PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions
Fuente:
arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911619007643648 |
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| author | Jing, Taotao Chen, Tina Tian, Renran Chen, Yaobin Domeyer, Joshua Toyoda, Heishiro Sherony, Rini Ding, Zhengming |
| author_facet | Jing, Taotao Chen, Tina Tian, Renran Chen, Yaobin Domeyer, Joshua Toyoda, Heishiro Sherony, Rini Ding, Zhengming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2112_02604 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions Jing, Taotao Chen, Tina Tian, Renran Chen, Yaobin Domeyer, Joshua Toyoda, Heishiro Sherony, Rini Ding, Zhengming Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2112.02604 |