TrajPrism Benchmark Unifies Language and Trajectory Understanding
The introduction of a new benchmark, TrajPrism, aims to connect natural language descriptions with urban trajectory data. This benchmark integrates three distinct tasks: generating trajectories based on instructions, retrieving semantic trajectories driven by language, and captioning trajectories. It combines actual urban trajectories with language annotations that have been filtered by judges, following a four-step methodology. An assessment protocol evaluates trajectory fidelity, the quality of retrieval, and the grounding in language. This research bridges the divide between geometry-focused trajectory modeling and language-oriented mobility benchmarks that prioritize route planning over precise text-route matching. The findings are available on arXiv with the identifier 2605.10782.
Key facts
- TrajPrism is a multi-task benchmark for language-trajectory alignment.
- It unifies instruction-conditioned trajectory generation, semantic retrieval, and captioning.
- The benchmark uses real urban trajectories paired with judge-filtered language annotations.
- Annotations are generated under a four-step process.
- Evaluation protocol measures trajectory fidelity, retrieval quality, and language groundedness.
- Prior work rarely evaluates trajectory and language modalities together on the same real-world data.
- The research is published on arXiv with identifier 2605.10782.
- The work aims to improve fine-grained alignment between text and underlying routes.
Entities
Institutions
- arXiv