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一个行人群穿越意图预测模型整合了时空特征
Hai Zou1, Yongqing Guo2, Fulu Wei1
1School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, 255000, China.
Scientific reports
|July 2, 2025
概括
预测行人穿越意图是道路安全的关键. 本研究介绍了一个使用时空数据和群组信息的模型,以改善自动驾驶汽车的行人行为预测.
科学领域:
- 计算机科学 计算机科学
- 机器人技术 机器人技术 机器人技术
- 运输工程 运输工程
背景情况:
- 步行者是脆弱的道路使用者 (VRU),在交通事故中缺乏保护.
- 准确的行人行为预测对于先进的驾驶辅助系统 (ADAS),自动驾驶汽车和交通管理至关重要.
研究的目的:
- 为自动驾驶开发一个行人群穿越意图预测模型.
- 通过整合时空特征和群体动态来提高预测准确度.
主要方法:
- 该模型整合了时空特征:行人姿势关键点,2D位置轨迹和组信息.
- 使用JAADbeh和JAADall数据集进行实验验证.
主要成果:
- 拟议的模型在准确性,精度和F1-Score方面表现出卓越的性能.
- 在大规模的JAADall数据集上实现了0.82准确度,显示了稳定性.
- 结合行人群信息显著提高了预测准确度,特别是对于群体.
结论:
- 该研究为自动驾驶和智能交通系统中的行人意图预测提供了可靠的框架.
- 为未来研究非视觉特征和复杂情景提供基础.
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