个人简介
鲁光泉 ，博士，北京航空航天大学长聘教授，博士生导师，入选国家级科技创新领军人才、交通运输部交通运输行业中青年科技创新领军人才、中国智能交通协会中青年科技创新领军人才， 国家重点研发计划项目负责人，国家一流本科课程负责人，教育部高等学校交通运输类专业教学指导委员会交通工程教学指导分委员会委员，中国智能交通协会交通安全专业委员会秘书长，中国道路运输协会专家委员会特聘专家，中国道路交通安全协会、中国交通教育研究会理事。
长期致力于交通运输工程学科的研究与教学，重点在车路系统安全建模与协同优化、交通科技发展战略等方向开展研究；是《国家综合立体交通网规划纲要》编制工作专项组成员，中国工程院交通强国、综合交通工程科技2030、2035等重大咨询项目的主要执笔人之一；获得教育部自然科学二等奖、中国公路学会科学技术特等奖、教育部技术发明一等奖、中国智能交通协会科技进步一等奖、地理信息科技进步一等奖、中国交通运输协会科技进步二等奖各1项；出版学术专著2部、译著2部和教材2部。
教育经历
1992.9-1996.7
哈尔滨工业大学 | 内燃机 | 学士学位 | 本科
1998.9-2004.3
吉林大学 | 载运工具运用工程 | 硕士学位 | 硕士研究生毕业
2001.9-2004.6
吉林大学 | 载运工具运用工程 | 博士学位 | 博士研究生毕业
工作经历
2006.9-至今
交通科学与工程学院 | 北京航空航天大学 讲师、副教授、教授
2004.10-2006.9
汽车工程系 | 清华大学 博士后
1996.7-2006.5
交通工程学院 | 昆明理工大学 助教、讲师、副教授
科研项目
[1]国家自然基金重点项目：智能网联新型混合交通流协同控制
[2]重点研发计划项目：车路协同系统要素耦合机理与协同优化方法
[3]国家自然基金联合基金重点：人车路协同环境下的驾驶人工作负荷变化规律与控制权智能交互机理研究
[4]国家863计划课题：车车交互式协同控制系统关键技术,科学技术部
[5]国家自然基金：基于图像序列分析的道路交叉口交通冲突理论与方法
不断改善安全、提高出行效率、节约能源消耗、降低交通对环境的影响是交通科学与工程技术发展的动力。从信息化到智能化是解决一系列交通问题的有效途径。我们重点围绕交通安全改善与效率提升这两大主题，以深入的数据分析和智能化技术为手段，在驾驶行为、车路协同等方面开展深入研究。
一、驾驶行为建模
以风险动态平衡理论和行为补偿理论为基础，研究驾驶人的驾驶行为特征及行为建模。主要的学术贡献包括：提出了一个描述驾驶人跟驰过程的主观危险感量化指标（安全裕度，SM）并建立了基于主观危险感行为补充的跟驰模型（期望安全裕度模型，DSM），可用于异质性、随机性跟驰行为的量化分析，拟人化（个性化）的ACC控制等；提出了一种行车过程的风险场统一量化方法，可以用于不同行车场景下的风险统一量化，行车安全预警与自动驾驶轨迹规划等；发现了交叉口闯黄灯数量与交通量的非线性关系；发现了交叉口冲突车辆先行/让行的主要影响因素及让行/先行决策规律；解释了信号交叉口通行黄灯决策的心理机制。
1、跟驰行为
[1] Lu, G. *, Bo, C. , Lin, Q. , & Wang, Y. . (2012). Quantitative indicator of homeostatic risk perception in car following. Safety Science, 50(9).
[2] Tan, H. , Lu, G. * , and Liu, M. . Risk Field Model of Driving and Its Application in Modeling Car-Following Behavior. IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2021.3105518.
[3] Lu, G. *, Cheng, B. , Wang, Y. , & Lin, Q. . (2013). A car-following model based on quantified homeostatic risk perception. Mathematical Problems in Engineering, 165-185.
[4] Zhang, J. , Wang, Y. , & Lu, G. *. (2019). Impact of heterogeneity of car-following behavior on rear-end crash risk. Accident Analysis & Prevention, 125(APR.), 275-289.
[5] Wang, Y. , Zhang, J . , & Lu, G . * . (2019). Influence of driving behaviors on the stability in car following. IEEE Transactions on Intelligent Transportation Systems. 20 (3):1081-1098.
[6] 鲁光泉, 刘倩, 王云鹏, 陈鹏, & 丁川. (2018). 一种基于风险动态平衡理论的弯道跟驰模型. ZL201810758357.4.
[7] 鲁光泉, 王云鹏, 吴梦晓, 陈鹏, & 丁川. (2017) 一种基于V2V的弯道车头时距与车头间距计算方法. ZL201610956587.2.
2、交叉口通行行为
[8] Hua, J., Lu, G. *, & Liu, H. X. (2022). Modeling and simulation of approaching behaviors to signalized intersections based on risk quantification. Transportation research part C: emerging technologies, 142, 103773.
[9] Lu, G . *, Wang, Y . , Wu, X . , Liu, H . (2015). Analysis of yellow-light running at signalized intersections using high-resolution traffic data. Transportation Research Part A: Policy and Practice. 73, 39-52.
[10] Lu, G. *, Liu, M. , Wang, Y. , Wan, H. , & Tian, D. . (2015). Logit-based analysis of drivers' crossing behavior at unsignalized intersections in China. Human Factors the Journal of the Human Factors & Ergonomics Society, 57(7), 1101-1114.
[11] Liu, M. , Wang, Y. , Lu, G. *, & Zhang, Z. . (2014). Logit-based merging behavior model for uncontrolled intersections in china. Journal of Transportation Engineering, 140(12), 04014059.
[12] Lu, G. *, Kong, L. , Wang, Y. , & Tian, D. . (2014). Vehicle trajectory extraction by simple two-dimensional model matching at low camera angles in intersection. IET Intelligent Transport Systems. 8(7), 631-638.
[13] Liu, M. , Lu G. *, Wang, Y. , & Zhang Z. . (2014). Preempt or yield? an analysis of driver's dynamic decision making at unsignalized intersections by classification tree. Safety Science. 65, 36-44.
[14] Liu, M. , Lu, G. *, Wang, Y. , & Zhang, Z. . (2014). Analyzing drivers' crossing decisions at unsignalized intersections in China. Transportation Research Part F: Psychology & Behaviour. 24, 244-255.
[15] 鲁光泉, 谭海天, 陈发城, 丁川. (2021). 一种基于风险动态平衡的无信号交叉口车辆运动规划方法. ZL202010438570.4
二、协同控制优化
对智能网联汽车的通行控制，在国内最早在实车上实现了基于车车通信的车辆运动协同控制（2013年），提出了一种从单交叉口到路网的智能网联汽车协同控制方法。
[16] Wang, Y. , Cai, P. , & Lu, G. *. (2020). Cooperative autonomous traffic organization method for connected automated vehicles in multi-intersection road networks. Transportation Research Part C Emerging Technologies, 111, 458-476.
[17] 鲁光泉,王云鹏,田大新.车车协同安全控制技术（专著）.科学出版社.2014
[18] 鲁光泉,田大新,王云鹏.车用安全通信-协议、安全及隐私（译著）.北京理工大学出版社.2015
[19] 鲁光泉, 潘日佩, 王云鹏, 陈鹏, & 丁川. (2017). 一种车车通信环境下考虑车辆间相对位置的多车协同定位算法. ZL201710564723.8.
[20] 鲁光泉, 李良, 王云鹏, 田大新, 余贵珍, & 于海洋等. (2013). 一种基于车车协同的跟驰辅助控制系统. ZL201310170268.5.
三、自动驾驶接管
针对L3级自动驾驶汽车接管过程，从接管的安全性、稳定性、及时性方面开展研究。主要的学术成果包括：把信任问题引入接管过程分析，系统分析了接管行为特征及信任度对接管行为的影响；对影响接管安全性、稳定性、及时性的因素进行了系统分析；系统分析了自动驾驶专用车道对临近车道车辆行为的影响。
[21] Chen, F., Lu, G. *, Tan, H., Liu, M., & Wan, H. (2022). Effects of assignments of dedicated automated vehicle lanes and inter-vehicle distances of automated vehicle platoons on car-following performance of nearby manual vehicle drivers. Accident Analysis & Prevention, 177, 106826.
[22] Jin, M. , Lu, G. *, Chen, F. , Shi, X. , Tan, H. , & Zhai, J. . (2021). Modeling takeover behavior in level 3 automated driving via a structural equation model: considering the mediating role of trust. Accident Analysis & Prevention, 157(1), 106156.
[23] Lu, G. , Zhai, J. , Li, P. , Chen, F. , & Liang, L. * . (2021) Measuring drivers’ takeover performance in varying levels of automation: Considering the influence of cognitive secondary task. Transportation Research Part F: Traffic Psychology and Behaviour, 82, 96-110
[24] Chen, F. , Lu, G. *, Lin, Q. , Zhai, J. , & Tan, H. . (2021). Are novice drivers competent to take over control from level 3 automated vehicles? a comparative study with experienced drivers. Transportation Research Part F Traffic Psychology and Behaviour, 81(1), 65-81.
[25] 鲁光泉,赵鹏云,王兆杰,林庆峰.自动驾驶中视觉次任务对年轻驾驶人接管时间的影响[J].中国公路学报,2018,31(04):165-171.
[26] 鲁光泉,陈发城,李鹏辉,翟俊达,谭海天,赵鹏云.驾驶人跟车风险接受水平对其接管绩效的影响[J].汽车工程,2021,43(06):808-814.
[27] 林庆峰,王兆杰,鲁光泉*.城市道路环境下自动驾驶车辆接管绩效分析[J].中国公路学报,2019,32(06):240-247.
[28] 鲁光泉, 石茜, 丁川, & 陈发城. (2020). 一种基于皮电反应确定汽车防撞预警系统预警时刻的方法. ZL201910985621.2.
四、交通安全、交通状态与交通可靠性
对交通安全状态、交通运行状态和交通可靠性的预测开展了研究。主要的学术成果包括：提出了交通冲突和交通事故的非线性关系模型；提出了考虑上下游交通状态关联性的交通状态预测方法；提出了不同服务水平交互作用下的城市主干路出行时间可靠性计算模型。
[29] Lu, G. *, Cheng, B. , Kuzumaki, S , & Mei, B. . (2011). Relationship between road traffic accidents and conflicts recorded by drive recorders. Traffic Injury Prevention, 12(4), 320-326.
[30] Cai, P. , Wang, Y. , & Lu, G. *. (2018). Tunable and transferable rbf model for short-term traffic forecasting. IEEE Transactions on Intelligent Transportation Systems, 1-11.
[31] Cai, P. , Wang Y. , Lu, G. *, Chen, P. , Ding, C. , Sun , J. . (2016). A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting. Transportation Research Part C: Emerging Technology. 62 :21-34.
[32] Li, D. , Fu, B. , Wang, Y. , Lu, G. , Berezin, Y. , & Stanley, H. E. , et al. (2015). Percolation transition in dynamical traffic network with evolving critical bottlenecks. Proceedings of the National Academy of Sciences, 112(3), 669-672.
[33] Lei, F. , Wang, Y. , Lu, G. *, & Sun, J. . (2014). A travel time reliability model of urban expressways with varying levels of service. Transportation Research Part C Emerging Technologies, 48, 453-467.
[34] 王云鹏,陈鹏,鲁光泉,于滨,李大庆.城市交通系统运行可靠性分析方法（专著）.人民交通出版社.2017
[35] 丁川,鲁光泉,王云鹏.交通运输系统的可靠性与安全性（译著）.机械工业出版社.2018
[36] 鲁光泉, 熊莹, 王云鹏, 鹿应荣, 陈鹏, & 丁川. (2016)一种基于贪心算法的城市道路网络最优修复时序方案. ZL201510777119.4.
[37] 鲁光泉, 熊莹, 王云鹏, 鹿应荣, 马晓磊, & 陈鹏等. (2017). 一种基于渗流理论的轨道网络拥挤瓶颈识别方法. ZL201710416450.2.
[38] 鲁光泉, 王云鹏, 王馨, 田大新, 余贵珍, & 于海洋. (2018). 一种宏观—微观结合的环路交通可靠性仿真方法与系统. ZL201410827008.5.
[39] 鲁光泉, 王云鹏, 余贵珍, & 田大新. (2010). 基于标线参数的交通事故现场二维测量及俯视校正方法. ZL201010132282.2.
五、科技发展战略
[40] 傅志寰、孙永福等.交通强国战略研究（第二卷）（参编）.人民交通出版社.2019
[41] 中国工程科技2035发展战略研究项目组、工程科技战略咨询研究智能支持系统项目组.中国工程科技2035发展战略研究——技术路线图卷（一）（参编）.中国工信出版集团、电子工业出版社.2020
[42] “中国工程科技2035发展战略研究”项目组.中国工程科技2035发展战略：机械与运载领域报告（参编）.科学出版社.2019
[43] “中国工程科技2035发展战略研究"项目组.中国工程科技2035发展战略：公共安全领域报告（参编）.科学出版社.2020
[44] “国家综合立体交通网规划纲要学习读本”编写组. 国家综合立体交通网规划纲要学习读本（参编）.人民交通出版社. 2021
近期论文
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[1].A unified driving behavior model based on psychological safety space:Transportation Research Part F: Psychology and Behaviour,2025
[2].How do drivers perceive collision risk? A quantitative exploration in generalized two-dimensional scenarios:Accident Analysis & Prevention,2024
[3].Driving Behavior Model for Multi-Vehicle Interaction at Uncontrolled Intersections Based on Risk Field Considering Drivers Visual Field Characteristics:IEEE Transactions on Intelligent Transportation Systems,2024
[4].A unified risk field-based driving behavior model for car-following and lane-changing behaviors simulation:Simulation Modelling Practice and Theory,2024
[5].A risk-field based motion planning method for multi-vehicle conflict scenario:IEEE Transactions on Vehicular Technology,2023
[6].Modeling Vehicle Paths at Intersections: A Unified Approach Based on Entrance and Exit Lanes:IEEE Transactions on Intelligent Transportation Systems,2023
[7].A Method of Identifying Personalized Car-Following Characteristics for Adaptive Cruise Control System:IEEE Transactions on Intelligent Transportation Systems,2023
[8].Effects of assignments of dedicated automated vehicle lanes and inter-vehicle distances of automated vehicle platoons on car-following performance of nearby manual vehicle drivers:Accident Analysis and Prevention,2022,177:106826
[9].Modeling and simulation of approaching behaviors to signalized intersections based on risk quantification:Transportation Research Part C,2022,142:103773
[10].Risk Field Model of Driving and Its Application in Modeling Car-Following Behavior:IEEE Transactions on Intelligent Transportation Systems,2022,23(8):11605-11620
[11].Modeling takeover behavior in level 3 automated driving via a structural equation model: Considering the mediating role of trust:Accident Analysis and Prevention,2021,157
[12].Measuring drivers’ takeover performance in varying levels of automation: Considering the influence of cognitive secondary task:Transportation Research Part F: Psychology and Behaviour,2021,82:96–110
[13].Are novice drivers competent to take over control from level 3 automated vehicles? A comparative study with experienced drivers:Transportation Research Part F,2021(81):65–81
[14].道路交通安全（教材）.北京:人民交通出版社,2018
[15].车车协同安全控制技术（专著）:科学出版社,2014
[16].车用安全通信-协议、安全及隐私（译著）.[专著]:北京理工大学出版社,2015
[17].城市交通系统运行可靠性分析方法（专著）.[专著].北京:人民交通出版社,2017
[18].智能交通技术概论（教材）.[专著]:清华大学出版社,2020
[19].交通运输系统的可靠性与安全性（译著）.[专著]:机械工业出版社,2018
[20].交通强国战略研究（第二卷）（参编）:人民交通出版社,2019
[21].中国工程科技2035发展战略研究——技术路线图卷（一）（参编）:中国工信出版集团、电子工业出版社,2020
[22].中国工程科技2035发展战略：机械与运载领域报告（参编）:科学出版社,2019
[23].中国工程科技2035发展战略：公共安全领域报告（参编）:科学出版社,2020
[24].Quantitative indicator of homeostatic risk perception in car following.:Safety science,2012,50(9):1898-1905
[25].Analysis of yellow-light running at signalized intersections using high-resolution traffic data.:Transportation research part A: policy and practic,2015,73:39-52
[26].Cooperative autonomous traffic organization method for connected automated vehicles in multi-intersection road networks:Transportation Research Part C-Emerging Technologies,2020,111:458-476
[27].Impact of heterogeneity of car-following behavior on rear-end crash risk.[期刊]:Accident Analysis and Prevention,2019,125:275-289
[28].Tunable and Transferable RBF Model for Short-Term Traffic Forecasting:IEEE Transactions on Intelligent Transportation Systems
[29].自动驾驶中视觉次任务对年轻驾驶人接管时间的影响.中国：西安:中国公路学报,2018,31(4):165-171
[30].Influence of Driving Behaviors on the Stability in Car Following:IEEE Transactions on Intelligent Transportation Systems,2019,20(3):1081-1098
[31].A New Control Strategy Integrated into the Desired Safety Margin Car-Following Model Considering the Disturbance Level:Transportation Research Record,2018
[32].Extended Desired Safety Margin Car-Following Model That Considers Variation of Historical Perceived Risk and Acceptable Risk:Transportation Research Record,2018,2018(0361198118773884)
[33].A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting.:Transportation Research Part C: Emerging Technolog,2016,62:21-34
[34].Logit-based analysis of drivers’ crossing behavior at unsignalized intersections in china.:Human Factors,2015,57(7):1101-1114
[35].Percolation transition in dynamical traffic network with evolving critical bottlenecks.:Proceedings of the National Academy of Sciences,2015,112(3):669-672
[36].Logit-Based Merging Behavior Model for Uncontrolled Intersections in China.:Journal of Transportation Engineering,2015,140(12):04014059
[37].Analysis of road traffic network cascade failures with coupled map lattice method.:Mathematical Problems in Engineering,2015,2015:101059
[38].A travel time reliability model of urban expressways with varying levels of service.:Transportation Research Part C-Emerging Technologies,48:453-46
[39].Vehicle trajectory extraction by simple two-dimensional model matching at low camera angles in intersection.:IET Intelligent Transport Systems,2014,8(7):631-638
[40].Preempt or yield? An analysis of driver’s dynamic decision making at unsignalized intersections by classification tree.:Safety Science,2014,65:36-44
[41].Analyzing drivers’crossing decisions at unsignalized intersections in China.:Transportation research part F,2014,24:244-255
[42].A car-following model based on quantified homeostatic risk perception.:Mathematical Problems in Engineering,,2013,2013:408756
[43].Relationship between road traffic accidents and conflicts recorded by drive recorders.:Traffic injury prevention,2011,12(4):320-326
[44].An Optimal Schedule for Urban Road Network Repair Based on the Greedy Algorithm.:PloS one,2016,11(10):e0164780
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