Abstract: Autonomous vehicle trajectory data holds significant research and practical value,attracting extensive attention to its storage and querying technologies.However,existing trajectory data management solutions are primarily designed for general trajectory data and exhibit notable limitations in efficiently writing autonomous driving trajectory data with high sampling frequency.Additionally,the high cost of index maintenance in dynamic environments makes it challenging to meet the demands of dynamic updates and real-time queries.To address the challenges of achieving high-frequency writes,dynamic updates,and real-time queries for high-sampling-rate and high-real-time trajectory data in autonomous driving scenarios,this paper proposes Truster,an efficient query-oriented clustered storage solution for autonomous vehicle trajectory data.This method includes the design of an encoder and embedder to generate position-aware keys for raw trajectories and extract feature vectors;a storage structure based on a Log-Structured Merge tree(LSM-tree)called the CLSM-tree to achieve clustered storage of similar trajectories;an LCC compaction strategy that leverages locality-sensitive hashing(LSH)for rapid clustering during the compaction of Sorted-String Tables(SSTables);and a trajectory query algorithm that uses multi-granularity cache and bucket mapping to quickly narrow down the search space.Truster not only supports high-frequency writes but also maintains index adaptability to dynamic workloads while offering enhanced query efficiency.Comparative experiments on the real-world autonomous vehicle trajectory dataset Argoverse demonstrate that Truster achieves a 20% to 200% improvement in write performance and a 20% to 100% improvement in query performance compared to existing methods.

Key words: Trajectory data, Trajectory storage, Trajectory query, LSM-tree, LSH