| 摘要: |
| 目的 在智能驾驶领域,准确预测行人穿越行为对于确保车辆和行人安全至关重要。 方法 设计了一种结合
多种计算机视觉技术的行人穿越行为预测模型,该模型通过分析行人的位置、姿态、动作及环境特征来准确判断行
人意图。 为了增强模型对不同距离行人的感知能力,采用了不同尺度放大的预处理和数据滤波平滑的后处理技
术。 提出了先条件后预测(Predict after Condition,PAC)两阶段方法,以实现更为有效的行人穿越预测。 结果 基于
JAAD 数据集的测试结果表明:所提模型平均精度达 89. 31%,相较于传统单阶段方法提升了 8. 76%。 结论 特征重
要度分析进一步表明:加入路面面积特征后,预测准确率从 68. 43%显著提升至 85. 06%,强调了行人位置与路面轮
廓关系在行人穿越行为研究中的重要性。 对降低人车碰撞事故,提高智能驾驶车辆的安全性具有重要意义。 |
| 关键词: 行人穿越行为预测 多特征融合 行人行为 智能驾驶 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Pedestrian Crossing Behavior Prediction Based on Multi-Source Feature Fusion |
|
HOU Linpeng1,YANG Chaoyu2
|
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1. School of Computer Science and Engineering Anhui University of Science & Technology Huainan 232001 Anhui
China
2. School of Artificial Intelligence Anhui University of Science & Technology Huainan 232001 Anhui China
|
| Abstract: |
| Objective Accurately predicting pedestrian crossing behavior is crucial for ensuring the safety of both vehicles
and pedestrians in the field of intelligent driving. Methods A pedestrian crossing behavior prediction model was
designed which combined various computer vision technologies. The model accurately assessed pedestrian intentions by
analyzing their positions postures actions and environmental features. To enhance the model?? s perception capability for
pedestrians at different distances preprocessing with multi-scale magnification and post-processing for data filtering and
smoothing were employed. A two-stage method called prediction after condition PAC was proposed to achieve more
effective prediction of pedestrian crossings. Results Testing results based on the JAAD dataset indicated that the proposed
model achieved an average accuracy of 89. 31% representing an improvement of 8. 76% compared with traditional singlestage methods. Conclusion Further analysis of feature importance shows that after incorporating the road surface area
feature the prediction accuracy significantly increases from 68. 43% to 85. 06% highlighting the importance of the
relationship between pedestrian location and the road profile in the study of pedestrian crossing behavior. This has
significant implications for reducing vehicle-pedestrian collision accidents and enhancing the safety of intelligent driving
vehicles. |
| Key words: pedestrian crossing behavior prediction multi-feature fusion pedestrian behavior intelligent driving |
