基于手机传感器轨迹的路面地物检测方法

doi:10.11896/jsjkx.210200145

摘要/Abstract

摘要： 针对传统路面地物信息采集方法存在的数据采集周期长、成本高等问题,提出了一种基于手机传感器轨迹的城市路面地物检测方法。利用手机记录车辆行驶过程中各传感器数据的变化,分析经过姿态校正后的加速度数据,研究加速度变化与路况之间的联系,构建BP神经网络模型,并使用已采集数据对模型进行训练,以识别路面地物。实验结果表明,基于手机传感器轨迹的路面地物检测方法具有快速准确地检测路面地物信息的能力,且地物检测准确率大于85%,能够较为准确地检测路面地物,文中基于手机姿态传感器对手机加速度传感器姿态进行了实时矫正,利用手机垂直于路面的加速度变化检测路面地物,因此所提方法具有手机加速度传感器姿态无关性,此外,所提方法对硬件设备要求低、数据采集效率高,降低了路面地物信息采集的成本,具有广泛的应用前景。

关键词: BP神经网络, 车辆轨迹, 路面地物检测, 手机传感器

Abstract: Aimed at the problem of low efficiency and high cost in the traditional road surface object collection procedure,the method of road surface object recognition from mobile phone based sensor trajectories is proposed.Mobile phones are used to record the data changes of various sensors in the process of driving,and then the acceleration data after attitude correction are analyzed to find the relationship between the acceleration trend and the road condition.Finally,the constructe the BP neural network model,and use the acquired data to train the BP neural network model to recognize the road surface object and its position.Experiment results show that,the road surface object can be fast and accurately recognized by the mobile phone based senor trajectories,and the accuracy can be higher than 85%.In this paper,attitude of mobile acceleration sensor has carried on the real time correction.Because the acceleration changing of the Mobile phones is perpendicular to the road,we use the acceleration change to detecte the road feature.The method has nothing to do with a mobile phone accelerometer gesture,in addition,hardware requirements of the method are low,theefficiency of data acquisition is high,which reduce the cost of the road surface features information acquisition.

Key words: BP neural network, Mobile phone sensors, Road surface object recognition, Vehicle trajectories

中图分类号:

P228

焦东来, 王浩翔, 吕海洋, 徐轲. 基于手机传感器轨迹的路面地物检测方法[J]. 计算机科学, 2021, 48(11A): 283-289. https://doi.org/10.11896/jsjkx.210200145

JIAO Dong-lai, WANG Hao-xiang, LYU Hai-yang, XU Ke. Road Surface Object Detection from Mobile Phone Based Sensor Trajectories[J]. Computer Science, 2021, 48(11A): 283-289. https://doi.org/10.11896/jsjkx.210200145

参考文献

[1]GIACOMIN J,WOO Y.A study of the human ability to detect road surface type on the basis of steering wheel vibration feedback[J].Proceedings of TheInstitution of Mechanical Engineers,Part D:Journal of Automobile Engineering,2005,219(11):1259-1270.
[2]SMITH K D,RAM P.Measuring and Specifying PavementSmoothness:[techbrief][R].United States.Federal Highway Administration,2016.
[3]LAN Q X.Research on Extraction of Potholes Features Based on Digital Image Recognition[D].Chongqing:Chongqing Jiaotong University,2015.
[4]YANG C.Perceiving Metropolitan-Scale Pothole Profiles with Crowdsourcing[D].Shanghai:Shanghai Jiao Tong University,2015.
[5]YANG L,LIU R F,LU X S,et al.An automatic extraction method for road surface pothole in vehicle-borne laser point cloud[J].Surveying and Mapping Engineering,2020,1(6):66-71.
[6]HUI B,GUO M,WANG Z,et al.Multi-dimensional index detection of pavement potholes based on 3D laser technology[J].Journal of Tongji University (Natural Science),2018,46(1):60-66.
[7]CHANG K T,CHANG J R,LIU J K.Detection of pavementdistresses using 3D laser scanning technology[M]//Computing in Civil Engineering.2005:1-11.
[8]HUSTON D,PELCZARSKI N,ESSER B,et al.Damage detection in roadways with ground penetrating radar[C]//Eighth International Conference on Ground Penetrating Radar.International Society for Optics and Photonics,2000:91-94.
[9]ZHANG Z,XIAO A,CHAN C K,et al.An efficient algorithm for pothole detection using stereo vision[C]//2014 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).IEEE,2014:564-568.
[10]YU X,SALARI E.Pavement pothole detection and severitymeasurement using laser imaging[C] //2011 IEEE International Conference on Electro/Information Technology.IEEE,2011:1-5.
[11]LIU W,FWA T F,ZHAO Z.Wavelet analysis and interpretation of road roughness[J].Journal of Transportation Engineering,2005,131(2):120-130.
[12]KANG B,CHOI S.Pothole Detection System using 2D LiDAR and Camera [C]//2017 Ninth International Conference on Ubiquitous and Future Networks.IEEE,2017:744-746.
[13]MERT Z,CHRISTOP H.Continuous road damage detection using regular service vehicles[C]//Proceedings of the ITS world congress.2011:5-8.
[14]MEDNIS A,STRAZDINS G,LIEPINS M,et al.RoadMic:Road surface monitoring using vehicular sensor networks with microphones[C]//International Conference on Networked Digital Technologies.Berlin:Springer,2010:417-429.
[15]ERIKSSON J,GIROD L,HULL B,et al.The pothole patrol:using a mobile sensor network for road surface monitoring[C]//Proceedings of the 6th International Conference on Mobile Systems,Applications,and Services.2008:29-39.
[16]KOCH C,BRILAKIS I.Pothole detection in asphalt pavement images[J].Advanced Engineering Informatics,2011,25(3):507-515.
[17]KARUPPUSWAMY J.Detection and avoidance of simulatedpotholes in autonomous vehicle navigation in an unstructured environment[C]//Intelligent Robots and Computer Vision XIX:Algorithms,Techniques,and Active Vision.International Society for Optics and Photonics,2000:70-80.
[18]LIN J,LIU Y.Potholes detection based on SVM in the pave-ment distress image[C]//2010 Ninth International Symposium on Distributed Computing and Applications to Business,Engineering and Science.IEEE,2010:544-547.
[19]NG J R,WONG J S,GOH V T,et al.Identification of Road Surface Conditions using IoT Sensors and Machine Learning[M]//Computational Science and Technology.Singapore:Springer,2019:259-268.
[20]MEDNIS A,STRAZDINS G,ZVIEDRIS R,et al.Real time pothole detection using android smartphones with accelerometers[C]//2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS).IEEE,2011:1-6.
[21]PERTTUNEN M,MAZHELIS O,CONG F,et al.Distributedroad surface condition monitoring using mobile phones[C]//International Conference on Ubiquitous Intelligence and Computing.Berlin:Springer,2011:64-78.
[22]MOHAN P,PADMANABHAN V N,RAMJEE R.Nericell:rich monitoring of road and traffic conditions using mobile smartphones[C]//Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems.2008:323-336.
[23]KULKARNI A,MHALGI N,GURNANI S,et al.Pothole detection system using machine learning on Android[J].International Journal of Emerging Technology and Advanced Engineering,2014,4(7):360-364.
[24]TAI Y C,CHAN C W,JANE Y J.Automatic road anomaly detection using smart mobile device[C]//Conference on Technologies and Applications of Artificial Intelligence.Hsinchu,China:2010.
[25]ZANG K,SHEN J,HUANG H S,et al.Assessing and mapping of road surface roughness based on GPS and accelerometer sensors on bicycle-mounted smartphones[J].Sensors,2018,18(3):914.
[26]DU R,QIU G,GAO K,et al.Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor[J].Sensors,2020,20(2),451.

相关文章 15

[1]	刘宝宝, 杨菁菁, 陶露, 王贺应. 基于DE-LSTM模型的教育统计数据预测研究 Study on Prediction of Educational Statistical Data Based on DE-LSTM Model 计算机科学, 2022, 49(6A): 261-266. https://doi.org/10.11896/jsjkx.220300120
[2]	徐佳楠, 张天瑞, 赵伟博, 贾泽轩. 面向供应链风险评估的改进BP小波神经网络研究 Study on Improved BP Wavelet Neural Network for Supply Chain Risk Assessment 计算机科学, 2022, 49(6A): 654-660. https://doi.org/10.11896/jsjkx.210800049
[3]	朱旭辉, 沈国娇, 夏平凡, 倪志伟. 基于螺旋进化萤火虫算法和BP神经网络的模型及其在PPP融资风险预测中的应用 Model Based on Spirally Evolution Glowworm Swarm Optimization and Back Propagation Neural Network and Its Application in PPP Financing Risk Prediction 计算机科学, 2022, 49(6A): 667-674. https://doi.org/10.11896/jsjkx.210800088
[4]	夏静, 马中, 戴新发, 胡哲琨. 基于BP神经网络的智能云效能模型 Efficiency Model of Intelligent Cloud Based on BP Neural Network 计算机科学, 2022, 49(2): 353-367. https://doi.org/10.11896/jsjkx.201100140
[5]	郭福民, 张华, 胡瑢华, 宋岩. 一种基于表面肌电信号的腕部肌力估计方法研究 Study on Method for Estimating Wrist Muscle Force Based on Surface EMG Signals 计算机科学, 2021, 48(6A): 317-320. https://doi.org/10.11896/jsjkx.200600021
[6]	程铁军, 王曼. 基于变权组合的突发事件网络舆情趋势预测 Network Public Opinion Trend Prediction of Emergencies Based on Variable Weight Combination 计算机科学, 2021, 48(6A): 190-195. https://doi.org/10.11896/jsjkx.200600094
[7]	石琳姗, 马创, 杨云, 靳敏. 基于SSC-BP神经网络的异常检测算法 Anomaly Detection Algorithm Based on SSC-BP Neural Network 计算机科学, 2021, 48(12): 357-363. https://doi.org/10.11896/jsjkx.201000086
[8]	周俊, 尹悦, 夏斌. 基于LSTM神经网络的声发射信号识别研究 Acoustic Emission Signal Recognition Based on Long Short Time Memory Neural Network 计算机科学, 2021, 48(11A): 319-326. https://doi.org/10.11896/jsjkx.210700034
[9]	宋岩, 胡瑢华, 郭福民, 袁新亮, 熊睿洋. 基于sEMG的改进SVM+BP肌力预测分层算法 Improved SVM+BP Algorithm for Muscle Force Prediction Based on sEMG 计算机科学, 2020, 47(6A): 75-78. https://doi.org/10.11896/JsJkx.190900143
[10]	诸珺文. 基于改进BP神经网络的SQL注入识别 SQL InJection Recognition Based on Improved BP Neural Network 计算机科学, 2020, 47(6A): 352-359. https://doi.org/10.11896/JsJkx.191200054
[11]	周立鹏, 孟利民, 周磊, 蒋维, 董建平. 基于BP神经网络的摔倒检测算法 Fall Detection Algorithm Based on BP Neural Network 计算机科学, 2020, 47(6A): 242-246. https://doi.org/10.11896/JsJkx.191000077
[12]	陈燕文,李坤,韩焱,王燕平. 基于MFCC和常数Q变换的乐器音符识别 Musical Note Recognition of Musical Instruments Based on MFCC and Constant Q Transform 计算机科学, 2020, 47(3): 149-155. https://doi.org/10.11896/jsjkx.190100224
[13]	刘晓彤,王伟,李泽禹,沈思婉,姜小明. 基于改进BP神经网络的尿液中红白细胞识别算法 Recognition Algorithm of Red and White Cells in Urine Based on Improved BP Neural Network 计算机科学, 2020, 47(2): 102-105. https://doi.org/10.11896/jsjkx.191100195
[14]	马创, 周代棋, 张业. 基于改进鲸鱼算法的BP神经网络水资源需求预测方法 BP Neural Network Water Resource Demand Prediction Method Based on Improved Whale Algorithm 计算机科学, 2020, 47(11A): 486-490. https://doi.org/10.11896/jsjkx.191200047
[15]	张春祥, 赵春蕾, 陈超, 罗辉. 基于手机传感器的人体活动识别综述 Review of Human Activity Recognition Based on Mobile Phone Sensors 计算机科学, 2020, 47(10): 1-8. https://doi.org/10.11896/jsjkx.200400092

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed