[1]娄月新,陈圣迪,陆键,等.基于改进卡尔曼滤波算法的路面构造深度计算方法[J].东南大学学报(自然科学版),2020,50(1):129-136.[doi:10.3969/j.issn.1001-0505.2020.01.017] 　Lou Yuexin,Chen Shengdi,Lu Jian,et al.Calculation method for pavement macrotexture depth based on improved Kalman filter algorithm[J].Journal of Southeast University (Natural Science Edition),2020,50(1):129-136.[doi:10.3969/j.issn.1001-0505.2020.01.017] 点击复制 基于改进卡尔曼滤波算法的路面构造深度计算方法() 分享到： var jiathis_config = { data_track_clickback: true };

50

2020年第1期

129-136

2020-01-13

文章信息/Info

Title:
Calculation method for pavement macrotexture depth based on improved Kalman filter algorithm

1同济大学道路与交通工程教育部重点实验室, 上海 201804; 2上海海事大学交通运输学院, 上海 201306
Author(s):
1The Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China
2College of Transportation Engineering, Shanghai Maritime University, Shanghai 201306, China

Keywords:

U416.2
DOI:
10.3969/j.issn.1001-0505.2020.01.017

Abstract:
To improve the measurement accuracy of pavement macrotexture depth, an improved Kalman filter algorithm was proposed to calculate pavement macrotexture depth. First, the outliers of road elevation values obtained by a high precision laser range sensor were screened and interpolated by a statistical test method. Secondly, the corrected data were filtered by an improved Kalman filter algorithm, and the mean profile depth model was established to calculate the pavement profile depth. Then, the AC-13 asphalt concrete pavement and the SMA-13 asphalt concrete pavement were selected as test samples to compare the improved Kalman filter algorithm with the sliding filter algorithm and the sand patch method, and the macrotexture depth conversion models of the two pavement types were established. The results show that for the two pavement types of AC-13 and SMA-13, the mean square errors of the improved Kalman filter algorithm are 0.001 3 and 0.002 0 and the average absolute percentage errors are 2.92% and 3.85%. The correlation index with the sand patch method is more than 0.95, and both the repeatability standard deviation and the variation coefficient are less than 5%. The method has higher measurement accuracy and better stability. Thus, it can accurately measure and calculate pavement macrotexture depth.

参考文献/References:

[1] 张金喜,刘利花,王利利.速度和构造深度对水泥混凝土路面摩擦系数的影响[J].北京工业大学学报,2009,35(1):48-52.
Zhang J X, Liu L H, Wang L L. Influence of speed and texture depth on skid resistance of pavement surface[J]. Journal of Beijing University of Technology, 2009, 35(1): 48-52.(in Chinese)
[2] 窦光武.基于断面高程的路面构造深度计算模型研究[J].公路交通科技,2015,32(1):50-56. DOI:10.3969/j.issn.1002-0268.2015.01.009.
Dou G W. Research of calculation model of pavement texture depth based on profile elevation[J]. Journal of Highway and Transportation Research and Development, 2015, 32(1): 50-56. DOI:10.3969/j.issn.1002-0268.2015.01.009. (in Chinese)
[3] 蒋难得.基于激光视觉的沥青路面构造深度测量方法研究[D].武汉:武汉科技大学,2014.
Jiang N D. Asphalt pavement mean texture depth measurement method research based on laser vision[D]. Wuhan: Wuhan University of Science and Technology, 2014.(in Chinese)
[4] 中华人民共和国交通运输部.JTG E60—2008公路路基路面现场测试规程[S].北京:人民交通出版社,2008.
[5] American Society for Testing Materials. E1845-15 Standard practice for calculating pavement macrotexture mean profile depth[S]. West Conshohocken: ASTM International, 2005. DOI:10.1520/e1845-01r05e01.
[6] Australian Road Research Board. Austroads Test Method AG: AM/T013 Pavement surface texture measurement with a laser profilometer[S]. Melbourne: Australian Road Research Board, 2011.
[7] 中华人民共和国交通运输部.JTG/T E61—2014公路路面技术状况自动化检测规程[S].北京:人民交通出版社,2014.
[8] 马荣贵,王建锋,李平.沥青路面构造深度精确检测方法研究[J].科学技术与工程,2014,14(8):265-268. DOI:10.3969/j.issn.1671-1815.2014.08.052.
Ma R G, Wang J F, Li P. Research on high precision measurement of pavement texture depth[J]. Science Technology and Engineering, 2014, 14(8): 265-268. DOI:10.3969/j.issn.1671-1815.2014.08.052. (in Chinese)
[9] Sengoz B, Topal A, Tanyel S. Comparison of pavement surface texture determination by sand patch test and 3D laser scanning[J]. Periodica Polytechnica Civil Engineering, 2012, 56(1): 73. DOI:10.3311/pp.ci.2012-1.08.
[10] Prowell B, Hanson D. Evaluation of circular texture meter for measuring surface texture of pavements[J]. Transportation Research Record: Journal of the Transportation Research Board, 2005, 1929: 88-96. DOI:10.3141/1929-11.
[11] Vaiana R, Capiluppi G F, Gallelli V, et al. Pavement surface performances evolution: An experimental application[J]. Procedia - Social and Behavioral Sciences, 2012, 53: 1149-1160. DOI:10.1016/j.sbspro.2012.09.964.
[12] Hong S J, Hyun T J, Kim H B, et al. A study on the measurement of texture depth of pavement using portable laser profiler[J].Journal of the Korean Society of Road Engineers, 2012, 14(6): 45-55. DOI:10.7855/ijhe.2012.14.6.045.
[13] Xiao Y, van de Ven M F C, Molenaar A, et al. Surface texture of antiskid surface layers used on runways[C]// Transportation Research Board 90th Annual Meeting. Washington DC, United States, 2011: 1169.
[14] Praticò F G, Vaiana R. A study on the relationship between mean texture depth and mean profile depth of asphalt pavements[J]. Construction and Building Materials, 2015, 101: 72-79. DOI:10.1016/j.conbuildmat.2015.10.021.
[15] American Society for Testing Materials. E178-08 Standard practice for dealing with outlying observations[S]. West Conshohocken: ASTM International, 2008.
[16] 全国交通工程设施(公路)标准化技术委员会.GB/T 26764—2011多功能路况快速检测设备[S].北京:中国标准出版社,2011.
[17] Kalman R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering, 1960, 82(1): 35. DOI:10.1115/1.3662552.
[18] Chen B D, Liu X, Zhao H Q, et al. Maximum correntropy Kalman filter[J]. Automatica, 2017, 76: 70-77. DOI:10.1016/j.automatica.2016.10.004.
[19] 杨兆升,朱中.基于卡尔曼滤波理论的交通流量实时预测模型[J].中国公路学报,1999,12(3):63-67. DOI:10.19721/j.cnki.1001-7372.1999.03.009.
Yang Z S, Zhu Z. A real time traffic volume prediction model based on the Kalman filtering theory[J]. China Journal of Highway and Transport, 1999, 12(3): 63-67. DOI:10.19721/j.cnki.1001-7372.1999.03.009. (in Chinese)
[20] 王振,白星振,马梦白,等.一种基于数据预处理和卡尔曼滤波的温室监测数据融合算法[J].传感技术学报,2017,30(10):1525-1530. DOI:10.3969/j.issn.1004-1699.2017.10.012.
Wang Z,Bai X Z, Ma M B, et al. A data fusion algorithm on data preprocessing and Kalman filter for greenhouse environment monitor[J]. Chinese Journal of Sensors and Actuators, 2017, 30(10): 1525-1530. DOI:10.3969/j.issn.1004-1699.2017.10.012. (in Chinese)
[21] Khodaparast J, Khederzadeh M. Least square and Kalman based methods for dynamic phasor estimation: A review[J]. Protection and Control of Modern Power Systems, 2017, 2: 1-18. DOI: 10.1186/s41601-016-0032-y.
[22] 张敏,李凯,韩焱,等.基于卡尔曼滤波的陀螺仪降噪处理[J].传感技术学报,2018,31(2):223-227.
Zhang M, Li K, Han Y, et al. The noise reduction of gyroscope based on Kalman filter[J]. Chinese Journal of Sensors and Actuators, 2018, 31(2): 223-227.(in Chinese)
[23] American Society for Testing Materials. E965-15 Standard test method for measuring pavement macrotexture depth using a volumetric technique[S]. West Conshohocken: ASTM International, 2006. DOI: 10.1520/E0965-96R06.
[24] Li P F,Souleyrette R R. A generic approach to estimate freeway traffic time using vehicle ID-matching technologies[J]. Computer-Aided Civil and Infrastructure Engineering, 2016, 31(5): 351-365. DOI:10.1111/mice.12159.
[25] Plati C, Pomoni M, Stergiou T. Development of a mean profile depth to mean texture depth shift factor for asphalt pavements[J]. Transportation Research Record: Journal of the Transportation Research Board, 2017, 2641(1): 156-163. DOI:10.3141/2641-18.
[26] Fisco N R. Comparison of macrotexture measurement methods[D]. Columbus: Ohio State University, 2009.

相似文献/References:

[1]房建成,王庆,吴秋平,等.改进的车载DR系统自适应扩展卡尔曼滤波模型及仿真研究[J].东南大学学报(自然科学版),1999,29(1):35.[doi:10.3969/j.issn.1001-0505.1999.01.007]
Fang Jiancheng,Wang Qing,Wu Qiuping,et al.A New Modified Adaptive Extended Kalman Filter of DR System for Land Vehicle Navigation[J].Journal of Southeast University (Natural Science Edition),1999,29(1):35.[doi:10.3969/j.issn.1001-0505.1999.01.007]
[2]祝燕华,蔡体菁,刘莹.IMU/计程仪/重力组合导航系统信息融合方法[J].东南大学学报(自然科学版),2009,39(6):1146.[doi:10.3969/j.issn.1001-0505.2009.06.012]
Zhu Yanhua,Cai Tijing,Liu Ying.Information fusion method of IMU/log/ gravity integrated navigation system[J].Journal of Southeast University (Natural Science Edition),2009,39(1):1146.[doi:10.3969/j.issn.1001-0505.2009.06.012]
[3]房建成,万德钧,吴秋平.GPS动态定位的强跟踪卡尔曼滤波研究[J].东南大学学报(自然科学版),1997,27(2):60.[doi:10.3969/j.issn.1001-0505.1997.02.011]
Fang Jiancheng,Wan Dejun,Wu Qiuping.Modified Strong Tracking Kalman Filter and Its Application in GPS Kinematic Positioning for Moving Vehicles[J].Journal of Southeast University (Natural Science Edition),1997,27(1):60.[doi:10.3969/j.issn.1001-0505.1997.02.011]
[4]房建成,万德钧.捷联惯导系统的静基座快速初始对准方法[J].东南大学学报(自然科学版),1996,26(2):115.[doi:10.3969/j.issn.1001-0505.1996.02.019]
Fang Jiancheng,Wan Dejun.A Fast Initial Alignment Method for Strapdown Inertial Navigation System on Stationary Base[J].Journal of Southeast University (Natural Science Edition),1996,26(1):115.[doi:10.3969/j.issn.1001-0505.1996.02.019]
[5]董春娇,邵春福,周雪梅,等.基于交通流参数相关的阻塞流短时预测卡尔曼滤波算法[J].东南大学学报(自然科学版),2014,44(2):413.[doi:10.3969/j.issn.1001-0505.2014.02.033]
Dong Chunjiao,Shao Chunfu,Zhou Xuemei,et al.Kalman filter algorithm for short-term jam traffic prediction based on traffic parameter correlation[J].Journal of Southeast University (Natural Science Edition),2014,44(1):413.[doi:10.3969/j.issn.1001-0505.2014.02.033]