心理学的特徴とTransformer融合による歩行者横断意図予測
Pedestrian Crossing Intent Prediction via Psychological Features and Transformer Fusion
歩行者の横断意図を、注意・位置・状況・相互作用の4つの行動ストリームをTransformerで融合し、不確実性も考慮して予測する軽量モデルを提案した。PSIベンチマークで高い性能を示し、選択的予測により精度も向上した。
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著者: Sima Ashayer, Hoang H. Nguyen, Yu Liang, Mina Sartipi
分類: cs.CV, cs.RO
原文アブストラクト
Pedestrian intention prediction needs to be accurate for autonomous vehicles to navigate safely in urban environments. We present a lightweight, socially informed architecture for pedestrian intention prediction. It fuses four behavioral streams (attention, position, situation, and interaction) using highway encoders, a compact 4-token Transformer, and global self-attention pooling. To quantify uncertainty, we incorporate two complementary heads: a variational bottleneck whose KL divergence captures epistemic uncertainty, and a Mahalanobis distance detector that identifies distributional shift. Together, these components yield calibrated probabilities and actionable risk scores without compromising efficiency. On the PSI 1.0 benchmark, our model outperforms recent vision language models by achieving 0.9 F1, 0.94 AUC-ROC, and 0.78 MCC by using only structured, interpretable features. On the more diverse PSI 2.0 dataset, where, to the best of our knowledge, no prior results exist, we establish a strong initial baseline of 0.78 F1 and 0.79 AUC-ROC. Selective prediction based on Mahalanobis scores increases test accuracy by up to 0.4 percentage points at 80% coverage. Qualitative attention heatmaps further show how the model shifts its cross-stream focus under ambiguity. The proposed approach is modality-agnostic, easy to integrate with vision language pipelines, and suitable for risk-aware intent prediction on resource-constrained platforms.
