A Study on the Influence of Speed on Road Roughness Sensing: The SmartRoadSense Case †

SmartRoadSense is a crowdsensing project aimed at monitoring the conditions of the road surface. Using the sensors of a smartphone, SmartRoadSense monitors the vertical accelerations inside a vehicle traveling the road and extracts a roughness index conveying information about the road conditions. The roughness index and the smartphone GPS data are periodically sent to a central server where they are processed, associated with the specific road, and aggregated with data measured by other smartphones. This paper studies how the smartphone vertical accelerations and the roughness index are related to the vehicle speed. It is shown that the dependence can be locally approximated with a gamma (power) law. Extensive experimental results using data extracted from SmartRoadSense database confirm the gamma law relationship between the roughness index and the vehicle speed. The gamma law is then used for improving the SmartRoadSense data aggregation accounting for the effect of vehicle speed.

[1]  Hiroyuki Oneyama,et al.  A study on the use of smartphones under realistic settings to estimate road roughness condition , 2014, EURASIP J. Wirel. Commun. Netw..

[2]  Costas Papadimitriou,et al.  Design Optimization of Quarter-car Models with Passive and Semi-active Suspensions under Random Road Excitation , 2005 .

[3]  Tetsuro Butsuen The design of semi-active suspensions for automotive vehicles , 1989 .

[4]  Jo Yung Wong,et al.  Theory of ground vehicles , 1978 .

[5]  Ayman A. Aly,et al.  Fuzzy Control of a Quarter-Car Suspension System , 2009 .

[6]  James V. Krogmeier,et al.  A Recursive Multiscale Correlation-Averaging Algorithm for an Automated Distributed Road-Condition-Monitoring System , 2011, IEEE Transactions on Intelligent Transportation Systems.

[7]  Fausto Pedro García Márquez,et al.  Digital Filters And Signal Processing , 2014 .

[8]  Dave Cavalcanti,et al.  Framework of Data Acquisition and Integration for the Detection of Pavement Distress via Multiple Vehicles , 2017 .

[9]  Berend Jan van der Zwaag,et al.  RoADS: A Road Pavement Monitoring System for Anomaly Detection Using Smart Phones , 2015, MSM/MUSE/SenseML.

[10]  David C. Wong,et al.  The Burg algorithm for LPC speech analysis/Synthesis , 1980 .

[11]  Nirvana Meratnia,et al.  A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior , 2015, UbiComp/ISWC Adjunct.

[12]  Abhijit Mukherjee,et al.  Community Sensor Network for Monitoring Road Roughness Using Smartphones , 2017, J. Comput. Civ. Eng..

[13]  Hiroyuki Oneyama,et al.  Using Smartphones to Estimate Road Pavement Condition , 2014 .

[14]  Alessandro Bogliolo,et al.  Geospatial data aggregation and reduction in vehicular sensing applications: The case of road surface monitoring , 2014, 2014 International Conference on Connected Vehicles and Expo (ICCVE).

[15]  Jukka Riekki,et al.  Distributed Road Surface Condition Monitoring Using Mobile Phones , 2011, UIC.

[16]  J. L. Hock,et al.  An exact recursion for the composite nearest‐neighbor degeneracy for a 2×N lattice space , 1984 .

[17]  James Durbin,et al.  The fitting of time series models , 1960 .

[18]  Gary J. Balas,et al.  Road adaptive active suspension design using linear parameter-varying gain-scheduling , 2002, IEEE Trans. Control. Syst. Technol..

[19]  T D Gillespie,et al.  Fundamentals of Vehicle Dynamics , 1992 .

[20]  Igor Rychlik,et al.  Models for road surface roughness , 2012 .

[21]  M. Ndoye,et al.  Sensing and Signal Processing for a Distributed Pavement Monitoring System , 2006, 2006 IEEE 12th Digital Signal Processing Workshop & 4th IEEE Signal Processing Education Workshop.

[22]  Ryan Newton,et al.  The pothole patrol: using a mobile sensor network for road surface monitoring , 2008, MobiSys '08.

[23]  Purushottam Kulkarni,et al.  Wolverine: Traffic and road condition estimation using smartphone sensors , 2012, 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012).

[24]  Jie Wu,et al.  CRSM: a practical crowdsourcing-based road surface monitoring system , 2016, Wirel. Networks.

[25]  Alberto Carini,et al.  SmartRoadSense: Collaborative Road Surface Condition Monitoring , 2014 .

[26]  L. Selavo,et al.  Towards Vehicular Sensor Networks with Android Smartphones for Road Surface Monitoring , 2013 .

[27]  Alexander Schuller,et al.  Road Condition Measurement and Assessment: A Crowd Based Sensing Approach , 2016, ICIS.

[28]  Hiroyuki Oneyama,et al.  A Study on the Use of Smartphones for Road Roughness Condition Estimation , 2013 .

[29]  Alberto Carini,et al.  Sensing road roughness via mobile devices: A study on speed influence , 2015, 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA).

[30]  L. Shampine Vectorized adaptive quadrature in MATLAB , 2008 .

[31]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[32]  Astarita Vittorio,et al.  Automated Sensing System for Monitoring of Road Surface Quality by Mobile Devices , 2014 .

[33]  Tore Dahlberg OPTIMIZATION CRITERIA FOR VEHICLES TRAVELLING ON A RANDOMLY PROFILED ROAD - A SURVEY , 1979 .

[34]  B. R. Davis,et al.  OPTIMAL LINEAR ACTIVE SUSPENSIONS WITH INTEGRAL CONSTRAINT , 1988 .

[35]  Bishnu S. Atal,et al.  Predictive coding of speech signals and subjective error criteria , 1978, ICASSP.

[36]  Jie Wu,et al.  CRSM: Crowdsourcing Based Road Surface Monitoring , 2013, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.

[37]  Klas Bogsjö,et al.  Road profile statistics relevant for vehicle fatigue , 2007 .

[38]  Yu-chin Tai,et al.  Automatic Road Anomaly Detection Using Smart Mobile Device , 2010 .

[39]  Eugene J. O'Brien,et al.  The use of vehicle acceleration measurements to estimate road roughness , 2008 .

[40]  Xu Cang-su Study on Calculation of IRI Based on Power Spectral Density of Pavement Surface Roughness , 2007 .

[41]  N. Levinson The Wiener (Root Mean Square) Error Criterion in Filter Design and Prediction , 1946 .

[42]  Abhijit Mukherjee,et al.  Characterisation of road bumps using smartphones , 2016 .

[43]  Aboul Ella Hassanien,et al.  RoadMonitor: An Intelligent Road Surface Condition Monitoring System , 2014, IEEE Conf. on Intelligent Systems.

[44]  Girts Strazdins,et al.  Real time pothole detection using Android smartphones with accelerometers , 2011, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS).

[45]  John Laurent,et al.  Road surface inspection using laser scanners adapted for the high precision 3D measurements of large flat surfaces , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[46]  Chih-Wei Yi,et al.  Toward Crowdsourcing-Based Road Pavement Monitoring by Mobile Sensing Technologies , 2015, IEEE Transactions on Intelligent Transportation Systems.

[47]  Panos Y. Papalambros,et al.  Optimal Partitioning and Coordination Decisions in Decomposition-Based Design , 2007, DAC 2007.