Quantum-Resilient Proof-of-Location 4D Semantic-Mapping for Decentralized HD Maps

High-definition (HD) maps have become significant in autonomous driving. However, existing solutions are predominantly controlled by a limited number of companies, which results in fragmented and isolated data, limited transparency, high costs, and slow updates. This paper positions multi-frame fused 4D semantic mapping as a fundamental method for a decentralized solution built on top of Web3.0 technologies. The proposed mapping framework integrates multi-sensor synchronization, vehicle pose estimation, point cloud alignment, dynamic object filtering, and Bird’s Eye View (BEV)-based semantic projection to produce a unified 4D semantic point cloud map. The solution provides centimeter-level accuracy, temporal consistency, and stable semantics. By integrating 4D semantic mapping with Web3.0 technologies such as blockchain for traceability, distributed storage, and token based incentives, it becomes possible to establish a collaborative and transparent mapping model. This paper highlights the technical foundation of 4D semantic mapping, reviews the limitations of centralized HD maps, and explains how Web3.0 can reshape map production, ownership, and management in the autonomous driving industry.