arXiv cs.CY· Wanchun Ni, Tao Qi, Leonel Aguilar, Jiugeng Sun, Marlene Wagner, Verena Zimmermann, Mennatallah El-Assady·· 11 小时前AI 评分29
STRIDE：面向多样化场景的文本到轨迹对齐自动评估框架
STRIDE: Automated Evaluation of Text-to-Trajectory Alignment across Diverse Contexts
AI 导读
STRIDE 是首个评估场景描述与行人轨迹之间上下文对齐的框架，无需人工轨迹数据即可实现可验证、自动化的规模化评估。其实例 STRIDE-Bench 覆盖 30 张真实地图、1K 场景、6K 行为问题与 11K 测量，人类一致性达 80%。对多个文本到轨迹模型的评测显示，上下文对齐能力有限，细粒度上下文条件控制仍是持续挑战。
正文 · 原文
Abstract:Language-conditioned trajectory generation is here, but its evaluation has not kept pace. Existing pedestrian trajectory metrics compare trajectories with real-world human data. This does not scale to text-to-trajectory generation across diverse contexts, as collecting human trajectories for every scenario is costly and infeasible. Moreover, pedestrian behavior is heterogeneous and context-dependent, with no single metric as the correct answer, and current evaluation frameworks are not transferable to this domain. These challenges make scalable, reliable evaluation difficult. We introduce STRIDE, the first framework for evaluating context alignment between scenario descriptions and pedestrian trajectories. STRIDE addresses these challenges through three design choices. First, we derive our VRDST evaluation protocol from sociological theories to define a complete evaluation space. Second, it decomposes high-level context into scenario-adaptive behavioral questions. Third, every question is resolved against a deterministic measurement tool library that yields reproducible answers. Together, STRIDE enables verifiable, automated, and scalable evaluation across diverse contexts without requiring human trajectory data. We instantiate STRIDE in the crowd domain as STRIDE-Bench, comprising 1K scenarios, 6K behavioral questions, and 11K measurements with calibrated expected answers across 30 real-world maps. Comprehensive human validations show that STRIDE-Bench is consistent with human behavior and judgment, achieving 80% human agreement. We further evaluate several text-to-trajectory models, finding limited context-alignment capability and persistent challenges in fine-grained context conditioning. We believe that the STRIDE framework provides a first step toward principled evaluation of context-aligned pedestrian trajectory generation.
| Comments: | Accepted at NeurIPS 2026, Evaluations & Datasets Track |
| Subjects: | Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2609.34799 [cs.AI] |
| (or arXiv:2609.34799v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.34799 arXiv-issued DOI via DataCite |
Submission history
From: Wanchun Ni [view email]
[v1]
Mon, 28 Sep 2026 10:01:45 UTC (2,661 KB)
[v2]
Tue, 29 Sep 2026 09:09:24 UTC (2,661 KB)
[v3]
Wed, 4 Nov 2026 14:44:10 UTC (2,664 KB)
来源：arXiv cs.CY · arxiv.org