基于LSTM-att的车辆异常驾驶行为识别

车辆的异常行为可能引发交通事故, 甚至造成经济损失和人员伤亡. 准确识别车辆异常行为可以预防潜在的危险. 针对现有研究存在的数据难以保留时间特征等问题, 本文提出一种带有注意力层的长短记忆神经网络的识别模型, 利用真实交通场景车辆异常轨迹对所提出的模型进行训练和验证. 实验结果表明, 所提出的模型能够有效的识别车辆异常驾驶行为, 准确率可达到98.4%.

Recognition of Vehicle Abnormal Driving Behaviors Based on LSTM-att

Abnormal behaviors of vehicles may cause traffic accidents or even economic losses and casualties. Accurate recognition of abnormal vehicle behaviors can prevent potential hazards. To tackle the problems in existing studies, such as difficulty to retain the time characteristics of data, this study proposes a recognition model of long short-term memory (LSTM) neural network with an attention layer, and trains and verifies the proposed model by using abnormal vehicle trajectories in real traffic scenes. The experimental results show that the proposed model can effectively recognize abnormal driving behaviors with accuracy reaching 98.4%.