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

arXiv cs.CV / 4/27/2026

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Key Points

The PSI paper introduces a benchmark dataset focused on autonomous driving scenarios involving pedestrian crossing interactions and driver decision-making.
It models the dynamic evolution of pedestrian crossing intentions from the driver’s perspective and adds human textual explanations tied to both intention estimation and driving decisions.
The benchmark is designed to support multiple standardized tasks, including pedestrian intention prediction, driver decision modeling, reasoning generation, and trajectory forecasting.
PSI aims to enable more causal and interpretable evaluation of autonomous driving models by aligning system reasoning with human cognitive processes.

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.