視覚言語基盤モデルによる歩行者意図予測
Pedestrian Intention Prediction via Vision-Language Foundation Models
視覚言語基盤モデルと階層的プロンプトを用いて歩行者の横断意図を予測し、車速や時間情報の統合とプロンプト最適化により精度を最大19.8%向上させた研究。
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著者: Mohsen Azarmi, Mahdi Rezaei, He Wang
分類: cs.CV, cs.AI, cs.ET, cs.LG, cs.RO
原文アブストラクト
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.
