汽车工程 ›› 2020, Vol. 42 ›› Issue (11): 1464-1472.doi: 10.19562/j.chinasae.qcgc.2020.11.003

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无人驾驶汽车周边车辆行为识别算法研究*

蔡英凤1, 邰康盛2, 王海2, 李祎承1, 陈龙1   

  1. 1.江苏大学汽车工程研究院,镇江 212013;
    2.江苏大学汽车与交通工程学院,镇江 212013
  • 收稿日期:2020-01-05 出版日期:2020-11-25 发布日期:2021-01-25
  • 通讯作者: 王海,教授,博士,E-mail:wanghai1019@163.com
  • 基金资助:
    * 国家自然科学基金(51875255,U1764264)、国家重点研发计划(2017YFB0102603)、江苏省自然科学基金(BK20180100)、江苏省六大人才高峰项目(2018-TD-GDZB-022)和江苏省战略性新兴产业发展重大专项(苏发改高技发(2016)1094号)资助。

Research on Behavior Recognition Algorithm of Surrounding Vehicles for Driverless Car

Cai Yingfeng1, Tai Kangsheng2, Wang Hai2, Li Yicheng1, Chen Long1   

  1. 1. Institude of Automotive Engineering, Jiangsu University, Zhenjiang 212013;
    2. Institude of Automotive and Transportation Engineering, Jiangsu University, Zhenjiang 212013
  • Received:2020-01-05 Online:2020-11-25 Published:2021-01-25

摘要: 周边车辆行为识别对于提升无人驾驶汽车决策规划的合理性和控制安全性至关重要。传统的周边车辆行为识别方法识别精度普遍不高,且缺乏对交通主体相互之间邻域影响的考虑,算法鲁棒性较差。针对此,本文中提出了一种SLSTMAT(Social-LSTM-Attention)算法,创新性地引入目标车辆社交特征并通过卷积神经网络提取,建立了基于深度学习的车辆行为识别模型,应用注意力机制来捕捉行为时窗中的多时步信息,实现了周边车辆行为准确识别。采用HighD轨迹数据集和实车数据进行算法验证。结果表明,所提算法对周边车辆行为识别的准确率达94.01%,在目标车辆到达换道点的前1 s时刻行为识别精度达90%,具有较高的工程应用价值。

关键词: 无人驾驶汽车, 行为识别, 长短时记忆网络, 注意力机制, 社交特征

Abstract: The behavior recognition of surrounding vehicles is very important to improve the decision-making rationality and the control safety for driverless cars. Traditional methods of surrounding vehicle’s behavior recognition suffer from low accuracy and poor robustness, without considering the interaction between adjacent traffic objects. In order to solve these problems, SLSTMAT (Social-LSTM-Attention) algorithm is proposed to achieve high accuracy for surrounding vehicle’s behavior recognition. The social characteristics of target vehicle are innovatively introduced and extracted by convolutional neural network. Based on deep learning,the recognition model for vehicle behavior is established. Attention mechanism is applied to capture multiple time-step information in the behavior time window. The algorithm is verified by HighD trajectory data set and real vehicle data. The results show that the accuracy rate of SLSTMAT for surrounding vehicle’s behavior recognition reaches 94.01%, and the precision of behavior recognition reaches 90% at 1s before the target vehicle driving to the lane change point, which has high engineering application value

Key words: driverless cars, behavior recognition, long and short term memory network, attention mechanism, social characteristics