mlsanityData to 7 Oct 2026

PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions

arXiv 2112.02604 ↗First posted 5 Dec 2021cs.CV cs.AIcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 23 of its 75 references that are in the map. First posted on arXiv 5 Dec 2021.

Builds on

23 of 75 references.

References · 23

  1. 2017Attention Is All You Needcited by 59,932
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  4. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 532
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  10. 2019Grounding Human-to-Vehicle Advice for Self-driving Vehiclescited by 71
  11. 2020Spatiotemporal Relationship Reasoning for Pedestrian Intent Predictioncited by 71
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11 more
  1. 2020TITAN: Future Forecast using Action Priorscited by 58
  2. 2021LOKI: Long Term and Key Intentions for Trajectory Predictioncited by 57
  3. 2021Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attentioncited by 48
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 8; 2025: 3; 2026: 3.

17 papers.

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  1. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 52
  2. 2022Behavioral Intention Prediction in Driving Scenes: A Surveycited by 30
  3. 2024RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Modelscited by 20
  4. 2025Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Reviewcited by 8
  5. 2023Learning Pedestrian Actions to Ensure Safe Autonomous Drivingcited by 4
  6. 2023A Survey on Datasets for Decision-making of Autonomous Vehiclecited by 2
  7. 2024Diving Deeper Into Pedestrian Behavior Understanding: Intention Estimation, Action Prediction, and Event Risk Assessmentcited by 2
  8. 2024Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approachcited by 2
  9. 2024Pedestrian Motion Prediction Using Transformer-based Behavior Clustering and Data-Driven Reachability Analysiscited by 1
  10. 2024Online Residual Learning from Offline Experts for Pedestrian Trackingcited by 1
  11. 2024Gating Syn-to-Real Knowledge for Pedestrian Crossing Prediction in Safe Drivingcited by 1
  12. 2025Pedestrian Intention and Trajectory Prediction in Unstructured Traffic Using IDD-PeDcited by 1
5 more
  1. 2025VLMs Guided Interpretable Decision Making for Autonomous Drivingcited by 1
  2. 2026From Detection to Understanding: TAR and TAR-Bench for Multi-Task Traffic Anomaly Reasoningcited by 1
  3. 2024A low complexity contextual stacked ensemble-learning approach for pedestrian intent predictioncited by 0
  4. 2026COSI-Lab: Conference Living Lab for Modeling Multi-Perspective Multimodal Social Intentioncited by 0
  5. 2026TAU-Agent: An Agentic Retrieval-Augmented Framework for Traffic Anomaly Understandingcited by 0