汽车主动防撞系统研究
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摘要
随着汽车的大规模生产、使用和公路等级的不断提高,特别是高速公路的飞速发展,汽车的行驶速度逐渐加快,汽车安全问题越来越受到人们的重视。因此,研究汽车主动防撞系统对提高汽车行驶的安全性,避免事故的发生有重要的理论意义和实用价值。
本论文在分析现有研究基础上,提出建立基于热红外成像传感器的汽车主动防撞系统,汽车正常行驶时,该系统不发生作用,当汽车与目标物之间距离小于相应行驶条件下设定的安全距离时,系统发出报警信息,提示驾驶员采取相应措施,及时避免事故的发生。
在对汽车制动过程分析的基础上,得出汽车制动距离的一般模型。依据汽车制动距离模型的要求和目标物的运动类型得出汽车安全距离模型,给出安全距离的计算方法。
道路附着系数是计算安全距离的重要参数之一,但道路附着系数由于路面条件不同很难实际测量。利用回忆对比的方法修改附着系数,将经典理论数据输入神经网络,并对模型进行训练巩固,在不同路面类型进行制动实验获得实验数据,将测得实验值与理论数值进行对比修正,使系统内的附着系数值和实际附着系数越来越接近,为准确计算汽车安全距离提供依据。
将热红外成像传感器采集的图像用灰度增强法进行增强,并对图像进行分割以突出目标物。由于图像分割后,还可能有次目物体影响判断,因此对分割后的图像进行形态学处理,有效地将目标图像区域从场景区域中分割出来。通过投影成像关系,重新构建立体视图从而测出目标物的距离,并给出目标物的运动类型判断公式。
建立了汽车防撞专家系统,设计了专家系统推理机和知识库。将汽车安全行驶的知识规则以及安全行驶的推理策略导入专家系统,以汽车驾驶知识作为辅助指导操作在危险情况下进行报警避免汽车碰撞。并依据汽车在实际行驶的需要设计了信号采集、信号处理、修改参数和报警提示等模块的系统程序。
with the mass production of vehicles and the construction of the highway, the vehicle runs increasing fast. So, more people pay more attention to vehicle safety today. Therefore, the study of active collision avoidance system to improve the safety vehicle to avoid accidents has important theoretical and practical value.
In this paper, the analysis of existing studies based on thermal infrared imaging based on vehicle active anti-collision sensor system. The system does not work when the vehicle runs normally. When the distance between object and the vehicle is less than a safe distance, this system works to give the driver alarming to avoid the accident.
The model for calculating the distance of braking was set on the foundation of analyzing the brake procedure. The algorithm for calculate the safe distance was obtained according to the general models of the braking distance of car.
The road adhere coefficient is a vital parameter for calculating the safety distance. However, the road adhere coefficient is difficult to be measured when the road condition changed. The artificial neural network model was set after studying the road adhere coefficient and utilize the existing theory. The signals gathered in the process of braking were transmitted to the network and the parameters were revised using these signals to make the parameter has the least difference with actual value.
The image enhanced algorithm was applied in the enhancement of the infrared. Then, the image was segmented to detect the object. But there are other objects in the image after segmentation. The mathematical morphology was used to process the image after segment at the same time in order to improve the effect of object detection. The distance between the vehicle and the object was calculated after using the projection imaging model. And the moving model determination function was constructed at the same time.
In addition, the vehicle security expert system was constructed after the studying of the expert system. The vehicle safe running rules and the deduce algorithm were input to the expert system after analyzing the knowledge of the expert system is the key method for anti-collision. This system provides an effect assistant drive method, which can escape the accident. At last, the signal capture and process, parameters modify and alarming models were designed according to the need of the vehicle running condition.
本论文在分析现有研究基础上,提出建立基于热红外成像传感器的汽车主动防撞系统,汽车正常行驶时,该系统不发生作用,当汽车与目标物之间距离小于相应行驶条件下设定的安全距离时,系统发出报警信息,提示驾驶员采取相应措施,及时避免事故的发生。
在对汽车制动过程分析的基础上,得出汽车制动距离的一般模型。依据汽车制动距离模型的要求和目标物的运动类型得出汽车安全距离模型,给出安全距离的计算方法。
道路附着系数是计算安全距离的重要参数之一,但道路附着系数由于路面条件不同很难实际测量。利用回忆对比的方法修改附着系数,将经典理论数据输入神经网络,并对模型进行训练巩固,在不同路面类型进行制动实验获得实验数据,将测得实验值与理论数值进行对比修正,使系统内的附着系数值和实际附着系数越来越接近,为准确计算汽车安全距离提供依据。
将热红外成像传感器采集的图像用灰度增强法进行增强,并对图像进行分割以突出目标物。由于图像分割后,还可能有次目物体影响判断,因此对分割后的图像进行形态学处理,有效地将目标图像区域从场景区域中分割出来。通过投影成像关系,重新构建立体视图从而测出目标物的距离,并给出目标物的运动类型判断公式。
建立了汽车防撞专家系统,设计了专家系统推理机和知识库。将汽车安全行驶的知识规则以及安全行驶的推理策略导入专家系统,以汽车驾驶知识作为辅助指导操作在危险情况下进行报警避免汽车碰撞。并依据汽车在实际行驶的需要设计了信号采集、信号处理、修改参数和报警提示等模块的系统程序。
with the mass production of vehicles and the construction of the highway, the vehicle runs increasing fast. So, more people pay more attention to vehicle safety today. Therefore, the study of active collision avoidance system to improve the safety vehicle to avoid accidents has important theoretical and practical value.
In this paper, the analysis of existing studies based on thermal infrared imaging based on vehicle active anti-collision sensor system. The system does not work when the vehicle runs normally. When the distance between object and the vehicle is less than a safe distance, this system works to give the driver alarming to avoid the accident.
The model for calculating the distance of braking was set on the foundation of analyzing the brake procedure. The algorithm for calculate the safe distance was obtained according to the general models of the braking distance of car.
The road adhere coefficient is a vital parameter for calculating the safety distance. However, the road adhere coefficient is difficult to be measured when the road condition changed. The artificial neural network model was set after studying the road adhere coefficient and utilize the existing theory. The signals gathered in the process of braking were transmitted to the network and the parameters were revised using these signals to make the parameter has the least difference with actual value.
The image enhanced algorithm was applied in the enhancement of the infrared. Then, the image was segmented to detect the object. But there are other objects in the image after segmentation. The mathematical morphology was used to process the image after segment at the same time in order to improve the effect of object detection. The distance between the vehicle and the object was calculated after using the projection imaging model. And the moving model determination function was constructed at the same time.
In addition, the vehicle security expert system was constructed after the studying of the expert system. The vehicle safe running rules and the deduce algorithm were input to the expert system after analyzing the knowledge of the expert system is the key method for anti-collision. This system provides an effect assistant drive method, which can escape the accident. At last, the signal capture and process, parameters modify and alarming models were designed according to the need of the vehicle running condition.
引文
[1]钟志华.张维刚.曹立波等.汽车安全安全技术[M].北京:机械工业出版社,2005.
[2] Alenak Hudoklin.Vojan Rozman. Safety analysis of the railway traffic systems, Reliability engineering and systems safety.1992, 37:7-13.
[3]赵俊.智能交通系统浅析.山西警官高等专科学校学报,2002,3(1):47~48
[4]李峰.智能交通系统在国外的发展趋势.国外公路,1999,19(1):1~5
[5]徐利民.智能交通系统发展及其战略思考.技术经济,2003,4:15~16
[6]张丰焰.现代智能交通系统及其发展趋势.交通科技,2002,1:11~12
[7]柴毅.黄席褪.周欣.汽车驾驶智能型防碰撞系统研究.重庆大学学报(自然科学版).2001.24(4)
[8]宋晓琳.冯广刚.杨济匡.汽车主动防撞系统的发展现状及趋势[J].汽车工程, 2008,30(4): 285～290.
[9]粟菊.智能交通系统(ITS)在国外[J].中国机电工业.2000.9:21~22.
[10]谢飞.未来智能汽车及智能汽车交通系统.汽车技术.
[11]杨东凯.吴今培.张其善.智能交通系统( ITS)的发展及其模型化研究.北京航空航天大学学报.2002,26(1):22~25
[12]韩赞东.陈强尉.昊斌贝.超声定位技术在汽车安全预警系统中的应用.测控技术.2002,21(8):10~14
[13]赵建顺.王玉泰.超声汽车防撞系统的研究济南大学学报.2002,15(3):216~218
[14]贺乐厅.基于毫米波雷达的车辆防撞技术与实验研究.[D] .东南大学,2003.
[15]蒋飞.汽车主动防撞雷达系统的研究.[D]武汉理工大学,2006.
[16]陈勇.黄席樾.杨尚罡.汽车防撞系统的研究与发展.[J]计算机仿真.2006,23(12):239~242
[17]程玉兰.红外现场诊断技术[M].北京:机械工业出版社,2002.
[18] GREHART G. Thermal image modeling[C]. Proceedings of SPI Eon Infrared Sensor s and Sensor Fusion, 1987, 782: 3- 9.
[19] STETS J. Synthetic IR scene generation[C] .SPIE on Infrared Systems and Components II, 1988, 890: 130- 146.
[20] MICHAEL R W. Infrared signature generation of airborne targets [C] . Proceedings of SPIE, Character inaction, Propagation, and Simulation of Sources and Backgrounds III, 1993, 1967: 114- 122.
[21] HONG H K, HAN S H, HONG G P, etal Simulation of reticleseekers using the generated thermal images [C] .IEEE Asia Pacific Conference on Circuit and System96, 1996, 1:183- 186.
[22] YU Wei jie,Peng Qun sheng,TU Hong midget al.An infrared image synthesis model based on infrared physics and heat transfer [J].International Journal of Infrared & Millimeter Waves,1998, 19(12): 1661- 1669.
[23] YU Wei jie,TU Hong ming ,WANG Zhang ye,et al.An infrared image synthesis model for high speed targets [J].International Journal of Infrared & Millimeter Waves,2002,23 (12): 1743 -1751.
[24] BALFOU L S.Determination of infrared contrast transmittance through an obscuring atmosphere [C] .Proceedings of SPIE,Character ization, Propagation, and Simulation of InfraredScenes,1990,1311: 146- 155.
[25] KOPEIKA N S.The role of the atmosphere in target acquisition: model verus experiment [C] .Proceedings of SPIE, Infrared Technology and Application XXIV, 1998, 3436:867- 878.
[26]赵玉洁,国外汽车自动防撞系统发展迅速. [J]自动化博览,1992,10:18
[27]李金英.国外汽车防撞装置. [J]国外技术:38~39
[28]SmulderSA·Modeling and Filtering of Freeway Traffic Flow,Transportation and Traffic Theory,1987;139-158.
[29]侯德藻.汽车纵向主动防撞系统的研究.[D]清华大学,2004.
[30]柴毅.智能化汽车行驶主动安全系统研究.[D]重庆大学,2001.
[31]陈勇.不良气候条件下道路交通安全事故预防系统研究.[D]重庆大学,2007.
[32]喻凡.林逸.汽车系统动力学[M].北京:机械工业出版社,2005.
[33]王德丰.陈玉润.汽车运用学[M] .北京-中国林业出版社,1992.
[34]应世杰.高速公路汽车防撞预警系统的开发研究.[D]长安大学,2004.
[35]吴光强.汽车理论[M] .北京:人民交通出版社,2007.
[36] Wallentowitz H.Longitudinal dynamics of vehicles. Lecture Notes, Institut Fur kraftfahrwesen Aachen, 2001.
[37]Willie D Jones. Keeping cars from crashing.IEEE Spectrum. September 2001: 40~45
[38]侯德藻.新型汽车主动防撞安全距离模型[J] .汽车工程,2005,(27):780-785.
[39]党宏社..韩崇超.段战胜.汽车防撞报警与制动距离的确定[J].长安大学学报,2002(22):89-91.
[40]马骏.高速公路行车安全距离的分析与研究[J].西安公路交通大学学报,1998,18(4):90-94.
[41]Peter Seder, Bongsob Song, J Karl Hedrick.Developmentof a collision avoidance system[C].SAE paper,1998.
[42]唐阳山.江振伟.白艳等.汽车驾驶智能型防碰撞系统[J].重庆大学学报,2008(10):324-326.
[43]柴毅.黄席樾.周欣等.汽车防碰撞安全距离模型及仿真研究[J].辽宁工业大学学报,2001(24):92~94.
[44]岳昕.高速公路汽车追尾事故智能预警系统.[D] .东北林业大学,2004.
[45]李宇.基于路面辩识的行车安全距离控制研究.[D] .长安大学,2006.
[46]刘哲义.对影响轮胎与路面间附着性能因素的分析[J].公路,2000,6(6):48-51.
[47]刘长生.汽车轮胎与公路路面附着系数的研究[J].公路,2006,5(5):159-163.
[48]余卓平.左建令.张立军.路面附着系数估算技术发展现状综述[J].汽车工程,2006,28(6):546-549
[49]Muller Steffen, UchanskiMichae, l HedrickKar.l Estimation of the Maximum Tire-Road Friction Coefficient [J]. Journal of Dynamic Systems, Measurement, and Control ,2003, 125: 607-617.
[50]EichhornU, Roth J. Prediction and Monitoring of Tyre/Road Friction[C]. XXIV FISITA Congress, London, GB, 1992.
[51]BreuerB, EichhornU. Measurement of Tyre/Road Friction Ahead of the Car and Inside the Tyre[C]. Proceedings of AVEC 1992:347-353.
[52]边明远.汽车防滑控制系统(ABS/ASR)道路识别技术及车身速度算法研究[D].北京:北京理工大学, 2003.
[53] Bachmann Th. The Importance of the Integration of Road, Tyre,and Vehicle Technologies[C]. FISITA XXth World Congress, Montréal, l Canada, 1995.
[54] Wang J, AgrawalP, AlexanderL. An Experimental Study with Al-Ternate Measurement Systems for Estimation of Tire-road Friction Coefficient[C]. Proceeding of the American Control Conference, Denver,Colorado, 2003: 4957-4962.
[55] Gustafson F. Estimation and Change Detection of Tire-road Friction Using the Wheel Slip[C] . IEEE 1996: 99-104.
[56] Gustafsson F. Monitoring Tire-Road Friction Using the Wheel Slip[C]. IEEE Control System, 1998: 42-49.
[57] Lee Chankyu, Hedrick Kar,l YiKyongsu. Real-Time Slip-based Estimation of Maximum Tire-road Friction Coefficient [J]. IEEE ASME Transactions on Mechatronics, 2004, 9(2).
[58] NakaoYukio, KawasakiHiroaki and Major Douglas J. Estimation of Friction Levels between Tire and Road[C]. SAE Paper 2002-01-1198.
[59]Gustafson F. Slip-based Tire-road Friction Estimation [J]. Automatic 1997, 33(6): 1087-1099.
[60] Wang Junmin, Lee Alexander, Rajesh Rajaman.I Friction Estimation on Highway Vehicles Using Longitudinal Measurements [J]. Journal of Dynamic Systems, Measurement, and Control,2004, 126(6): 265-275.
[61] Yi Kyongsu, Jeoung Taeyoung. Road Friction Estimation Using Wheel Speed Sensor[C]. 5TH World Congress on ITS, 1998.
[62] YiKyongsu, Hedrick Kar,l Lee Seong-Chu.L Estimation of Tire road Friction Using Observer Based Identifiers [J]. Vehicle Sys-tem Dynamics 1999, 31: 233-261.
[63] Yi Kyongsu, Jeoung Taeyoung. Observer Based Estimation of Tire-road Friction for Collision Warning Algorithm Adaptation [J]. JSME SeriesC, 1998, 41(1).
[64] DaiβA. Model Based Calculation of Friction Curves Between Tyre and Road Surface[C]. IEEE 1995: 291-295.
[65] St. GermannM, DaiβWürtenbergerA. Monitoring of the Friction Coefficient between Tire and Road Surface[C]. IEEE 1994:613-618.
[66] Liu Chia-Shang, PengHue,i Road Friction Coefficient Estimation for Vehicle Path Prediction [ J]. Vehicle System Dynamics,1996, 25(Suppl): 413-425.
[67]尹朝庆.尹皓.人工智能与专家系统. [M],北京:中国水利水电出版社,2002.
[68]蔡瑞英.李长河.人工智能[M] .武汉:武汉理工大学出版社,2002.
[69]闻新.周露等. MATLAB神经网络应用设计[M]北京:科学出版社,2000.
[70]从爽.MATLAB工具箱的神经网络理论与应用第2版[M]北京:中国科学技术大学出版社2003
[71]寇雪芹.师帅兵.BP人工神经网络在二传感器数据融合处理中的应用,计量技术, 2003 ( 2 ): 26~28
[72]王铁.张国忠.周淑文.基于竞争神经网络的ABS路面辨识[J].东北大学学报,2003,6(6),560~562.
[73]程洪.郑南宁.高振海等基于主元神经网络和K-均值的道路识别算法[J].西安交通大学学报,2003,8(8),812~815.
[74]王爱玲.叶明声.邓秋香.MATLABR2007图象处理技术与应用[M]北京:电子工业出版社,2008
[75]章毓晋.图像处理.[M] .北京:清华大学出版社,2002.
[76]A.MURAT TEKALP.Digital Video Processing[M].北京:清华大学出版社,2002
[77]Uwe Regensburg and Volker Graefe.Object Classification for Obstacle Avoidance [J].SPIEVol.1388 Mobile Robots V.1990:112-118
[78]Kenneth R.Castleman.Digital Image Processing [M].Prentice Hall 1996
[79] Alan Bovik. Handbook of image and video processing [M]. Beijing: Publishing House of ElectronicsIndustry, 2006.
[80]刘涛.智能型汽车行驶主动安全系统研究.[D]重庆大学,2003.
[81]柴毅.智能化汽车行驶主动安全系统研究.[D]重庆大学,2001.
[82]廖传棉峰.基于多线程模式的汽车智能辅助驾驶系统研究[D].重庆大学,2002.
[83]周欣,黄席樾,黎昱.基于单目视觉的高速公路车道保持与距离测量.中国图象图形学报,2003,Vol.8(A),No.5:590~595
[84]Zhou Xin. Road detection and reconstruction for highway application. Proc.of SPIE.2002.Vol.4875.No. .2:816-821
[85]Uwe Regensburger and Volker Graefe.Object Classification for Obstacle Avoidance [J].SPIEVol.1388 Mobile Robots V.1990:112-118
[86] Paul Harmon, Rex Maus, William Morrissey. Expert systems: New York: Wiley, c1988.
[87]雷振山.赵晨光.魏丽等.labview8.2基础教程[M]北京:中国铁道出版社,2007.
[88]刘君华.基于LabVIEW的虚拟仪器设计[M] .北京:电子工业出版社,2003.
[89]杨乐平.李海涛.赵勇等. LabVIEW高级程序设计[M] .北京:清华大学出版社,2004.
[90]National Instruments Corporation. LabVIEW基础2006
[91] NI Corporation. LabVIEW Measurements Manual. USA: NI Corporation,2000b.
[92] NI Corporation. LabVIEW User Manual.
[93]侯国平.王砷.叶齐鑫. LabVIEW7.1编程与虚拟仪器设计[M],北京:清华大学出版社,2005.
[94]吴权威.王绪益. Access2003中文版应用基础教程[M],北京:中国铁道出版社,2005
[95]成昊.张莉.李丽萍.新概念Access 2003教程[M],长春:中国铁道出版社,2008
[2] Alenak Hudoklin.Vojan Rozman. Safety analysis of the railway traffic systems, Reliability engineering and systems safety.1992, 37:7-13.
[3]赵俊.智能交通系统浅析.山西警官高等专科学校学报,2002,3(1):47~48
[4]李峰.智能交通系统在国外的发展趋势.国外公路,1999,19(1):1~5
[5]徐利民.智能交通系统发展及其战略思考.技术经济,2003,4:15~16
[6]张丰焰.现代智能交通系统及其发展趋势.交通科技,2002,1:11~12
[7]柴毅.黄席褪.周欣.汽车驾驶智能型防碰撞系统研究.重庆大学学报(自然科学版).2001.24(4)
[8]宋晓琳.冯广刚.杨济匡.汽车主动防撞系统的发展现状及趋势[J].汽车工程, 2008,30(4): 285～290.
[9]粟菊.智能交通系统(ITS)在国外[J].中国机电工业.2000.9:21~22.
[10]谢飞.未来智能汽车及智能汽车交通系统.汽车技术.
[11]杨东凯.吴今培.张其善.智能交通系统( ITS)的发展及其模型化研究.北京航空航天大学学报.2002,26(1):22~25
[12]韩赞东.陈强尉.昊斌贝.超声定位技术在汽车安全预警系统中的应用.测控技术.2002,21(8):10~14
[13]赵建顺.王玉泰.超声汽车防撞系统的研究济南大学学报.2002,15(3):216~218
[14]贺乐厅.基于毫米波雷达的车辆防撞技术与实验研究.[D] .东南大学,2003.
[15]蒋飞.汽车主动防撞雷达系统的研究.[D]武汉理工大学,2006.
[16]陈勇.黄席樾.杨尚罡.汽车防撞系统的研究与发展.[J]计算机仿真.2006,23(12):239~242
[17]程玉兰.红外现场诊断技术[M].北京:机械工业出版社,2002.
[18] GREHART G. Thermal image modeling[C]. Proceedings of SPI Eon Infrared Sensor s and Sensor Fusion, 1987, 782: 3- 9.
[19] STETS J. Synthetic IR scene generation[C] .SPIE on Infrared Systems and Components II, 1988, 890: 130- 146.
[20] MICHAEL R W. Infrared signature generation of airborne targets [C] . Proceedings of SPIE, Character inaction, Propagation, and Simulation of Sources and Backgrounds III, 1993, 1967: 114- 122.
[21] HONG H K, HAN S H, HONG G P, etal Simulation of reticleseekers using the generated thermal images [C] .IEEE Asia Pacific Conference on Circuit and System96, 1996, 1:183- 186.
[22] YU Wei jie,Peng Qun sheng,TU Hong midget al.An infrared image synthesis model based on infrared physics and heat transfer [J].International Journal of Infrared & Millimeter Waves,1998, 19(12): 1661- 1669.
[23] YU Wei jie,TU Hong ming ,WANG Zhang ye,et al.An infrared image synthesis model for high speed targets [J].International Journal of Infrared & Millimeter Waves,2002,23 (12): 1743 -1751.
[24] BALFOU L S.Determination of infrared contrast transmittance through an obscuring atmosphere [C] .Proceedings of SPIE,Character ization, Propagation, and Simulation of InfraredScenes,1990,1311: 146- 155.
[25] KOPEIKA N S.The role of the atmosphere in target acquisition: model verus experiment [C] .Proceedings of SPIE, Infrared Technology and Application XXIV, 1998, 3436:867- 878.
[26]赵玉洁,国外汽车自动防撞系统发展迅速. [J]自动化博览,1992,10:18
[27]李金英.国外汽车防撞装置. [J]国外技术:38~39
[28]SmulderSA·Modeling and Filtering of Freeway Traffic Flow,Transportation and Traffic Theory,1987;139-158.
[29]侯德藻.汽车纵向主动防撞系统的研究.[D]清华大学,2004.
[30]柴毅.智能化汽车行驶主动安全系统研究.[D]重庆大学,2001.
[31]陈勇.不良气候条件下道路交通安全事故预防系统研究.[D]重庆大学,2007.
[32]喻凡.林逸.汽车系统动力学[M].北京:机械工业出版社,2005.
[33]王德丰.陈玉润.汽车运用学[M] .北京-中国林业出版社,1992.
[34]应世杰.高速公路汽车防撞预警系统的开发研究.[D]长安大学,2004.
[35]吴光强.汽车理论[M] .北京:人民交通出版社,2007.
[36] Wallentowitz H.Longitudinal dynamics of vehicles. Lecture Notes, Institut Fur kraftfahrwesen Aachen, 2001.
[37]Willie D Jones. Keeping cars from crashing.IEEE Spectrum. September 2001: 40~45
[38]侯德藻.新型汽车主动防撞安全距离模型[J] .汽车工程,2005,(27):780-785.
[39]党宏社..韩崇超.段战胜.汽车防撞报警与制动距离的确定[J].长安大学学报,2002(22):89-91.
[40]马骏.高速公路行车安全距离的分析与研究[J].西安公路交通大学学报,1998,18(4):90-94.
[41]Peter Seder, Bongsob Song, J Karl Hedrick.Developmentof a collision avoidance system[C].SAE paper,1998.
[42]唐阳山.江振伟.白艳等.汽车驾驶智能型防碰撞系统[J].重庆大学学报,2008(10):324-326.
[43]柴毅.黄席樾.周欣等.汽车防碰撞安全距离模型及仿真研究[J].辽宁工业大学学报,2001(24):92~94.
[44]岳昕.高速公路汽车追尾事故智能预警系统.[D] .东北林业大学,2004.
[45]李宇.基于路面辩识的行车安全距离控制研究.[D] .长安大学,2006.
[46]刘哲义.对影响轮胎与路面间附着性能因素的分析[J].公路,2000,6(6):48-51.
[47]刘长生.汽车轮胎与公路路面附着系数的研究[J].公路,2006,5(5):159-163.
[48]余卓平.左建令.张立军.路面附着系数估算技术发展现状综述[J].汽车工程,2006,28(6):546-549
[49]Muller Steffen, UchanskiMichae, l HedrickKar.l Estimation of the Maximum Tire-Road Friction Coefficient [J]. Journal of Dynamic Systems, Measurement, and Control ,2003, 125: 607-617.
[50]EichhornU, Roth J. Prediction and Monitoring of Tyre/Road Friction[C]. XXIV FISITA Congress, London, GB, 1992.
[51]BreuerB, EichhornU. Measurement of Tyre/Road Friction Ahead of the Car and Inside the Tyre[C]. Proceedings of AVEC 1992:347-353.
[52]边明远.汽车防滑控制系统(ABS/ASR)道路识别技术及车身速度算法研究[D].北京:北京理工大学, 2003.
[53] Bachmann Th. The Importance of the Integration of Road, Tyre,and Vehicle Technologies[C]. FISITA XXth World Congress, Montréal, l Canada, 1995.
[54] Wang J, AgrawalP, AlexanderL. An Experimental Study with Al-Ternate Measurement Systems for Estimation of Tire-road Friction Coefficient[C]. Proceeding of the American Control Conference, Denver,Colorado, 2003: 4957-4962.
[55] Gustafson F. Estimation and Change Detection of Tire-road Friction Using the Wheel Slip[C] . IEEE 1996: 99-104.
[56] Gustafsson F. Monitoring Tire-Road Friction Using the Wheel Slip[C]. IEEE Control System, 1998: 42-49.
[57] Lee Chankyu, Hedrick Kar,l YiKyongsu. Real-Time Slip-based Estimation of Maximum Tire-road Friction Coefficient [J]. IEEE ASME Transactions on Mechatronics, 2004, 9(2).
[58] NakaoYukio, KawasakiHiroaki and Major Douglas J. Estimation of Friction Levels between Tire and Road[C]. SAE Paper 2002-01-1198.
[59]Gustafson F. Slip-based Tire-road Friction Estimation [J]. Automatic 1997, 33(6): 1087-1099.
[60] Wang Junmin, Lee Alexander, Rajesh Rajaman.I Friction Estimation on Highway Vehicles Using Longitudinal Measurements [J]. Journal of Dynamic Systems, Measurement, and Control,2004, 126(6): 265-275.
[61] Yi Kyongsu, Jeoung Taeyoung. Road Friction Estimation Using Wheel Speed Sensor[C]. 5TH World Congress on ITS, 1998.
[62] YiKyongsu, Hedrick Kar,l Lee Seong-Chu.L Estimation of Tire road Friction Using Observer Based Identifiers [J]. Vehicle Sys-tem Dynamics 1999, 31: 233-261.
[63] Yi Kyongsu, Jeoung Taeyoung. Observer Based Estimation of Tire-road Friction for Collision Warning Algorithm Adaptation [J]. JSME SeriesC, 1998, 41(1).
[64] DaiβA. Model Based Calculation of Friction Curves Between Tyre and Road Surface[C]. IEEE 1995: 291-295.
[65] St. GermannM, DaiβWürtenbergerA. Monitoring of the Friction Coefficient between Tire and Road Surface[C]. IEEE 1994:613-618.
[66] Liu Chia-Shang, PengHue,i Road Friction Coefficient Estimation for Vehicle Path Prediction [ J]. Vehicle System Dynamics,1996, 25(Suppl): 413-425.
[67]尹朝庆.尹皓.人工智能与专家系统. [M],北京:中国水利水电出版社,2002.
[68]蔡瑞英.李长河.人工智能[M] .武汉:武汉理工大学出版社,2002.
[69]闻新.周露等. MATLAB神经网络应用设计[M]北京:科学出版社,2000.
[70]从爽.MATLAB工具箱的神经网络理论与应用第2版[M]北京:中国科学技术大学出版社2003
[71]寇雪芹.师帅兵.BP人工神经网络在二传感器数据融合处理中的应用,计量技术, 2003 ( 2 ): 26~28
[72]王铁.张国忠.周淑文.基于竞争神经网络的ABS路面辨识[J].东北大学学报,2003,6(6),560~562.
[73]程洪.郑南宁.高振海等基于主元神经网络和K-均值的道路识别算法[J].西安交通大学学报,2003,8(8),812~815.
[74]王爱玲.叶明声.邓秋香.MATLABR2007图象处理技术与应用[M]北京:电子工业出版社,2008
[75]章毓晋.图像处理.[M] .北京:清华大学出版社,2002.
[76]A.MURAT TEKALP.Digital Video Processing[M].北京:清华大学出版社,2002
[77]Uwe Regensburg and Volker Graefe.Object Classification for Obstacle Avoidance [J].SPIEVol.1388 Mobile Robots V.1990:112-118
[78]Kenneth R.Castleman.Digital Image Processing [M].Prentice Hall 1996
[79] Alan Bovik. Handbook of image and video processing [M]. Beijing: Publishing House of ElectronicsIndustry, 2006.
[80]刘涛.智能型汽车行驶主动安全系统研究.[D]重庆大学,2003.
[81]柴毅.智能化汽车行驶主动安全系统研究.[D]重庆大学,2001.
[82]廖传棉峰.基于多线程模式的汽车智能辅助驾驶系统研究[D].重庆大学,2002.
[83]周欣,黄席樾,黎昱.基于单目视觉的高速公路车道保持与距离测量.中国图象图形学报,2003,Vol.8(A),No.5:590~595
[84]Zhou Xin. Road detection and reconstruction for highway application. Proc.of SPIE.2002.Vol.4875.No. .2:816-821
[85]Uwe Regensburger and Volker Graefe.Object Classification for Obstacle Avoidance [J].SPIEVol.1388 Mobile Robots V.1990:112-118
[86] Paul Harmon, Rex Maus, William Morrissey. Expert systems: New York: Wiley, c1988.
[87]雷振山.赵晨光.魏丽等.labview8.2基础教程[M]北京:中国铁道出版社,2007.
[88]刘君华.基于LabVIEW的虚拟仪器设计[M] .北京:电子工业出版社,2003.
[89]杨乐平.李海涛.赵勇等. LabVIEW高级程序设计[M] .北京:清华大学出版社,2004.
[90]National Instruments Corporation. LabVIEW基础2006
[91] NI Corporation. LabVIEW Measurements Manual. USA: NI Corporation,2000b.
[92] NI Corporation. LabVIEW User Manual.
[93]侯国平.王砷.叶齐鑫. LabVIEW7.1编程与虚拟仪器设计[M],北京:清华大学出版社,2005.
[94]吴权威.王绪益. Access2003中文版应用基础教程[M],北京:中国铁道出版社,2005
[95]成昊.张莉.李丽萍.新概念Access 2003教程[M],长春:中国铁道出版社,2008