Adaptive confidence threshold selection improves F1 scores in explainable multi-task classification for autonomous driving and is supported by a new 958-image dataset.
PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions
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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.
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Beyond Fixed Thresholds and Domain-Specific Benchmarks for Explainable Multi-Task Classification in Autonomous Vehicles
Adaptive confidence threshold selection improves F1 scores in explainable multi-task classification for autonomous driving and is supported by a new 958-image dataset.