汽车自适应巡航控制及相应宏观交通流模型研究
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摘要
汽车自适应巡航控制(ACC:Adaptive Cruise Control)系统作为一种先进的驾驶辅助系统,旨在减轻驾驶员的精神负担,减少因驾驶员失误而造成的交通事故、提高汽车乘坐舒适性和燃油经济性、并改善交通流等,因而近年来已得到了政府、企业以及高校研究机构的广泛关注。
在汽车ACC系统的控制单元中,安全车间距策略以及系统的控制算法是实现ACC系统的功能及其实用化的关键所在,其设计的好坏直接决定了ACC系统的动态响应。而对由ACC汽车构成的交通流建立对应的宏观模型,不仅有利于分析ACC车流的动态演化规律、揭示ACC系统对交通流的改善作用,而且为交通管理者进行决策和控制提供理论依据。本文以汽车ACC系统为研究对象,分别对间距策略、针对车间相互纵向动力学特性的上层控制、结合车间纵向动力学和油门刹车切换特性的一体化控制、ACC车流的宏观交通流模型进行了研究。全文主要研究成果总结如下：
(1)提出了一种考虑前车速度趋势的可变跟车时距策略,并对其间距误差的收敛稳定性进行了理论证明。该间距策略通过引入对前车未来速度扰动的考虑提高了间距控制的前瞻性以及抗干扰能力,并通过饱和函数的处理,使车头时距更为合理的同时改善交通流的通行能力。与现有车间距策略相比,该间距策略能够适应复杂多变的行驶环境,有效地平衡了行驶过程中的安全性和跟车性,改善了间距控制的动态性能。
(2)在模型预测控制(MPC:Model Predictive Control)的框架下提出了一个兼顾安全性、跟车性、舒适性和燃油经济性的多目标ACC上层控制策略。首先建立了表征车间相互纵向动力学特性的高阶ACC上层控制模型；接着在该模型的基础上深入分析了车辆行驶的多个控制目的,并将其转化为相应的性能指标和系统约束,在MPC的框架下设计了一个多目标ACC上层控制策略。相比于现有的上层控制策略,该策略不仅保证了ACC车辆在行驶过程中的安全性和跟车性,而且显著改善了乘坐舒适性和燃油经济性。
(3)在MPC的框架下提出了一种考虑驾驶员行驶特性的双模式多目标ACC上层控制策略。该策略以对NGSIM实测微观驾驶数据的分析为基础,由平稳跟车模式、快速接近模式、以及模糊切换策略组成。当两车间距处于期望值附近时,该双模式ACC策略采用平稳跟车模式,利用上述第二点提出的算法,安全、平稳地跟踪前车；一旦车间距远大于期望值时,该策略切换到快速接近模式,设计了一个时间最优的多目标MPC算法,将其转化为一个混合整数非线性规划(MINLP:Mixed Integer Nonlinear Programming)命题,并提出了一种基于粒子群(PSO:Particle Swarm Optimization)算法的双层嵌套算法对其进行有效求解。为了模拟了驾驶员的决策过程,基于模糊推理建立了不同模式间的切换逻辑。仿真结果表明：该双模式ACC上层控制策略在满足多个行驶目的的同时,有效地模拟了驾驶员的行驶特性,根据行驶环境选择不同的行车策略,避免了换道乱插队现象的发生,因而有利于提高ACC系统的使用率。
(4)提出了一种结合车间相互纵向动力学特性和油门刹车机械特性的ACC一体化控制结构,并在该控制结构下利用MPC的框架设计了油门刹车优化切换的多目标控制策略。首先通过引入逻辑变量并加入逻辑不等式约束将油门、刹车的机械特性统一在一个模型框架内,继而结合车间纵向动力学特性得到了ACC的综合模型；接着在该模型基础上设计了油门刹车优化切换的一体化多目标控制算法,在满足道路行驶过程中的安全性、跟车性、舒适性、节油性等多个目标的同时,对车辆自身的油门刹车切换性能(切换序列、对应执行器输入量)进行优化,从而在MPC的框架下转化为一个MINLP命题,并采用上述第三点提出的基于PSO的双层嵌套算法对其进行有效求解。与经典的阈值切换策略相比,该策略在保证行驶过程中的多个目的的同时,有效减少了油门、刹车间的切换次数,避免了因不同执行机构频繁切换而带来的机械磨损,改善了执行器的动态响应。
(5)建立了ACC汽车的宏观交通流模型。该模型结合了ACC车辆的控制模型以及交通流的基本特性,真实地反映了宏观ACC车流的稳态特性和动态演化规律,有效地揭示了交通流动力波的传动特性。通过分析该宏观模型的速-密特性和流-密特性,证明了ACC系统的使用能显著提高道路的通行能力和服务水平,从而有效改善交通流。
As an advanced Driver Assistant System (DAS), Adaptive Cruise Control (ACC) system is designed to release human drivers'mental load, reduce the car accidents, increase the driving comfort, decrease the fuel consumption and improve the traffic flow. In recent years, ACC system has received considerable attention from government, automobile companies, and research institutions.
In the control module of ACC system, spacing policy and the control algorithm are the key factors to achieve system function and its real application, so they directly determine the dynamical responses of ACC systems. Establishing the macroscopic traffic flow model of ACC-equipped vehicles is not only beneficial to analyze the macroscopic dynamical characteristics of ACC flow, reflect the improvement of ACC system to the traffic flow, but also provides theoretical support for traffic management and control. This dissertation takes ACC system as the research object, investigate the spacing policy, the upper level control algorithm which aims at the longitudinal dynamics of inter-vehicle, the integrated control design including the longitudinal dynamics of inter-vehicle and the switch performance of throttle and brake, and the macroscopic traffic flow model of ACC flow, respectively. The main contributions of this dissertation are presented as follows:
(1) A variable time headway policy which considers the velocity tendency of preceding vehicle is proposed, and the corresponding stability of spacing error is proved. By introducing the acceleration disturbance of preceding vehicle, the ability regarding forward-looking and anti-disturbance of spacing control is improved. Moreover, the introduction of saturation function makes time headway more reasonable and meanwhile improves the capacity of traffic flow. Compared to the traditional spacing policies, the proposed one can adapt the complex traffic scenarios, effectively balance the safety and car-following, and improve the dynamical performance of spacing control.
(2) A multi-objectives upper level controller of ACC is proposed in MPC (Model Predictive Control) framework, which can satisfy safety, car-following, driving comfort and fuel efficiency. First, a high order state model which describes the longitudinal dynamics of inter-vehicle is established. Based on this model, the control objectives of ACC system are analyzed and transformed to the performance index and system constraints, respectively. Then the control algorithm is designed in MPC framework to satisfy these multi-objectives and constraints. It is shown that the proposed ACC upper-level control algorithm not only meets the safety and car-following requirements, but also outperforms the traditional algorithms by improving driving comfort and reducing fuel consumption.
(3) A two-mode upper level controller of ACC in MPC framework is designed based on humans'driving habits. By analyzing the real microscopic traffic data from NGSIM, the control algorithm is designed to consist of steady following mode, fast approaching mode and fuzzy switch logic. Steady following mode is activated when the inter-distance falls round the desired value. The control algorithm proposed in (2) is utilized for this mode, with the purpose of following the preceding vehicle safely and steadily. Once the inter-distance becomes larger than the desired value, ACC vehicle switches to the fast approaching mode, which employs a time-optimal MPC algorithm. Then the controller design is transformed to be an online Mixed Integer NonLinear Programming (MINLP), which is solved by a nested two-loop algorithm based on Particle Swarm Optimization (PSO). In order to imitate human drivers' decision-making, the switching strategy between the two control modes is developed based on fuzzy inference It is shown that this two-mode upper level ACC algorithm can satisfy the multi-objectives of vehicle traveling, choose different control modes according to the current traffic condition, and therefore effectively reflect the humans'driving habits and increase the usage of ACC system.
(4) An integrated control structure is proposed, which synthesizes the longitudinal dynamics of inter-vehicie with the mechanical characteristics of throttle and brake. Moreover, an integrated ACC algorithm with optimal switching between throttle and brake is designed in this control structure. First, the binary integer variables and logic constraints arc introduced to synthesize the dynamics of throttle and brake into one model framework, and then it is combined with the longitudinal dynamics of inter-vehicle, which leads to an integrated ACC model. Based on this integrated ACC model, the control algorithm is designed to satisfy not only safety, car-following, driving comfort and fuel efficiency, but also the optimal switching performance (the switching sequence and the control input of actuator) between throttle and brake. Therefore, the controller design is transformed to be an online MINLP, and it can be solved by the nested two-loop algorithm based on PSO, which is proposed in (3) mentioned above. Compared to the tradition threshold switching algorithm, this control algorithm can not only satisfy the multi-objectives of vehicle traveling, but also effectively reduce the switching between throttle and brake, avoid the mechanism abrasion caused by frequent switching between different actuators, and improve the dynamics of control input of actuators.
(5) A macroscopic traffic flow model of ACC-equiiped vehicles is established. By synthesizing the ACC control algorithm and the principles of traffic flow, this macroscopic ACC model truly reflect the stability features and dynamical evolution laws of ACC flow, and effectively reveal the transmission characteristics of traffic wave. Based on this macroscopic ACC model, the characteristics of speed-density and flow-density are analyzed. It is proved that the usage of ACC system is beneficial to increase road capacity and service ability, therefore effectively improve traffic flow.
在汽车ACC系统的控制单元中,安全车间距策略以及系统的控制算法是实现ACC系统的功能及其实用化的关键所在,其设计的好坏直接决定了ACC系统的动态响应。而对由ACC汽车构成的交通流建立对应的宏观模型,不仅有利于分析ACC车流的动态演化规律、揭示ACC系统对交通流的改善作用,而且为交通管理者进行决策和控制提供理论依据。本文以汽车ACC系统为研究对象,分别对间距策略、针对车间相互纵向动力学特性的上层控制、结合车间纵向动力学和油门刹车切换特性的一体化控制、ACC车流的宏观交通流模型进行了研究。全文主要研究成果总结如下：
(1)提出了一种考虑前车速度趋势的可变跟车时距策略,并对其间距误差的收敛稳定性进行了理论证明。该间距策略通过引入对前车未来速度扰动的考虑提高了间距控制的前瞻性以及抗干扰能力,并通过饱和函数的处理,使车头时距更为合理的同时改善交通流的通行能力。与现有车间距策略相比,该间距策略能够适应复杂多变的行驶环境,有效地平衡了行驶过程中的安全性和跟车性,改善了间距控制的动态性能。
(2)在模型预测控制(MPC:Model Predictive Control)的框架下提出了一个兼顾安全性、跟车性、舒适性和燃油经济性的多目标ACC上层控制策略。首先建立了表征车间相互纵向动力学特性的高阶ACC上层控制模型；接着在该模型的基础上深入分析了车辆行驶的多个控制目的,并将其转化为相应的性能指标和系统约束,在MPC的框架下设计了一个多目标ACC上层控制策略。相比于现有的上层控制策略,该策略不仅保证了ACC车辆在行驶过程中的安全性和跟车性,而且显著改善了乘坐舒适性和燃油经济性。
(3)在MPC的框架下提出了一种考虑驾驶员行驶特性的双模式多目标ACC上层控制策略。该策略以对NGSIM实测微观驾驶数据的分析为基础,由平稳跟车模式、快速接近模式、以及模糊切换策略组成。当两车间距处于期望值附近时,该双模式ACC策略采用平稳跟车模式,利用上述第二点提出的算法,安全、平稳地跟踪前车；一旦车间距远大于期望值时,该策略切换到快速接近模式,设计了一个时间最优的多目标MPC算法,将其转化为一个混合整数非线性规划(MINLP:Mixed Integer Nonlinear Programming)命题,并提出了一种基于粒子群(PSO:Particle Swarm Optimization)算法的双层嵌套算法对其进行有效求解。为了模拟了驾驶员的决策过程,基于模糊推理建立了不同模式间的切换逻辑。仿真结果表明：该双模式ACC上层控制策略在满足多个行驶目的的同时,有效地模拟了驾驶员的行驶特性,根据行驶环境选择不同的行车策略,避免了换道乱插队现象的发生,因而有利于提高ACC系统的使用率。
(4)提出了一种结合车间相互纵向动力学特性和油门刹车机械特性的ACC一体化控制结构,并在该控制结构下利用MPC的框架设计了油门刹车优化切换的多目标控制策略。首先通过引入逻辑变量并加入逻辑不等式约束将油门、刹车的机械特性统一在一个模型框架内,继而结合车间纵向动力学特性得到了ACC的综合模型；接着在该模型基础上设计了油门刹车优化切换的一体化多目标控制算法,在满足道路行驶过程中的安全性、跟车性、舒适性、节油性等多个目标的同时,对车辆自身的油门刹车切换性能(切换序列、对应执行器输入量)进行优化,从而在MPC的框架下转化为一个MINLP命题,并采用上述第三点提出的基于PSO的双层嵌套算法对其进行有效求解。与经典的阈值切换策略相比,该策略在保证行驶过程中的多个目的的同时,有效减少了油门、刹车间的切换次数,避免了因不同执行机构频繁切换而带来的机械磨损,改善了执行器的动态响应。
(5)建立了ACC汽车的宏观交通流模型。该模型结合了ACC车辆的控制模型以及交通流的基本特性,真实地反映了宏观ACC车流的稳态特性和动态演化规律,有效地揭示了交通流动力波的传动特性。通过分析该宏观模型的速-密特性和流-密特性,证明了ACC系统的使用能显著提高道路的通行能力和服务水平,从而有效改善交通流。
As an advanced Driver Assistant System (DAS), Adaptive Cruise Control (ACC) system is designed to release human drivers'mental load, reduce the car accidents, increase the driving comfort, decrease the fuel consumption and improve the traffic flow. In recent years, ACC system has received considerable attention from government, automobile companies, and research institutions.
In the control module of ACC system, spacing policy and the control algorithm are the key factors to achieve system function and its real application, so they directly determine the dynamical responses of ACC systems. Establishing the macroscopic traffic flow model of ACC-equipped vehicles is not only beneficial to analyze the macroscopic dynamical characteristics of ACC flow, reflect the improvement of ACC system to the traffic flow, but also provides theoretical support for traffic management and control. This dissertation takes ACC system as the research object, investigate the spacing policy, the upper level control algorithm which aims at the longitudinal dynamics of inter-vehicle, the integrated control design including the longitudinal dynamics of inter-vehicle and the switch performance of throttle and brake, and the macroscopic traffic flow model of ACC flow, respectively. The main contributions of this dissertation are presented as follows:
(1) A variable time headway policy which considers the velocity tendency of preceding vehicle is proposed, and the corresponding stability of spacing error is proved. By introducing the acceleration disturbance of preceding vehicle, the ability regarding forward-looking and anti-disturbance of spacing control is improved. Moreover, the introduction of saturation function makes time headway more reasonable and meanwhile improves the capacity of traffic flow. Compared to the traditional spacing policies, the proposed one can adapt the complex traffic scenarios, effectively balance the safety and car-following, and improve the dynamical performance of spacing control.
(2) A multi-objectives upper level controller of ACC is proposed in MPC (Model Predictive Control) framework, which can satisfy safety, car-following, driving comfort and fuel efficiency. First, a high order state model which describes the longitudinal dynamics of inter-vehicle is established. Based on this model, the control objectives of ACC system are analyzed and transformed to the performance index and system constraints, respectively. Then the control algorithm is designed in MPC framework to satisfy these multi-objectives and constraints. It is shown that the proposed ACC upper-level control algorithm not only meets the safety and car-following requirements, but also outperforms the traditional algorithms by improving driving comfort and reducing fuel consumption.
(3) A two-mode upper level controller of ACC in MPC framework is designed based on humans'driving habits. By analyzing the real microscopic traffic data from NGSIM, the control algorithm is designed to consist of steady following mode, fast approaching mode and fuzzy switch logic. Steady following mode is activated when the inter-distance falls round the desired value. The control algorithm proposed in (2) is utilized for this mode, with the purpose of following the preceding vehicle safely and steadily. Once the inter-distance becomes larger than the desired value, ACC vehicle switches to the fast approaching mode, which employs a time-optimal MPC algorithm. Then the controller design is transformed to be an online Mixed Integer NonLinear Programming (MINLP), which is solved by a nested two-loop algorithm based on Particle Swarm Optimization (PSO). In order to imitate human drivers' decision-making, the switching strategy between the two control modes is developed based on fuzzy inference It is shown that this two-mode upper level ACC algorithm can satisfy the multi-objectives of vehicle traveling, choose different control modes according to the current traffic condition, and therefore effectively reflect the humans'driving habits and increase the usage of ACC system.
(4) An integrated control structure is proposed, which synthesizes the longitudinal dynamics of inter-vehicie with the mechanical characteristics of throttle and brake. Moreover, an integrated ACC algorithm with optimal switching between throttle and brake is designed in this control structure. First, the binary integer variables and logic constraints arc introduced to synthesize the dynamics of throttle and brake into one model framework, and then it is combined with the longitudinal dynamics of inter-vehicle, which leads to an integrated ACC model. Based on this integrated ACC model, the control algorithm is designed to satisfy not only safety, car-following, driving comfort and fuel efficiency, but also the optimal switching performance (the switching sequence and the control input of actuator) between throttle and brake. Therefore, the controller design is transformed to be an online MINLP, and it can be solved by the nested two-loop algorithm based on PSO, which is proposed in (3) mentioned above. Compared to the tradition threshold switching algorithm, this control algorithm can not only satisfy the multi-objectives of vehicle traveling, but also effectively reduce the switching between throttle and brake, avoid the mechanism abrasion caused by frequent switching between different actuators, and improve the dynamics of control input of actuators.
(5) A macroscopic traffic flow model of ACC-equiiped vehicles is established. By synthesizing the ACC control algorithm and the principles of traffic flow, this macroscopic ACC model truly reflect the stability features and dynamical evolution laws of ACC flow, and effectively reveal the transmission characteristics of traffic wave. Based on this macroscopic ACC model, the characteristics of speed-density and flow-density are analyzed. It is proved that the usage of ACC system is beneficial to increase road capacity and service ability, therefore effectively improve traffic flow.
引文
[1]我国逾半数交通事故死者年龄在15岁至44岁之间.新华网,http://news.qq.co m/a/20100115/002363.htm,2010-1-25.
[2]杨兆升.城市道路交通系统智能系统理论与实施方法,北京,中国铁道出版社,2002.
[3]Kubozuka T. Perspective of ITS Technology:A scenario. Proceeding of International Symposium on Advanced Vehicle Control,2002.
[4]《中国智能运输系统体系框架》专题组：中国智能运输系统体系框架.人民交通出版社,2003.
[5]陈旭梅,于雷,郭继孚,全永淼.美国智能交通系统ITS的近期发展综述.中外公路,2003,23(2)：9-12.
[6]侯德藻.汽车纵向主动避撞系统的研究.清华大学博士论文,2004.
[7]Lingyun X., Feng G. A comprehensive review of develpment of adaptvie cruise control systems. Vehicle System Dynamics,2010,48(10):1167-1192.
[8]Rajesh R. Dynamics and Control. Springer,2006.
[9]Vahidi A., Eskandarian A. Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Transactions on Intelligent Transportation System, 2003,4(3):143-153.
[10]Rajamani. R, Zhu C. Semi-Autonomous Adaptive Cruise Control Systems. IEEE Transactions on Vehicular Technology,2002,51(5):1186-1192.
[11]Greg M., Mark M., Brackstone M. Towards an understanding of adaptive cruise control. Transportation Reserch Part C,2001(9):33-51.
[12]田雷.汽车自适应巡航系统的模糊自校正控制算法研究.吉林大学硕士论文,2003.
[13]刘中海.自适应巡航控制系统的设计与仿真.清华大学硕士论文,2005.
[14]Shaout A., Jarrahz A. Cruise control techonology review. Computer Electrical Engineer,1997,23:259-271.
[15]Diamond H., Lawrence W. The development of an automatically controlled highway system. Transportation Research Institute's Report UMR 1266, University of Michigan,1966.
[16]Levine S., Athans M. On the optimal error regualteion of a string of moving vehicles. IEEE Transactions on Automatic Control,1996,11:355-361.
[17]Bender J., Fenton R. On the flow capacity of automated highways. Transportation Science,1970,4:52-63.
[18]Bender J., Fenton R., Olson K. An experimental study of vehicle automatic longitudinal control. IEEE Transactions on Vehicular Technology,1971,20:114-123.
[19]Fenton R., Mayhan J. Automated highway studies at the Ohio state University-An overview. IEEE Transactions on Vehicular Technology,1991,40:100-13.
[20]Caudill R., Garrard W. Vehicle follower longitudinal conrol for automated transit vehicles. Journal of Dynamic System, Measurement, and Control,1977:241-248.
[21]Garrard W. Longitudinal control for automated guideway transit vehicles. Report UMTA-MN-11-77-2, University of Minnesota,1977.
[22]Hesse R. Cabinentaxi- A personal public transportation system. Transportation, 1972,1:321-330.
[23]Maury J. The ARMIS PRT system. SAE International, SAE paper 740143,1974.
[24]Catling I. Advanced Technoloby for road transport:IVHS and ATT, Boston: Artech House,1993.
[25]Broqua F., Lerner G., Mauro V, Morello E. Cooperative driving:Basic concepts and a first assessment of 'Intelligent cruise control'strategies. Proceedings of the DRIVE Conference. Brussels,1991.
[26]Zhang X., Benz T. Simulation and evaluation of intelligent cruise control. IVHS Journal,1993,1(2):181-190.
[27]Joachim S., Walter S. Adaptive cruise control for coaches. International Symposim on Advanced vehicle control. Aachen University of Technology,1996,3: 1351-1366.
[28]Bishop R. Intelligent vehicle technology and trends, Boston:Artech House, 2005.
[29]Ioannou P., Ahmed-Zaid F., Wuh D. A time headway autonomous intelligent cruise controller:design and simulation,. California PATH Working paper, UCB-ITS-PWP-94-07,1994.
[30]Ioannou P. Development and experimental evaluation of autonomous vehicles for roadway/vehicle cooperative driving. Calfornia PATH Research Paper, UCB-ITS-PRR-98-9,1998.
[31]Darbha S., Rajagopal K. Intelligent cruise control systems and traffic flow stability. California PATH Research Paper, UCB-ITS-PRR-98-36,1998.
[32]Bishop R. Intelligeng Vehicle Technoloby and Trends, Boston:Artech House,2 005.
[33]Fancher P., Ervin R., Sayer J. Intelligent cruise contro field operation test (final report). Transportation Research Institute, University of Michigan,1998.
[34]Tsugawa S., Aoki M., Hsaka A., Seki K. A survey of present IVHS activities in Japan. Control Engineer Practice,1997,5(11):1607-1614.
[35]Ioannou P. Automated Highway Systems, New York:Plenum Press,1997.
[36]朱永强.汽车自适应巡航系统的控制策略开发及行驶环境评估.吉林大学硕士论文,2007.
[37]Yanakiev D., Kanellakopoulos I. Nonlinear Spacing Policies for Automated Heavy-Duty Vehicles. IEEE Transactions on Vehicular Technology,1998,47(4): 1365-1377.
[38]Zhou J., Peng H. Range policy of adaptive cruise control vehicles for improved flow stability and string stability. IEEE Transactions on Intelligent Transportation System,2005,6(2):229-237.
[39]Swaroop D., Hedrick J.K., Chien C.C., Ioannou. P. A comparison of spacing and headway control laws for automatically controlled vehicles. Vehicle System Dynamics,1994,23:597-625.
[40]Lingyun X., Feng G., Jiangfeng W. On scalability of platoon of automated vehicles for leader-predecessor information framework. IEEE Intellitent vehicles Symposium. China,2009.
[41]Darbha S., Hedrick J. Constant Spacing Strategies for Platooning in Automated Highway Systems. Journal of Dynamics Systems, Measurement and Control,1999, 121:462-470.
[42]Santhanakrishnan K., Rajamani R. On spacing policies for highway vehicle automation. IEEE Transactions on Transportation systems,2003,4(4):198-204.
[43]Ioannou P., Chien C. C. Autonomous intelligent cruise control. IEEE Transactions on Vehicular Technology,1993,42:657-672.
[44]Tsang-Wei L., Sheue-Ling H., Paul A.G. Effects of time-gap settings of adaptive cruise control (ACC) on driving performance and subjective acceptance in a bus driving simulator. Safety Sciences,2009,47:620-625.
[45]Yi-Hsien C., Jyh-Ching J. Longitudinal vehicle control with the spacing policy in consideration of brake input limits. IEEE International Conference on Systems, Man and Cybernetic,2007:1705-1710.
[46]Swaroop D., Rajagopal K.R. Intelligent cruise control system and traffic flow stability. Transportation Research Part C:Emerging Technologies,1999,7(6): 329-352.
[47]Li P., Shrivastava A. Traffic flow stability induced by constant time headway policy for adaptive cruise control vehicles Transportation Research Part C:Emerging Technologies,2002,10(4):275-301.
[48]Jin Z., Masahiro O., Abdelkader E.K., A safety spacing policy and its impact on highway traffic flow. IEEE Symposium on Intellgient vehicles,2009:960-965.
[49]Jianlong Z., loannou P. Adaptive vehicle following control system with variable time headways.44th IEEE Conference on Decision and Control,2005:3880-3885.
[50]Broqua F., Lerner G., Mauro V., Morello E. Cooperative driving:basic concepts and a first assessment of the intelligent cruise control strategies. Proceedings of Drive Conference,1991:908-929.
[51]Wang J., Rajamani R. Adaptive cruise control system design and its impact on traffic flow. Proceedings of the American Control Conference. Alaska,2002.
[52]Greenshields B.D. A study of traffic capacity. Proceedings of HRB,1934,14: 448-477.
[53]橘本佳幸,里中久志,重松崇.衝突回避スシのム開.自动车技述会学述演会前刷集,1994：57-60.
[54]Xu Q., Hedrick K., Sengupta R., VanderWerf J. Effects of vehicle-vehicle/ roadside-vehicle communication on adaptive cruise controlled highway systems. Proceedings of IEEE Vehicular Technology. Canada,2002:1249-53.
[55]Fancher P., Bareket Z., Peng H., Ervin R. Research on desirable adaptive cruise control behavior in traffic streams. University of Michigan Transportation Research Institute Report (UMTRI-2002-16),2002.
[56]宾洋.汽车行驶纵向距离自适应控制系统的研究.重庆大学硕士论文,2002.
[57]Rajamani R., Zhu C. Semi-autonomous adaptive cruise control systems. IEEE Transactions on Vehicular Technology,2002,51 (5):1186-1192.
[58]Bageshwar V.L., Garrard W.L., Rajamani R. Model Predictive control of transitional maneuvers for adaptive cruise control vehicles. IEEE Transactions on Vehicular Techonology,2004,53(5):1573-1585.
[59]Rajamani R. Vehicle dynamics and control, Springer,2006.
[60]Jianlong Z., Ioannou P.A. Longitudinal control of heavy trucks in mixed traffic: environmental and fuel economy considerations. IEEE Transactions on Intelligent Transportation System,2006,7(1):92-104.
[61]Paul V, Karl N., Bartono A. Stop and go cruise control. Proceedings of Seoul FISITA world automotive congress,2000:1-8.
[62]Abdullah R., Hussain A., Warwick K., and Zaycd A. Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-baxed switching and tuning. Neurocomputing,2008,71(13):2727-2741.
[63]Yamamura Y., Tabe M., M. Kanehira, et al. Developement of an adaptive cruise control system with stop and go capability. SAE Technical paper 2001-01-0798 2001.
[64]Canale M., Malan S. Tuning of stop and go driving control strategies using driver behavior analysis. IEEE Intelligent Vehicle Symposium,2002:407-412.
[65]Chiying L., Huei P. Optimal adaptive cruise control with guaranteed string stability. Vehicle system dynamics,1999,31:313-330.
[66]No T.S, Chong K.T., Roh D.H. A Lyapunov function approach to longitudinal control of vehicles in a platoon. IEEE Transactions on Vehicular Techonology,2001, 50(1):116-124.
[67]Yi S.Y., Chong K.T. Impedance control for a vehicle platoon system. Mechatronics,2005,15:627-638.
[68]Martinez J.J., Canudas-de-wit C. A safe longitudinal control for adaptive cruise control and stop and go scenarios. IEEE Transactions on Control Systems Technology, 2007,15(2):789-814.
[69]Marhefka D.W., Orin D.E. Simulation of contact using a nonlinear damping model. IEEE International Conference on Robotics and Automation. Minneapolis, 1996:1662-1668.
[70]刘中海.自适应巡航控制系统设计与仿真.清华大学计算硕士论文,2005.
[71]Rui J and Qing-song W. The adaptive cruise control vehicles in the cellular automata model. Physics Letter A,2006,359:99-102.
[72]管欣,王景武,高振海.基于最优预瞄加速度决策的汽车自适应巡航控制系统.吉林大学学报(工学版),2004,34：189-193.
[73]吴利军,刘昭度,何玮.汽车ACC跟随控制策略的研究.汽车工程,2005,27：514-521.
[74]李果,张良起,张彭.无人陆地车辆的自学习PD迭代纵向控制器.控制理论与应用,1994,11：713-719.
[75]张德兆,王建强,刘佳熙,李克强,连小珉.加速度连续型自适应巡航控制模式切换策略.清华大学学报,2010,50(8)：1277-1281.
[76]申瑞玲.车距的模糊自适应控制及其应用研究.东南大学硕士论文,2005.
[77]朱晓宏.车辆自动驾驶纵向运动模糊神经控制研究.武汉理工大学硕士论文,2003.
[78]Gerdes J.C., Hedrick J.K. Vehicle speed and spacing control via coordinated throttle and brake actuation. Control Engineer Practice 1997,5:1607-14.
[79]Zhang Y., Kosmatopoulos E.B., and Ioannou P.A. Autonomous intelligent cruise control using front and back information for tight vehicle following manuevers. IEEE Transactions on Vehicular Techonology 1999,48:319-28.
[80]Hedrick J.K., Lu X.Y. Heavy-duty vehicle modeling and longitudinal control,. Vehicle system dynamics 2005,43:653-69.
[81]Kuragaki S., Kuroda H., Chi-Minowa T., and et al. An adaptive control using wheel torque management techinque.
[82]Yi K., Hong J., and Kwon Y.D. A vehicle control algorithm for stop and go cruise control. Proceedings of the Institution of Mechanical Engineers,2001:1099-1115.
[84]宾洋,车辆走停巡航系统的非线性控制研究.清华大学博士论文,2006.
[85]舒迪前.预测控制系统及其应用,机械工业出版社,1995.
[86]丁宝苍.预测控制的理论与方法,机械上业出版社,2008.
[87]徐祖华.模型预测控制理论及应用研究.浙江大学博士论文,2004.
[88]许超,陈治纲,邵惠鹤.预测控制技术及其应用发展综述.化工自动化及仪表,2002,29(3)：1-10.
[89]钱积新,赵均,徐祖华.预测控制,北京：化学工业出版社,2007.
[90]石宇静.复杂工业过程模型预测控制的研究.东北大学博士论文,2009.
[91]Boyd S., Vandenberghe L. Convex Optimization:New York:Cambridge University,2004.
[92]Sanchez A., Katebi MR. Predictive control of dissolved oxygen in an activated sludge waste water treatment plant. Proceedings of the European Conrol Conference ECC. Cambridge, UK,2003.
[93]谢生钢.基于活性污泥模型的污水处理过程预测控制力法研究.浙江大学硕士论文,2008.
[94]杜建福.基于预测控制的小型无人直升机的自主飞行研究.上海交通大学博士论文,2008.
[95]Yasunobu S., Miyamoto S., Takaoka T., and Ohsihima H. Application of predictive fuzzy control to automatic train operation controller. Proceedings of Industrial Electronics Control and Industrumentation Tokyo, Japan,1984:657-662.
[96]Oshima H., Yasunobu S., and Sekino S.I. Automatic train operation system based on predictive fuzzy control. Proceedings of the International Workshop on Artificial Intelligence for Industry Applications,1988:485-489.
[97]黄秀玲.广义预测控制算法在ATO系统中的应用与研究.西南交通大学硕士论文,2009.
[98]Leung C., Huang S.D., Kwok N., and Dissanayake G. Planning under uncertainty using model predictive control for information gathering. Robotics and autonomous systems,2006,54(11):898-910.
[99]Yoo S.J., Choi Y.H., Park J.B. Generalized model predictive control based on self-recurrent wavelet neural-network for stable path tracking of mobile robots: adaptive learning rates approach. IEEE Transactions on Circuits and Systems I,2006, 53(6):1381-1394.
[100]韩光信,陈虹,马苗苗,赵海艳.约束非完整移动机器人轨迹跟踪的非线性预测控制.吉林大学学报(工学版),2009,39(1)：177-181.
[101]Rajagopalan A., Washington G., Rizzoni G., and Guezennec Y. Development of fuzzy logic and neural network control and advanced emisssions modeling for parallel hybrid vehicles. Report:NREL/SR-540-32919,2003.
[102]Kim T.S., Manzie C., and Shama R. Model predictive control of velocity and torque split in a parallel hybrid vehicle. IEEE International Conference on Systems, Man and Cybernetics. San Antonio,2009.
[103]Beck R., Bollig A., AbeI D. Comparison of two real-time predictive strategies for the optimal energy management of a hybrid electric vehicle. Oil & Gas Science and Technology,2006,62(4):635-643.
[104]Kermani S., Delprat S., Guerra T.M, and Trigui R. Predictive control for HEV energy management:experimental results. Vehicle Power and Propulsion Conference. Dearborn,2009:364-369.
[105]潘文军.中度混合动力汽车再生制动模型预测控制策略.重庆大学硕士论文,2010.
[106]Corona D., Schutter B.D. Adaptive cruise control for a smart car:a comparison benchmark for MPC-PWA control methods. IEEE Transactions on Control systems technoloby,2008,16(2):365-372.
[107]Brackstone M., McDonald M. Car-following:a historical review. Transportation Research Part F:Traffic psychology and Behavior,1990,2(4): 181-196.
[108]Prigogine I., Herman R. Kinetic theory of vehicular traffic, New York: American Elseiver,1971.
[109]姜锐.交通流复杂动态特性的微观和宏观模式研究.中国科技大学博士论文，2005.
[110]Lighthill J.J., Whitham G.B. On kinematic waves Ⅱ:A theory of traffic flow on long croweded roads. Proc. Royal Society London,1955:317-345.
[111]Richards PI. Shock waves on the highway. Operations research,1956,4(1): 42-51.
[112]张晋.基于元胞自动机的混合交通流建模方法研究.浙江大学博士论文,2004.
[113]Payne H.J. Models of freeway traffic and control. Mathematical models of public systems, simulation Council,1971:51-61.
[114]Philips W.F. A new continuum traffic model obtained from kinetic theory. IEEE transactions on Automatic Control,1978,23:1032-1036.
[115]Kuhne R.D. Macroscopic freewaay model for dense traffic-stop start waves and incident detection. The 9th International Symposium on Transportation and Traffic theory,1984:21-41.
[116]Michalopoulos P.G, Yi P., Lyrintzis A.S. Continuum modeling of traffic dynamics for congested freeways. International journal of transportation research, 1993,27(4):315-352.
[117]Papageorgiou M., Blosseville J.M., Hadj S.H. Modleing and real time control on traffic flow on the southern part of Boulevard Peripherique in Paris(Part Ⅰ modeling, Part Ⅱ:coordinated on-ramp metering. Transportation Research Part A, 1990,24(5):345-370.
[118]Kotsialos A., Papageorgiou M., Mangeas M., and Haj-Salem H. Coordianted and integrated control of motorway networks via non-linear optimal control. Transportation Research Part C,2002,10:65-84.
[119]吴正.交通流的动力学模拟与测量方法.复旦学报(自然科学版),1991,30(1)：111-117.
[120]戴世强,冯苏苇,顾国庆.交通流动力学：它的内容、方法、意义.自然杂志,1997,19(4)：196-201.
[121]Swaroop D., Rajagopal K.R. A review of constant time headway policy for automatic vehicle following. IEEE Intelligent Transportation systems. Oakland,2001: 65-69.
[122]Ioannou P., Xu Z., Eckert S., Clemons D., and Sieja T. Intelligent Cruise Control:theory and experiment. Proceedings of the 32nd IEEE conference on Decision and Control,1993.
[123]James W.J., Neil, D.L., Steve M., Scott O., Brain C.T. Use of advanced in-vehicle techonology by young and older early adopters, Survey results on Adaptive Cruise Control systems. Report No. DOT HS 810917:National Highway Traffic Safety Administration,2008.
[124]汪孝宗,胡雪琴,王红茹,宋学莲.中国如何实现减排目标：十二五减排措施将出台.中国经济周刊,2009.
[125]Naus G., van den Bleek R., Ploeg J., Scheepers B., van de Molengraft R., and Steinbuch M. Explicit MPC design and performance evaluation of an ACC Stop&Go. American Control Conference,2008:224-229.
[126]Chien C.C., Ioannou P., Lai M.C., Entrainment and vehicle following controllers design for autonomous intelligent vehicles, American Control Conference, 1994,1:6-10.
[127]Srdjan S.S., Milorad J.S., Dragoslav D.S., Decentralized overlapping control of a platoon of vehicles. IEEE Transactions on control systems technology,2000,8(5): 816-832.
[128]Kyongsu Y., Jintai C., Nonlinear brake control for vehicle CW/CA systems. IEEE/ASME Transactions on Mechatronics,2006,6:17-25.
[129]Lee G.D., Kim S.W. A longitudinal control system for a platoonn of vehicles using a fuzzy-sliding mode algorithm. Mechatronics,2002,12:97-118.
[130]Barth M., Scora G., Younglove T. Modal emissions model for heavy-duty diesel vehicles.83rd Annual Meeting of the Transportation-Research-Board. Washington, DC,2004:10-20.
[131]Moon S., Yi K. Human driving data-based design of a vehicle adaptive cruise control algorithm. Vehicle System Dynamics,2008,46(8):661-690.
[132]Somda F.H., Cormerais H., Buisson J. Intelligent transportation systems:a safe, robust and comfortable strategy for longitudinal monitoring. IET Intelligent Transportation System,2009,3(3):188-197.
[133]Yi K., Han D. A vehicle stop-and-go control strategy based on Human Drivers Charasteristics. Journal of Mechanical Science and Technoloby,2005,19(4): 993-1000.
[134]Naranjo J.E., Gonzalez C., Garcia R., and Pedro T.D. Cooperative Throttel and Brake Fuzzy Control for ACC Stop&Go Maneuvers. IEEE Transactions on Vehicle Technoloby,2007,56(4):1623-1630.
[135]Chakroborty P., Kikuchi S. Evaluation of the general motors based car-following models and a proposed fuzzy inference model. Transportation Research Part C:Emerging Technologies,1999,7:209-235.
[136]Dong-fan X., Zi-you G., Xiao-mei Z. The effect of ACC vehicles to mixed traffic flow consisting of manual and ACC vehicles. Chinese Physics B,2008,17(12): 4440-4446.
[137]Adminstration F.H. NGSIM Overview. Washington, DC:Federal Highway Administration,2006.
[138]Lu X.Y., Varaiya P., Horowitz R., and Skabardonis A. Fundamental Diagram Modeling from NGSIM data. The 2nd International Symposium on Freeway and Tollway Operations. Hawaii,2009.
[139]Colyar J., Halkias J. US Highway 101 Dataset. Federal Highway Adminstration, 2007.
[140]武欣.微观交通模型的全微观参数校正及仿真平台的研究.浙江大学硕士论文,2008.
[141]Zadeh L.A. FUZZY SETS. Informational and Control,1965,8(3):338-353.
[142]Floudas C.A. Nonlinear and mixed-integer ingeter optimization:Oxford University,1995.
[143]张聚,李平,王万良.基于Branch&Bound方法MIQP问题的求解及应用.系统仿真学报,2003,15(4)：488-491.
[144]Geoffrion A.M. Generalized benders decomposition. Journal of Optimization Theory and Applications,1972,10(4):237-260.
[145]Duran A.M., Grossmann I.E. An outer-approximation algorithm for a class of mixed-integer nonlinear programs. Mathematical Programming,1986,36:307.
[146]Silver E.A. An overview of heuristic solution methods. Journal of Operation. Research Society,2004,55(9):936-956.
[147]Whitley D. A genetic algorithm tutorial. Statistics and Computing,1994,4(2): 65-85.
[148]Dorigo M., Caro G.D., Gambardella L.M. Ant algorithms for discrete optimization. Artificial Life,1999,5(2):137-172.
[149]Clerc M., Mennedy, J. The particel swarm-Explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on evolutionary computation,2002,6(1):58-73.
[150]Del Valle Y.G., Venayagamoorthy K., Mohagheghi S., and et al. Particle swarm optimization:basic concepts, variants and applications in power systems. IEEE Transactions on evolutionary computation,2008,12(2):171-195.
[151]Kirkpatrick S., Gelatt C.D., Vecchi M.P. Optimization by simulated annealing. Science,1983,220:671-80.
[152]Chen W.H., Srivastava B. Simulated annealing procedures for forming machine cells in group teaching. European Journal of Operational Research,1994,75(1): 100-111.
[153]李兆华.过程综合的混合整数非线性规划嵌套求解方法.浙江大学硕士论文,2008.
[154]汪定伟,王俊伟,王洪峰,张瑞友,郭哲.智能优化方法：高等教育出版社,2007.
[155]王新茹.基于粒子群算法的智能航班调度方法研究.中国民航大学硕士论文,2008.
[156]胡家声,郭创新,叶彬,段惠明,曹一家.离散粒子群优化算法在输电网络扩展规划中的应用.电力系统自动化,2004，28(20)：31-37.
[157]Kennedy J., Eberhart R.C. A discrete binary of the particle swarm algorithm. IEEE International Conference on systems, man and cybernetics. Piscataway,1997.
[158]Huang S., Ren W. Automatic vehicle following with integrated throttle and brake control. International journal of control,1999,72(1):75-83.
[159]Beak W., Song B. Design and validation of a longitudinal velocity and distance controller via hardware-in-the-loop simulation. International journal of Automative Technolocy,2009,10(1):95-102.
[160]Kerner B.S. The physics of traffic:Springer,2005.
[161]Schonhof M., Kesting A., Helbing D. Coupled vehicle and information flows: Message transport on a dynamic vehicle network. Physica A,2006,363(1):73-81.
[162]Almut H., Michael C. Investigating the potential of convoy driving for congestion dispersion. IEEE Conference on Intelligent Transportation systems,1997: 735-740.
[163]Chi-ying L., Huei P. Optimal adaptive cruise control with guaranteed string stability. Vehicle system dynamics,1999,31:313-330.
[164]Yao-ming Y., Rui J., Mao-bin H., Qing-song W., and Ruili W. Traffic flow characteristics in a mixed traffic system consisting of ACC vehicles and manual vehicles:a hybrid modelling approach. Physica A,2009,388:2483-2491.
[165]Kouvaritakis B., Cannon M., Tsachouridis V. Recent developments in stochastic MPC and sustainable development. Annual Reviews in Control,2004,28,23-35.
[166]Ioannou P., Xu Z. Throttle and brake control system for automatic vehicle following. Journal of Intelligent Vehicle Highway System,1994,1(4):345-377.
[167]Kun L., Ioannou P. Modeling of traffic flow of automated vehicles. IEEE Transaction on Intelligent Transportation System,2004,5(2):99-113.
[168]Reinhart K., Panos M. Continuum flow models. Traffic flow theory,1992.
[169]Karaaslan U., Varaiya P., Warland J. Two proposals to improve freeway traffic flow. Americal Control Conference,1991:2539-2544.
[170]刘运通,石建军,熊辉.交通系统仿真技术,北京：人民交通出版社,2002.
[171]Rothery R.W. Traffic flow theory:Car following models.1992.
[2]杨兆升.城市道路交通系统智能系统理论与实施方法,北京,中国铁道出版社,2002.
[3]Kubozuka T. Perspective of ITS Technology:A scenario. Proceeding of International Symposium on Advanced Vehicle Control,2002.
[4]《中国智能运输系统体系框架》专题组：中国智能运输系统体系框架.人民交通出版社,2003.
[5]陈旭梅,于雷,郭继孚,全永淼.美国智能交通系统ITS的近期发展综述.中外公路,2003,23(2)：9-12.
[6]侯德藻.汽车纵向主动避撞系统的研究.清华大学博士论文,2004.
[7]Lingyun X., Feng G. A comprehensive review of develpment of adaptvie cruise control systems. Vehicle System Dynamics,2010,48(10):1167-1192.
[8]Rajesh R. Dynamics and Control. Springer,2006.
[9]Vahidi A., Eskandarian A. Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Transactions on Intelligent Transportation System, 2003,4(3):143-153.
[10]Rajamani. R, Zhu C. Semi-Autonomous Adaptive Cruise Control Systems. IEEE Transactions on Vehicular Technology,2002,51(5):1186-1192.
[11]Greg M., Mark M., Brackstone M. Towards an understanding of adaptive cruise control. Transportation Reserch Part C,2001(9):33-51.
[12]田雷.汽车自适应巡航系统的模糊自校正控制算法研究.吉林大学硕士论文,2003.
[13]刘中海.自适应巡航控制系统的设计与仿真.清华大学硕士论文,2005.
[14]Shaout A., Jarrahz A. Cruise control techonology review. Computer Electrical Engineer,1997,23:259-271.
[15]Diamond H., Lawrence W. The development of an automatically controlled highway system. Transportation Research Institute's Report UMR 1266, University of Michigan,1966.
[16]Levine S., Athans M. On the optimal error regualteion of a string of moving vehicles. IEEE Transactions on Automatic Control,1996,11:355-361.
[17]Bender J., Fenton R. On the flow capacity of automated highways. Transportation Science,1970,4:52-63.
[18]Bender J., Fenton R., Olson K. An experimental study of vehicle automatic longitudinal control. IEEE Transactions on Vehicular Technology,1971,20:114-123.
[19]Fenton R., Mayhan J. Automated highway studies at the Ohio state University-An overview. IEEE Transactions on Vehicular Technology,1991,40:100-13.
[20]Caudill R., Garrard W. Vehicle follower longitudinal conrol for automated transit vehicles. Journal of Dynamic System, Measurement, and Control,1977:241-248.
[21]Garrard W. Longitudinal control for automated guideway transit vehicles. Report UMTA-MN-11-77-2, University of Minnesota,1977.
[22]Hesse R. Cabinentaxi- A personal public transportation system. Transportation, 1972,1:321-330.
[23]Maury J. The ARMIS PRT system. SAE International, SAE paper 740143,1974.
[24]Catling I. Advanced Technoloby for road transport:IVHS and ATT, Boston: Artech House,1993.
[25]Broqua F., Lerner G., Mauro V, Morello E. Cooperative driving:Basic concepts and a first assessment of 'Intelligent cruise control'strategies. Proceedings of the DRIVE Conference. Brussels,1991.
[26]Zhang X., Benz T. Simulation and evaluation of intelligent cruise control. IVHS Journal,1993,1(2):181-190.
[27]Joachim S., Walter S. Adaptive cruise control for coaches. International Symposim on Advanced vehicle control. Aachen University of Technology,1996,3: 1351-1366.
[28]Bishop R. Intelligent vehicle technology and trends, Boston:Artech House, 2005.
[29]Ioannou P., Ahmed-Zaid F., Wuh D. A time headway autonomous intelligent cruise controller:design and simulation,. California PATH Working paper, UCB-ITS-PWP-94-07,1994.
[30]Ioannou P. Development and experimental evaluation of autonomous vehicles for roadway/vehicle cooperative driving. Calfornia PATH Research Paper, UCB-ITS-PRR-98-9,1998.
[31]Darbha S., Rajagopal K. Intelligent cruise control systems and traffic flow stability. California PATH Research Paper, UCB-ITS-PRR-98-36,1998.
[32]Bishop R. Intelligeng Vehicle Technoloby and Trends, Boston:Artech House,2 005.
[33]Fancher P., Ervin R., Sayer J. Intelligent cruise contro field operation test (final report). Transportation Research Institute, University of Michigan,1998.
[34]Tsugawa S., Aoki M., Hsaka A., Seki K. A survey of present IVHS activities in Japan. Control Engineer Practice,1997,5(11):1607-1614.
[35]Ioannou P. Automated Highway Systems, New York:Plenum Press,1997.
[36]朱永强.汽车自适应巡航系统的控制策略开发及行驶环境评估.吉林大学硕士论文,2007.
[37]Yanakiev D., Kanellakopoulos I. Nonlinear Spacing Policies for Automated Heavy-Duty Vehicles. IEEE Transactions on Vehicular Technology,1998,47(4): 1365-1377.
[38]Zhou J., Peng H. Range policy of adaptive cruise control vehicles for improved flow stability and string stability. IEEE Transactions on Intelligent Transportation System,2005,6(2):229-237.
[39]Swaroop D., Hedrick J.K., Chien C.C., Ioannou. P. A comparison of spacing and headway control laws for automatically controlled vehicles. Vehicle System Dynamics,1994,23:597-625.
[40]Lingyun X., Feng G., Jiangfeng W. On scalability of platoon of automated vehicles for leader-predecessor information framework. IEEE Intellitent vehicles Symposium. China,2009.
[41]Darbha S., Hedrick J. Constant Spacing Strategies for Platooning in Automated Highway Systems. Journal of Dynamics Systems, Measurement and Control,1999, 121:462-470.
[42]Santhanakrishnan K., Rajamani R. On spacing policies for highway vehicle automation. IEEE Transactions on Transportation systems,2003,4(4):198-204.
[43]Ioannou P., Chien C. C. Autonomous intelligent cruise control. IEEE Transactions on Vehicular Technology,1993,42:657-672.
[44]Tsang-Wei L., Sheue-Ling H., Paul A.G. Effects of time-gap settings of adaptive cruise control (ACC) on driving performance and subjective acceptance in a bus driving simulator. Safety Sciences,2009,47:620-625.
[45]Yi-Hsien C., Jyh-Ching J. Longitudinal vehicle control with the spacing policy in consideration of brake input limits. IEEE International Conference on Systems, Man and Cybernetic,2007:1705-1710.
[46]Swaroop D., Rajagopal K.R. Intelligent cruise control system and traffic flow stability. Transportation Research Part C:Emerging Technologies,1999,7(6): 329-352.
[47]Li P., Shrivastava A. Traffic flow stability induced by constant time headway policy for adaptive cruise control vehicles Transportation Research Part C:Emerging Technologies,2002,10(4):275-301.
[48]Jin Z., Masahiro O., Abdelkader E.K., A safety spacing policy and its impact on highway traffic flow. IEEE Symposium on Intellgient vehicles,2009:960-965.
[49]Jianlong Z., loannou P. Adaptive vehicle following control system with variable time headways.44th IEEE Conference on Decision and Control,2005:3880-3885.
[50]Broqua F., Lerner G., Mauro V., Morello E. Cooperative driving:basic concepts and a first assessment of the intelligent cruise control strategies. Proceedings of Drive Conference,1991:908-929.
[51]Wang J., Rajamani R. Adaptive cruise control system design and its impact on traffic flow. Proceedings of the American Control Conference. Alaska,2002.
[52]Greenshields B.D. A study of traffic capacity. Proceedings of HRB,1934,14: 448-477.
[53]橘本佳幸,里中久志,重松崇.衝突回避スシのム開.自动车技述会学述演会前刷集,1994：57-60.
[54]Xu Q., Hedrick K., Sengupta R., VanderWerf J. Effects of vehicle-vehicle/ roadside-vehicle communication on adaptive cruise controlled highway systems. Proceedings of IEEE Vehicular Technology. Canada,2002:1249-53.
[55]Fancher P., Bareket Z., Peng H., Ervin R. Research on desirable adaptive cruise control behavior in traffic streams. University of Michigan Transportation Research Institute Report (UMTRI-2002-16),2002.
[56]宾洋.汽车行驶纵向距离自适应控制系统的研究.重庆大学硕士论文,2002.
[57]Rajamani R., Zhu C. Semi-autonomous adaptive cruise control systems. IEEE Transactions on Vehicular Technology,2002,51 (5):1186-1192.
[58]Bageshwar V.L., Garrard W.L., Rajamani R. Model Predictive control of transitional maneuvers for adaptive cruise control vehicles. IEEE Transactions on Vehicular Techonology,2004,53(5):1573-1585.
[59]Rajamani R. Vehicle dynamics and control, Springer,2006.
[60]Jianlong Z., Ioannou P.A. Longitudinal control of heavy trucks in mixed traffic: environmental and fuel economy considerations. IEEE Transactions on Intelligent Transportation System,2006,7(1):92-104.
[61]Paul V, Karl N., Bartono A. Stop and go cruise control. Proceedings of Seoul FISITA world automotive congress,2000:1-8.
[62]Abdullah R., Hussain A., Warwick K., and Zaycd A. Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-baxed switching and tuning. Neurocomputing,2008,71(13):2727-2741.
[63]Yamamura Y., Tabe M., M. Kanehira, et al. Developement of an adaptive cruise control system with stop and go capability. SAE Technical paper 2001-01-0798 2001.
[64]Canale M., Malan S. Tuning of stop and go driving control strategies using driver behavior analysis. IEEE Intelligent Vehicle Symposium,2002:407-412.
[65]Chiying L., Huei P. Optimal adaptive cruise control with guaranteed string stability. Vehicle system dynamics,1999,31:313-330.
[66]No T.S, Chong K.T., Roh D.H. A Lyapunov function approach to longitudinal control of vehicles in a platoon. IEEE Transactions on Vehicular Techonology,2001, 50(1):116-124.
[67]Yi S.Y., Chong K.T. Impedance control for a vehicle platoon system. Mechatronics,2005,15:627-638.
[68]Martinez J.J., Canudas-de-wit C. A safe longitudinal control for adaptive cruise control and stop and go scenarios. IEEE Transactions on Control Systems Technology, 2007,15(2):789-814.
[69]Marhefka D.W., Orin D.E. Simulation of contact using a nonlinear damping model. IEEE International Conference on Robotics and Automation. Minneapolis, 1996:1662-1668.
[70]刘中海.自适应巡航控制系统设计与仿真.清华大学计算硕士论文,2005.
[71]Rui J and Qing-song W. The adaptive cruise control vehicles in the cellular automata model. Physics Letter A,2006,359:99-102.
[72]管欣,王景武,高振海.基于最优预瞄加速度决策的汽车自适应巡航控制系统.吉林大学学报(工学版),2004,34：189-193.
[73]吴利军,刘昭度,何玮.汽车ACC跟随控制策略的研究.汽车工程,2005,27：514-521.
[74]李果,张良起,张彭.无人陆地车辆的自学习PD迭代纵向控制器.控制理论与应用,1994,11：713-719.
[75]张德兆,王建强,刘佳熙,李克强,连小珉.加速度连续型自适应巡航控制模式切换策略.清华大学学报,2010,50(8)：1277-1281.
[76]申瑞玲.车距的模糊自适应控制及其应用研究.东南大学硕士论文,2005.
[77]朱晓宏.车辆自动驾驶纵向运动模糊神经控制研究.武汉理工大学硕士论文,2003.
[78]Gerdes J.C., Hedrick J.K. Vehicle speed and spacing control via coordinated throttle and brake actuation. Control Engineer Practice 1997,5:1607-14.
[79]Zhang Y., Kosmatopoulos E.B., and Ioannou P.A. Autonomous intelligent cruise control using front and back information for tight vehicle following manuevers. IEEE Transactions on Vehicular Techonology 1999,48:319-28.
[80]Hedrick J.K., Lu X.Y. Heavy-duty vehicle modeling and longitudinal control,. Vehicle system dynamics 2005,43:653-69.
[81]Kuragaki S., Kuroda H., Chi-Minowa T., and et al. An adaptive control using wheel torque management techinque.
[82]Yi K., Hong J., and Kwon Y.D. A vehicle control algorithm for stop and go cruise control. Proceedings of the Institution of Mechanical Engineers,2001:1099-1115.
[84]宾洋,车辆走停巡航系统的非线性控制研究.清华大学博士论文,2006.
[85]舒迪前.预测控制系统及其应用,机械工业出版社,1995.
[86]丁宝苍.预测控制的理论与方法,机械上业出版社,2008.
[87]徐祖华.模型预测控制理论及应用研究.浙江大学博士论文,2004.
[88]许超,陈治纲,邵惠鹤.预测控制技术及其应用发展综述.化工自动化及仪表,2002,29(3)：1-10.
[89]钱积新,赵均,徐祖华.预测控制,北京：化学工业出版社,2007.
[90]石宇静.复杂工业过程模型预测控制的研究.东北大学博士论文,2009.
[91]Boyd S., Vandenberghe L. Convex Optimization:New York:Cambridge University,2004.
[92]Sanchez A., Katebi MR. Predictive control of dissolved oxygen in an activated sludge waste water treatment plant. Proceedings of the European Conrol Conference ECC. Cambridge, UK,2003.
[93]谢生钢.基于活性污泥模型的污水处理过程预测控制力法研究.浙江大学硕士论文,2008.
[94]杜建福.基于预测控制的小型无人直升机的自主飞行研究.上海交通大学博士论文,2008.
[95]Yasunobu S., Miyamoto S., Takaoka T., and Ohsihima H. Application of predictive fuzzy control to automatic train operation controller. Proceedings of Industrial Electronics Control and Industrumentation Tokyo, Japan,1984:657-662.
[96]Oshima H., Yasunobu S., and Sekino S.I. Automatic train operation system based on predictive fuzzy control. Proceedings of the International Workshop on Artificial Intelligence for Industry Applications,1988:485-489.
[97]黄秀玲.广义预测控制算法在ATO系统中的应用与研究.西南交通大学硕士论文,2009.
[98]Leung C., Huang S.D., Kwok N., and Dissanayake G. Planning under uncertainty using model predictive control for information gathering. Robotics and autonomous systems,2006,54(11):898-910.
[99]Yoo S.J., Choi Y.H., Park J.B. Generalized model predictive control based on self-recurrent wavelet neural-network for stable path tracking of mobile robots: adaptive learning rates approach. IEEE Transactions on Circuits and Systems I,2006, 53(6):1381-1394.
[100]韩光信,陈虹,马苗苗,赵海艳.约束非完整移动机器人轨迹跟踪的非线性预测控制.吉林大学学报(工学版),2009,39(1)：177-181.
[101]Rajagopalan A., Washington G., Rizzoni G., and Guezennec Y. Development of fuzzy logic and neural network control and advanced emisssions modeling for parallel hybrid vehicles. Report:NREL/SR-540-32919,2003.
[102]Kim T.S., Manzie C., and Shama R. Model predictive control of velocity and torque split in a parallel hybrid vehicle. IEEE International Conference on Systems, Man and Cybernetics. San Antonio,2009.
[103]Beck R., Bollig A., AbeI D. Comparison of two real-time predictive strategies for the optimal energy management of a hybrid electric vehicle. Oil & Gas Science and Technology,2006,62(4):635-643.
[104]Kermani S., Delprat S., Guerra T.M, and Trigui R. Predictive control for HEV energy management:experimental results. Vehicle Power and Propulsion Conference. Dearborn,2009:364-369.
[105]潘文军.中度混合动力汽车再生制动模型预测控制策略.重庆大学硕士论文,2010.
[106]Corona D., Schutter B.D. Adaptive cruise control for a smart car:a comparison benchmark for MPC-PWA control methods. IEEE Transactions on Control systems technoloby,2008,16(2):365-372.
[107]Brackstone M., McDonald M. Car-following:a historical review. Transportation Research Part F:Traffic psychology and Behavior,1990,2(4): 181-196.
[108]Prigogine I., Herman R. Kinetic theory of vehicular traffic, New York: American Elseiver,1971.
[109]姜锐.交通流复杂动态特性的微观和宏观模式研究.中国科技大学博士论文，2005.
[110]Lighthill J.J., Whitham G.B. On kinematic waves Ⅱ:A theory of traffic flow on long croweded roads. Proc. Royal Society London,1955:317-345.
[111]Richards PI. Shock waves on the highway. Operations research,1956,4(1): 42-51.
[112]张晋.基于元胞自动机的混合交通流建模方法研究.浙江大学博士论文,2004.
[113]Payne H.J. Models of freeway traffic and control. Mathematical models of public systems, simulation Council,1971:51-61.
[114]Philips W.F. A new continuum traffic model obtained from kinetic theory. IEEE transactions on Automatic Control,1978,23:1032-1036.
[115]Kuhne R.D. Macroscopic freewaay model for dense traffic-stop start waves and incident detection. The 9th International Symposium on Transportation and Traffic theory,1984:21-41.
[116]Michalopoulos P.G, Yi P., Lyrintzis A.S. Continuum modeling of traffic dynamics for congested freeways. International journal of transportation research, 1993,27(4):315-352.
[117]Papageorgiou M., Blosseville J.M., Hadj S.H. Modleing and real time control on traffic flow on the southern part of Boulevard Peripherique in Paris(Part Ⅰ modeling, Part Ⅱ:coordinated on-ramp metering. Transportation Research Part A, 1990,24(5):345-370.
[118]Kotsialos A., Papageorgiou M., Mangeas M., and Haj-Salem H. Coordianted and integrated control of motorway networks via non-linear optimal control. Transportation Research Part C,2002,10:65-84.
[119]吴正.交通流的动力学模拟与测量方法.复旦学报(自然科学版),1991,30(1)：111-117.
[120]戴世强,冯苏苇,顾国庆.交通流动力学：它的内容、方法、意义.自然杂志,1997,19(4)：196-201.
[121]Swaroop D., Rajagopal K.R. A review of constant time headway policy for automatic vehicle following. IEEE Intelligent Transportation systems. Oakland,2001: 65-69.
[122]Ioannou P., Xu Z., Eckert S., Clemons D., and Sieja T. Intelligent Cruise Control:theory and experiment. Proceedings of the 32nd IEEE conference on Decision and Control,1993.
[123]James W.J., Neil, D.L., Steve M., Scott O., Brain C.T. Use of advanced in-vehicle techonology by young and older early adopters, Survey results on Adaptive Cruise Control systems. Report No. DOT HS 810917:National Highway Traffic Safety Administration,2008.
[124]汪孝宗,胡雪琴,王红茹,宋学莲.中国如何实现减排目标：十二五减排措施将出台.中国经济周刊,2009.
[125]Naus G., van den Bleek R., Ploeg J., Scheepers B., van de Molengraft R., and Steinbuch M. Explicit MPC design and performance evaluation of an ACC Stop&Go. American Control Conference,2008:224-229.
[126]Chien C.C., Ioannou P., Lai M.C., Entrainment and vehicle following controllers design for autonomous intelligent vehicles, American Control Conference, 1994,1:6-10.
[127]Srdjan S.S., Milorad J.S., Dragoslav D.S., Decentralized overlapping control of a platoon of vehicles. IEEE Transactions on control systems technology,2000,8(5): 816-832.
[128]Kyongsu Y., Jintai C., Nonlinear brake control for vehicle CW/CA systems. IEEE/ASME Transactions on Mechatronics,2006,6:17-25.
[129]Lee G.D., Kim S.W. A longitudinal control system for a platoonn of vehicles using a fuzzy-sliding mode algorithm. Mechatronics,2002,12:97-118.
[130]Barth M., Scora G., Younglove T. Modal emissions model for heavy-duty diesel vehicles.83rd Annual Meeting of the Transportation-Research-Board. Washington, DC,2004:10-20.
[131]Moon S., Yi K. Human driving data-based design of a vehicle adaptive cruise control algorithm. Vehicle System Dynamics,2008,46(8):661-690.
[132]Somda F.H., Cormerais H., Buisson J. Intelligent transportation systems:a safe, robust and comfortable strategy for longitudinal monitoring. IET Intelligent Transportation System,2009,3(3):188-197.
[133]Yi K., Han D. A vehicle stop-and-go control strategy based on Human Drivers Charasteristics. Journal of Mechanical Science and Technoloby,2005,19(4): 993-1000.
[134]Naranjo J.E., Gonzalez C., Garcia R., and Pedro T.D. Cooperative Throttel and Brake Fuzzy Control for ACC Stop&Go Maneuvers. IEEE Transactions on Vehicle Technoloby,2007,56(4):1623-1630.
[135]Chakroborty P., Kikuchi S. Evaluation of the general motors based car-following models and a proposed fuzzy inference model. Transportation Research Part C:Emerging Technologies,1999,7:209-235.
[136]Dong-fan X., Zi-you G., Xiao-mei Z. The effect of ACC vehicles to mixed traffic flow consisting of manual and ACC vehicles. Chinese Physics B,2008,17(12): 4440-4446.
[137]Adminstration F.H. NGSIM Overview. Washington, DC:Federal Highway Administration,2006.
[138]Lu X.Y., Varaiya P., Horowitz R., and Skabardonis A. Fundamental Diagram Modeling from NGSIM data. The 2nd International Symposium on Freeway and Tollway Operations. Hawaii,2009.
[139]Colyar J., Halkias J. US Highway 101 Dataset. Federal Highway Adminstration, 2007.
[140]武欣.微观交通模型的全微观参数校正及仿真平台的研究.浙江大学硕士论文,2008.
[141]Zadeh L.A. FUZZY SETS. Informational and Control,1965,8(3):338-353.
[142]Floudas C.A. Nonlinear and mixed-integer ingeter optimization:Oxford University,1995.
[143]张聚,李平,王万良.基于Branch&Bound方法MIQP问题的求解及应用.系统仿真学报,2003,15(4)：488-491.
[144]Geoffrion A.M. Generalized benders decomposition. Journal of Optimization Theory and Applications,1972,10(4):237-260.
[145]Duran A.M., Grossmann I.E. An outer-approximation algorithm for a class of mixed-integer nonlinear programs. Mathematical Programming,1986,36:307.
[146]Silver E.A. An overview of heuristic solution methods. Journal of Operation. Research Society,2004,55(9):936-956.
[147]Whitley D. A genetic algorithm tutorial. Statistics and Computing,1994,4(2): 65-85.
[148]Dorigo M., Caro G.D., Gambardella L.M. Ant algorithms for discrete optimization. Artificial Life,1999,5(2):137-172.
[149]Clerc M., Mennedy, J. The particel swarm-Explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on evolutionary computation,2002,6(1):58-73.
[150]Del Valle Y.G., Venayagamoorthy K., Mohagheghi S., and et al. Particle swarm optimization:basic concepts, variants and applications in power systems. IEEE Transactions on evolutionary computation,2008,12(2):171-195.
[151]Kirkpatrick S., Gelatt C.D., Vecchi M.P. Optimization by simulated annealing. Science,1983,220:671-80.
[152]Chen W.H., Srivastava B. Simulated annealing procedures for forming machine cells in group teaching. European Journal of Operational Research,1994,75(1): 100-111.
[153]李兆华.过程综合的混合整数非线性规划嵌套求解方法.浙江大学硕士论文,2008.
[154]汪定伟,王俊伟,王洪峰,张瑞友,郭哲.智能优化方法：高等教育出版社,2007.
[155]王新茹.基于粒子群算法的智能航班调度方法研究.中国民航大学硕士论文,2008.
[156]胡家声,郭创新,叶彬,段惠明,曹一家.离散粒子群优化算法在输电网络扩展规划中的应用.电力系统自动化,2004，28(20)：31-37.
[157]Kennedy J., Eberhart R.C. A discrete binary of the particle swarm algorithm. IEEE International Conference on systems, man and cybernetics. Piscataway,1997.
[158]Huang S., Ren W. Automatic vehicle following with integrated throttle and brake control. International journal of control,1999,72(1):75-83.
[159]Beak W., Song B. Design and validation of a longitudinal velocity and distance controller via hardware-in-the-loop simulation. International journal of Automative Technolocy,2009,10(1):95-102.
[160]Kerner B.S. The physics of traffic:Springer,2005.
[161]Schonhof M., Kesting A., Helbing D. Coupled vehicle and information flows: Message transport on a dynamic vehicle network. Physica A,2006,363(1):73-81.
[162]Almut H., Michael C. Investigating the potential of convoy driving for congestion dispersion. IEEE Conference on Intelligent Transportation systems,1997: 735-740.
[163]Chi-ying L., Huei P. Optimal adaptive cruise control with guaranteed string stability. Vehicle system dynamics,1999,31:313-330.
[164]Yao-ming Y., Rui J., Mao-bin H., Qing-song W., and Ruili W. Traffic flow characteristics in a mixed traffic system consisting of ACC vehicles and manual vehicles:a hybrid modelling approach. Physica A,2009,388:2483-2491.
[165]Kouvaritakis B., Cannon M., Tsachouridis V. Recent developments in stochastic MPC and sustainable development. Annual Reviews in Control,2004,28,23-35.
[166]Ioannou P., Xu Z. Throttle and brake control system for automatic vehicle following. Journal of Intelligent Vehicle Highway System,1994,1(4):345-377.
[167]Kun L., Ioannou P. Modeling of traffic flow of automated vehicles. IEEE Transaction on Intelligent Transportation System,2004,5(2):99-113.
[168]Reinhart K., Panos M. Continuum flow models. Traffic flow theory,1992.
[169]Karaaslan U., Varaiya P., Warland J. Two proposals to improve freeway traffic flow. Americal Control Conference,1991:2539-2544.
[170]刘运通,石建军,熊辉.交通系统仿真技术,北京：人民交通出版社,2002.
[171]Rothery R.W. Traffic flow theory:Car following models.1992.