by HD Maps

Efficient parking management is crucial in crowded Asian cities to optimize limited road space and parking facilities. The increasing vehicle ownership rate in Taiwan has intensified the demand for street parking, leading to excessive driving in search of available spots and contributing up to 30% of traffic congestion. This paper proposes a low-cost, infrastructure-free outdoor roadside parking management system based on high-definition (HD) map updating. The system fuses data from a solid-state LiDAR (SSL) system, a monocular camera, an inertial navigation system, a GPS, and HD maps followed by deep-learning-based efficient region extraction. The goal was to achieve high accuracy with minimal computational resources and infrastructure costs. The proposed system’s performance for dynamic HD map object updating was evaluated through parking management tests. The system’s costs were low due to the selection of SSL and monocular cameras. Traditional and novel extrinsic calibration methods were compared in various experiments, and a hardware architecture for precise sensor time synchronization was designed. Software algorithms for accurate image–point-cloud projection were developed to update HD map parking layers. By using normal distribution transform matching of the SSL and HD point cloud maps, navigation performance was achieved to 0.4-meter accuracy. When applied to license plate localization in two experimental scenarios, the mean performance error was approximately 0.48 and 0.62 m.