Journal of Transportation Systems Engineering and Information Technology ›› 2022, Vol. 22 ›› Issue (4): 63-71.DOI: 10.16097/j.cnki.1009-6744.2022.04.007

Special Issue: 2022年英文专栏

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Vehicle Lane Change Intention Recognition Driven by Trajectory Data

ZHAO Jian-dong* a, b, ZHAO Zhi-min a , QU Yun-chao c , XIE Dong-fan a , SUN Hui-juna   

  1. a. School of Traffic and Transportation; b. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport; c. State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
  • Received:2022-03-06 Revised:2022-04-21 Accepted:2022-05-07 Online:2022-08-25 Published:2022-08-22
  • Supported by:
    National Key Research and Development Program(2019YFB1600200);National Natural Science Foundation of China(71871011,71931002)。

轨迹数据驱动的车辆换道意图识别研究

赵建东* a, b,赵志敏a,屈云超c,谢东繁a,孙会君a   

  1. 北京交通大学,a. 交通运输学院;b. 综合交通运输大数据应用技术交通运输行业重点实验室; c. 轨道交通控制与安全国家重点实验室,北京 100044
  • 作者简介:赵建东(1975- ),男,山西忻州人,教授,博士。
  • 基金资助:
    国家重点研发计划;国家自然科学基金

Abstract: 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 overallaccuracy of 97.37% for vehicle lane change intention recognition with an iteration time of 6.66 s, which is at most 9.89% 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 performance.

Key words: intelligent transportation, lane change intention recognition, data-driven, gated recurrent unit neural network, attention mechanism

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。

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

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