道路全息感知与未来交通安全研究中心

关于中心

“道路全息感知与未来交通安全研究中心”以“多校联盟”的合作框架为基础，以
“道路全息感知与未来交通安全”为主要研究方向，联合同济大学、肯尼索州立
大学、奥本大学、东南大学、上海交通大学、上海海事大学、山东交通学院等
交通领域海内外知名高校，构建以海内外知名教授专家为核心团队的世界一流
合作研究和学术交流平台。

中心介绍
合作伙伴
中心大事记
科学研究

针对各种道路条件下驾驶行为特性进行研究，开展道路交通驾驶行为海量数据采
集与预处理、构建驾驶行为描述与人工智能模型、驾驶行为谱特征参数提取方法、
驾驶行为特征谱建库等方面的研究，代表性研究项目：“十三五”国家重点研发计
划公共安全专项“道路交通安全主动防控技术及系统集成”，国家自然科学基金项
目“基于人工智能建模的危险驾驶行为识别和风险评估”等。

道路交通驾驶行为解析及主动安全防控
交通安全分析理论及保障技术研究
道路全息感知与智能检测方法
数字化平台与未来交通安全
产学研合作
科研成果

1.Xue, Q., Xing, Y., & Lu, J. (2022). An integrated lane change prediction
model incorporating traffic context based on trajectory data.
Transportation Research Part C: Emerging Technologies, 141, 103738.
(SCI)……

科研论文
发明专利
荣誉奖励
标准规范
出版专著
中心人员

陆键教授团队深耕交通安全领域，针对车辆、交通行为开展了主动安全防控关键
技术研究，建立了面向人、车、路的点线面相结合的综合防控干预成套理论与技
术体系。近年来主持国家重点研发计划，国家自然科学基金等国家级及省部级课
题50余项，在国际一流杂志发表了学术研究论文近200余篇，出版专著八部。

中心负责人
研究员及博士后
特聘专家
研究生
相关课程

本课程是面向工科专业一年级本科学生的大类基础课。课程定位于培养学生用投
影方法在平面上以图形来表示空间的几何形体和几何形体之间关系的能力，培养
学生正确绘制、识读组合体二维图样，培养学生对三维形体的空间想象能力、空
间逻辑思维表达能力，培养学生阅读工程施工图和用尺规……

本科生课程
研究生课程
英文课程

科研成果

PUBLICATIONS

道路驾驶行为解析及主动安全防控

Road Traffic Driving behavior and proactive safety prevention and control

本团队该领域最新论文：

• Xue, Q., Xing, Y., & Lu, J. (2022). An integrated lane change prediction model incorporating traffic context based on trajectory data. Transportation Research Part C: Emerging Technologies, 141, 103738. (SCI)
• Xue, Q.W., Gao, K., Xing, Y.Y., Lu, J.*, Qu, X.B. (2021). A Context-Aware Framework for Risky Driving Behavior Evaluation Based on Trajectory Data. IEEE Intelligent Transportation Systems Magazine, doi: 10.1109/MITS.2021.3120279. (SCI)
• Chen. S.D., Xue, Q.W.*, Zhao. X.C., et al. (2021). Risky driving behavior recognition based on vehicle trajectory. International journal of environmental research and public health, 2021, 18(23): 12373. (SCI)
• Peng, Y., Cheng, L., Jiang, Y., & Zhu, S. (2021). Examining Bayesian network modeling in identification of dangerous driving behavior. PLoS one, 16(8), e0252484. (SCI)
• Wang, K., & Lu, J. J. (2021). A Two-Layer Risky Driver Recognition Model with Context Awareness. IEEE Access, 9, 138483-138495. (SCI)
• Wang, K., Xue, Q., & Lu, J. J. (2021). Risky driver recognition with class imbalance data and automated machine learning framework. International journal of environmental research and public health, 18(14), 7534. (SCI)
• Xue, Q.W, Lu, J., Gao, K. (2021). Driving Style Recognition Incorporating Risk Surrogate by Support Vector Machine. Smart Transportation Systems 2021. Springer, Singapore, 2021: 123-131. (EI)
• Chen, Y, Lu, J, Wang, K.*. (2021). Individual Difference Analysis of Driving Behaviors Using Kinematic Vehicle Data. Transportation Research Board 100th Annual Meeting.
• 彭一川,李崇奕,王可,邢莹莹.基于权重的欠采样提升算法识别激进驾驶员[J].武汉理工大学学报(交通科学与工程版),2021,45(02):195-201.
• Peng, Y., Li, C., Wang, K., Gao, Z., & Yu, R. (2020). Examining imbalanced classification algorithms in predicting real-time traffic crash risk. Accident Analysis & Prevention, 144, 105610. (SCI)
• Wang, K., Xue, Q., Xing, Y., & Li, C. (2020). Improve aggressive driver recognition using collision surrogate measurement and imbalanced class boosting. International journal of environmental research and public health, 17(7), 2375. (SCI)
• Xue, Q.W, Wang, K.*, Lu, J. (2020). Driving Behavior Evaluation Incorporating Context Awareness by Machine Learning. Transportation Research Board 99th Annual Meeting.
• 王可, 陆键, 蒋愚明. 基于车辆行驶轨迹的道路不良驾驶行为谱构建与特征值计算方法[J]. 交通运输工程学报, 2020, 20(6): 236-249
• 陆键, 王可, 蒋愚明. 基于车辆行驶轨迹的道路不良驾驶行为实时辨识方法[J]. 交通运输工程学报, 2020, 20(6).
• 薛清文, 蒋愚明, 陆键. 基于轨迹数据的危险驾驶行为识别方法[J]. 中国公路学报, 2020, 33(6): 84-94
• Xue, Q.W., Wang, K.*, Lu, J., Liu, Y.J. (2019). Rapid driving style recognition in car- following using machine learning and vehicle trajectory data. Journal of advanced transportation, 2019(PT.1), 1-11. (SCI)
• Xue, Q.W, Wang, K.*, Lu, J. (2019). Rapidly Driving Pattern Recognition Based on Rear-End Collision Risk. Transportation Research Board 98th Annual Meeting.

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1.Xue, Q., Xing, Y., & Lu, J. (2022). An integrated lane change prediction model incorporating traffic context based on trajectory data. Transportation Research Part C: Emerging Technologies, 141, 103738. (SCI)……

1.Xue, Q., Xing, Y., & Lu, J. (2022). An integrated lane change prediction model incorporating traffic context based on trajectory data. Transportation Research Part C: Emerging Technologies, 141, 103738. (SCI)……

1.Xue, Q., Xing, Y., & Lu, J. (2022). An integrated lane change prediction
model incorporating traffic context based on trajectory data.
Transportation Research Part C: Emerging Technologies, 141, 103738.
(SCI)……

1.Xue, Q., Xing, Y., & Lu, J. (2022). An integrated lane change prediction
model incorporating traffic context based on trajectory data.
Transportation Research Part C: Emerging Technologies, 141, 103738.
(SCI)……