Conference Agenda

The increasing demand for safe and efficient autonomous vehicle (AV) operations has intensified the need for realistic, high-fidelity digital road representations that enable robust virtual testing environments. Simulation-based validation has become a cornerstone of the AV development process, allowing for the reproducible assessment of perception, localization, and decision-making modules under controlled conditions. Within this context, the ASAM OpenDRIVE specification provides a standardized, XML-based description of static road networks, encapsulating geometric, semantic, and structural elements such as roads, lanes, junctions, and roadside objects. While previous research has primarily focused on the geometric accuracy and semantic richness of High Definition (HD) maps, comprehensive topological analyses—especially those addressing consistency, connectivity, and completeness of OpenDRIVE models—remain largely unexplored. This study aims to fill that gap by introducing a formal topological framework for evaluating OpenDRIVE-based road models through both synthetic and real-world test cases.

This paper presents a comprehensive toolkit for semi-automated High-Definition Maps (HD Maps) production that integrates Artificial Intelligence (AI)-driven feature extraction with 3D human-in-the-loop validation. High-definition maps provide centimeter-level road geometry and traffic asset information, but large-scale production remains costly due to dense mobile mapping data and manual digitization. The proposed workflow consists of two self-developed components: a Semi-automated HD Maps Production Tool for batch extraction and a 3D HD Maps Validation and Editing Tool for structured review. The project-based pipeline ingests georeferenced mobile laser scanning point clouds, Inertial Navigation System / Global Navigation Satellite System (INS/GNSS) trajectories, and camera imagery, and applies configurable chains of ground filtering, road-marking extraction, voxel down-sampling, clustering, oriented bounding box analysis, and AI-based traffic asset detection. Candidate features with confidence indicators and basic attributes are stored in a project database and edited in a tightly coupled 3D environment that supports snapping, constrained adjustments, and semantic reclassification while logging all user edits. The toolkit is evaluated on a closed proving ground (CARLab, Shalun) and a freeway section of Taiwan National Highway No. 3. At CARLab, semi-automated extraction achieves F1-scores of 0.85–0.95 for key layers. For a one-kilometer highway section, operator time is reduced from 90–120 minutes in a purely manual Geographic Information System (GIS) workflow to about 45 minutes with the proposed approach, while maintaining comparable geometric accuracy. These results demonstrate a practical path towards scalable, traceable HD Maps production for autonomous driving applications.

National Key Research and Development Program of China (2025YFB3910300);

3D scene reconstruction lies at the core of computer vision, photogrammetry, and geospatial science, spatial intelligence, aiming for accurate, photorealistic, and efficient digital twin representations of the real world. The emergence of revolutionary 3D Gaussian Splatting (3DGS) enables real-time rendering and geometrically precise reconstruction, yet existing methods struggle in large-scale outdoor scenes with weak textures, low geometric accuracy, dynamic objects, and lack of semantic information. Therefore, geometrically accurate 3D GS with enhanced semantic understanding greatly facilities the realization of digital twins for mobility and navigation. This work proposes a novel 3DGS framework which seamlessly incorporates dense UAV LiDAR point clouds, multi-view images and open-set semantics in an all-in-one optimization process. The key objective here is to investigate how geometric constraints derived from dense UAV LiDAR point clouds and cognitive supervision from SAM (Segment Anything Model) semantics can jointly participate in the optimization of Gaussian primitives, thereby improving geometry accuracy, visual realism, and semantic consistency in large-scale UAV 3D reconstructions for creating digital twins of the environments.