[2112.02604] PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2112.02604 (cs) [Submitted on 5 Dec 2021 ( v1 ), last revised 23 Apr 2026 (this version, v3)] Title: PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions Authors: Taotao Jing , Tina Chen , Renran Tian , Yaobin Chen , Joshua Domeyer , Heishiro Toyoda , Rini Sherony , Zhengming Ding View a PDF of the paper titled PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions, by Taotao Jing and 7 other authors View PDF HTML (experimental) Abstract: Accurately modeling pedestrian intention and understanding driver decision-making processes are critical for the development of safe and socially aware autonomous driving systems. We introduce PSI, a benchmark dataset that captures the dynamic evolution of pedestrian crossing intentions from the driver's perspective, enriched with human textual explanations that reflect the reasoning behind intention estimation and driving decision making. These annotations offer a unique foundation for developing and benchmarking models that combine predictive performance with interpretable and human-aligned reasoning. PSI supports standardized tasks and evaluation protocols across multiple dimensions, including pedestrian intention prediction, driver decision modeling, reasoning generation, and trajectory forecasting and more. By enabling causal and interpretable evaluation, PSI advances research toward autonomous systems that can reason, act, and explain in alignment with human cognitive processes. Comments: Published in NeurIPS 2025 datasets and benchmarks track Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2112.02604 [cs.CV] (or arXiv:2112.02604v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2112.02604 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Renran Tian [ view email ] [v1] Sun, 5 Dec 2021 15:54:57 UTC (10,335 KB) [v2] Sat, 11 Jun 2022 21:08:21 UTC (21,332 KB) [v3] Thu, 23 Apr 2026 22:37:38 UTC (21,004 KB) Full-text links: Access Paper: View a PDF of the paper titled PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions, by Taotao Jing and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2021-12 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar DBLP - CS Bibliography listing | bibtex Joshua E. Domeyer Heishiro Toyoda Rini Sherony Taotao Jing Zhengming Ding export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from