Pedestrian Intention Prediction via Vision-Language Foundation Models

Mohsen Azarmi; Mahdi Rezaei; He Wang

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arXiv · 2507.04141

Pedestrian Intention Prediction via Vision-Language Foundation Models

Mohsen Azarmi·Mahdi Rezaei·He Wang

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Abstract

Prediction of pedestrian crossing intention is a critical function in autonomous vehicles. Conventional vision-based methods of crossing intention prediction often struggle with generalizability, context understanding, and causal reasoning. This study explores the potential of vision-language foundation models (VLFMs) for predicting pedestrian crossing intentions by integrating multimodal data through hierarchical prompt templates. The methodology incorporates contextual information, including visual frames, physical cues observations, and ego-vehicle dynamics, into systematically refined prompts to guide VLFMs effectively in intention prediction. Experiments were conducted on three common datasets-JAAD, PIE, and FU-PIP. Results demonstrate that incorporating vehicle speed, its variations over time, and time-conscious prompts significantly enhances the prediction accuracy up to 19.8%. Additionally, optimised prompts generated via an automatic prompt engineering framework yielded 12.5% further accuracy gains. These findings highlight the superior performance of VLFMs compared to conventional vision-based models, offering enhanced generalisation and contextual understanding for autonomous driving applications.

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cs.CV cs.AI cs.ET cs.LG cs.RO

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Mohsen Azarmi, Mahdi Rezaei, He Wang. 2025-07-05. Pedestrian Intention Prediction via Vision-Language Foundation Models. https://arxiv.org/abs/2507.04141

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