轨迹数据驱动的车辆换道意图识别研究  

Vehicle Lane Change Intention Recognition Driven by Trajectory Data

作　　者：赵建东 赵志敏[1] 屈云超 谢东繁 孙会君[1] ZHAO Jian-dong;ZHAO Zhi-min;QU Yun-chao;XIE Dong-fan;SUN Hui-jun(School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China;Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100044,China;State Key Laboratory of Railway Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]北京交通大学交通运输学院,北京100044 [2]北京交通大学综合交通运输大数据应用技术交通运输行业重点实验室,北京100044 [3]北京交通大学轨道交通控制与安全国家重点实验室,北京100044

出　　处：《交通运输系统工程与信息》2022年第4期63-71,共9页Journal of Transportation Systems Engineering and Information Technology

基　　金：国家重点研发计划(2019YFB1600200);国家自然科学基金(71871011,71931002)。

摘　　要：为实现准确识别车辆换道意图,提高车辆行驶安全性,综合考虑车辆换道过程的时空特性及不同特征对车辆的影响程度,提出一种基于卷积神经网络(CNN)与门控循环神经网络(GRU)组合并融合注意力机制的换道意图识别模型。首先,筛选和平滑处理车辆轨迹数据,将车辆轨迹数据分为向左换道、向右换道及直线行驶3类,构建换道意图样本集。其次,构建融合注意力机制的CNN_GRU模型,识别换道意图样本集,考虑到行驶过程中车辆之间的交互性,将被预测车辆和周围车辆的位置和速度信息作为模型的输入,经过CNN层特征提取的特征作为GRU层的输入,经过注意力机制层对不同的特征增加不同的权重系数,利用Softmax层识别换道意图。最后,选用NGSIM中US-101数据集的轨迹数据验证融合注意力机制的CNN_GRU模型性能,同时,与LSTM、GRU、CNN_GRU及CNN_LSTM_Att等模型进行对比分析。验证结果表明,所提模型车辆换道意图识别整体准确率达到97.37%,迭代时间为6.66 s,相比于其他模型准确率最多提高9.89%,最少提高2.1%。分析不同预判时间下的意图识别,模型可在车辆换道前2 s内均能识别换道意图,准确率在89%以上,表现出良好的识别性能。In order to accurately identify the vehicle’s lane-changing intention and improve the driving safety of the vehicle,I comprehensively considered the spatiotemporal characteristics of the vehicle’s lane-changing process and the influence of different characteristics on the vehicle,and proposed a lane-changing intention recognition model with attention mechanism,which is based on the combination of Convolutional Neural Network(CNN)and Gated Recurrent Unit Neural Network(GRU).Firstly,I filtered and smoothed the vehicle trajectory data,and divided the vehicle trajectory data into three categories:left lane change,right lane change,and straight driving,so as to construct a sample set of lane change intention.Secondly,I built a CNN_GRU model that integrates attention mechanism to identify the sample set of lane change intention.Considering the interaction between vehicles during driving,I utilized the position,the speed information of the predicted vehicle and surrounding vehicles as the input of the model.After the CNN layer feature extraction,I then chose the extracted features as the input of GRU layer.And I also added different weight coefficients to different features through the attention mechanism layer,and leveraged the Softmax layer to identify the lane change intention.Finally,I verified the performance of CNN_GRU model with fused attention mechanism by using the trajectory data of US-101 dataset in NGSIM,and at the same time,compared and analyzed it with LSTM,GRU,CNN_GRU and CNN_LSTM_Att models.The results showed that the proposed model achieves an overall accuracy of 97.37%for vehicle lane change intention recognition with an iteration time of 6.66 s,which is at most9.89%and at least 2.1%improvement in accuracy compared to other models.By analyzing the accuracy of intention recognition at different pre-determination times,we know that the intention to change lanes can be accurately recognized within 2 s before the vehicle changes lanes,and the accuracy rate is above 89%,so the model has good recognition

关 键 词：智能交通 换道意图识别 数据驱动 门控神经单元网络 注意力机制 

分 类 号：U495[交通运输工程—交通运输规划与管理]