Vehicle abnormal trajectory detection based on attention mechanism
With the expansion of urban traffic,there are more and more safety hazards,and vehicle track anomaly detection has become more and more important in the field of driving safety.In order to better extract the features of the trajectory,convolu-tional neural network is added on the basis of recurrent neural network detection.The convolutional and cyclic neural network de-tection is used to learn the trajectory sequence information,and the attention mechanism is combined.Through this method,the quality of trajectory embedding is further improved.The results show that the anomaly detection performance of the proposed trajec-tory anomaly detection method is significantly better than other detection algorithms in various indexes,which verifies the effective-ness and practicability of the proposed anomaly detection method.
NETL
NSTL
万方数据
