基于局部关系特征和注意力机制的交通事故预测方法
Traffic Accident Prediction Method Based on Local Relational Features and Attention Mechanisms
张亚辉 1李颖 2刘天恩1
作者信息
- 1. 燕山大学 机械工程学院,河北,秦皇岛 066004
- 2. 北京理工大学 机械与车辆学院,北京 100081
- 折叠
摘要
基于相机的事故预测方法主要是建立交通对象的全局关系,而缺乏对局部关系的考虑.为此,提出一种基于局部关系特征和注意力机制的预测模型,实现车载相机实时预测交通事故风险.该模型首先引入局部关系多图网络,捕捉车辆间的局部交互关系,解决交通对象的局部交互信息应用不充分的问题.其次利用动态空间注意力机制确定交通事故风险车辆.最后将门控循环网络和动态时间注意力机制联合,有效利用动态场景当前帧和历史帧间的时序信息.事故数据集的实验结果表明,该模型准确率达到 73.78%,提前 1.55s预测交通事故风险,同时单帧预测时间为1.65 ms,具有卓越的实时性.为交通事故风险预测提供一种有效的解决方案.
Abstract
The accident prediction methods with cameras are mainly used to establish the global relationships among traffic objects,lacking the consideration of local relationships among them.Therefore,a predictive mod-el was proposed based on local relational features and attention mechanisms to carry out real-time traffic acci-dent risk prediction with vehicle-mounted cameras.Firstly,a local relational multi-graph network was incorpor-ated to capture the local interactions of vehicles,solving the insufficient application issue of local interaction in-formation about the traffic objects.Subsequently,a dynamic spatial attention mechanism was adopted to identify the risk vehicles at traffic accident.Finally,the Gated Recurrent Unit and dynamic temporal attention mechan-ism were integrated to effectively utilize the temporal information between the current and historical frames in dynamic scenes.Experimental results on the accident dataset show that the proposed model can predict accident in 1.55 seconds advance with a prediction accuracy of 73.78%and 1.65 ms single-frame prediction time,realiz-ing excellent real-time effectiveness,and providing an effective solution for the traffic accident prediction.
关键词
交通事故预测/图神经网络/注意力机制Key words
traffic accident prediction/graph neural network/attention mechanism引用本文复制引用
出版年
2025
NETL
NSTL
万方数据