Traffic Accident Prediction Method Based on Local Relational Features and Attention Mechanisms
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.
NETL
NSTL
万方数据
