用人类推理方式预测被遮挡行人，提升自动驾驶安全性
Prediction of Occluded Pedestrians in Road Scenes using Human-like Reasoning: Insights from the OccluRoads Dataset
- 结合知识图谱与贝叶斯推理，模拟人类对遮挡行人的判断逻辑
- 在新数据集上实现0.91的F1分数，比传统方法提升42%
- 适合研究自动驾驶感知、视觉推理与人机认知对齐的学者
行人检测是自动驾驶中的关键任务，旨在提升道路安全。近年来检测性能虽有显著提升，但在遮挡行人（尤其是完全不可见者）场景下仍远不及人类感知能力。本文提出OccluRoads数据集，包含真实与虚拟环境中多样化的道路场景，涵盖部分及完全遮挡的行人，并配有精细标注与人类感知相关的上下文信息。基于该数据集，我们构建了融合知识图谱（KG）、知识图谱嵌入（KGE）与贝叶斯推理的预测流程，实现了0.91的F1分数，较传统机器学习模型最高提升42%。
原文摘要 · Abstract (English)
Pedestrian detection is a critical task in autonomous driving, aimed at enhancing safety and reducing risks on the road. Over recent years, significant advancements have been made in improving detection performance. However, these achievements still fall short of human perception, particularly in cases involving occluded pedestrians, especially entirely invisible ones. In this work, we present the Occlusion-Rich Road Scenes with Pedestrians (OccluRoads) dataset, which features a diverse collection of road scenes with partially and fully occluded pedestrians in both real and virtual environments. All scenes are meticulously labeled and enriched with contextual information that encapsulates human perception in such scenarios. Using this dataset, we developed a pipeline to predict the presence of occluded pedestrians, leveraging Knowledge Graph (KG), Knowledge Graph Embedding (KGE), and a Bayesian inference process. Our approach achieves a F1 score of 0.91, representing an improvement of up to 42% compared to traditional machine learning models.
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