高速动态交通场景下自动驾驶车辆换道意图识别模型研究

Research on Lane Change Intention Recognition Model of Automated Vehicle in High-Speed Dynamic Traffic Scenario

为提高自动驾驶车辆在高速动态复杂交通场景下车辆换道意图识别精度和预判能力,提出了基于融合注意力机制的卷积残差双向长短时记忆(BiLSTM)识别模型。采用一维卷积神经网络提取车辆运动状态特征;将构造的特征向量作为BiLSTM输入信息;通过残差连接,解决多层BiLSTM易出现的优化瓶颈和梯度消失问题;利用注意力机制,调整残差BiLSTM不同时刻输出权重;应用Softmax函数计算驾驶意图概率。采用NGSIM高速公路数据集对模型进行验证,并与其他4种模型进行对比,结果表明:该模型对换道意图整体识别准确率最高,达到97.44%,在换道前2.5 s预测结果准确率达到90%以上,具有更好的识别精度和预判能力。

In order to improve the recognition accuracy and pre-judgment ability of autonomous vehicles in high-speed dynamic complex traffic scenarios,The lane-changing intention recognition model based on convolutional residual Bidirectional Long Short-Term Memory(BiLSTM)with fusion attention mechanism is proposed.It uses the one-dimensional Convolutional Neural Network(CNN)to extract the vehicle’s motion state features.The constructed feature vector is used as the input information of BiLSTM network.The residual connection is used to solve the problems of optimization bottlenecks and gradient disappearance in multi-layer BiLSTM network.It’s achieved to a adjust the weight of the output of the residual BiLSTM network at different moments with the attention mechanism.And the driving intent probability can be calculated by the Softmax function.The validity of the model is verified by using the expressway data set in NGSIM,the performance and effect of the other 4 models are compared with the model.The results show that the recognition accuracy of the lane-changing intention is the highest,which reaches 97.44%,and prediction accuracy of the vehicle’s lane-changing intention is 90%and higher within 2.5 s before the changing lanes,it shows that the model has better intent recognition accuracy and prediction ability.