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计算机系统应用:2020,29(3):55-63
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基于深度学习的驾驶员换道行为预测
(长安大学 信息工程学院, 西安 710064)
Driver’s Lane-Changing Behavior Prediction Based on Deep Learning
(School of Information Engineering, Chang'an University, Xi'an 710064, China)
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本文已被:浏览 814次   下载 227
投稿时间:2019-08-08    修订日期:2019-09-05
中文摘要: 车道变换在交通安全中起着至关重要的作用,准确预测驾驶员的车道变换行为可以显著提高驾驶安全性.本文提出了一种基于全连接神经网络和循环神经网络的混合神经网络,用于精准预测车道变换行为.并且提出动态时间窗口,提取包括驾驶员生理数据和车辆运动学数据的车道变换特征.最后,通过真实交通场景下的数据验证了所提出模型的有效性.此外,将所提出的模型与五种其他预测模型进行了比较,结果表明,与其他模型相比,本文所提出的预测模型具有更高的精确率和前瞻时间.
Abstract:Lane change plays a vital role in traffic safety. Accurately predicting the driver's lane change behavior can significantly improve driving safety. In this study, a hybrid neural network based on fully connected neural network and recurrent neural network is proposed to accurately predict lane change behavior. And a dynamic time window is proposed to extract lane change features including driver physiological data and vehicle kinematics data. Finally, the effectiveness of the proposed model is verified by the data in real traffic scenarios. In addition, the proposed model is compared with five other predictive models. The results show that the proposed model has higher accuracy and perspective time than other models.
文章编号:7310     中图分类号:    文献标志码:
基金项目:国家自然科学基金(61603058)
引用文本:
惠飞,魏诚.基于深度学习的驾驶员换道行为预测.计算机系统应用,2020,29(3):55-63
HUI Fei,WEI Cheng.Driver’s Lane-Changing Behavior Prediction Based on Deep Learning.COMPUTER SYSTEMS APPLICATIONS,2020,29(3):55-63

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