Detection of Road Artefacts Using Fuzzy Adaptive Thresholding

In this paper the authors are proposing approximate method for road artefacts detection and their location by analyzing acceleration values recorded in the car during driving over the road fragment using the smartphone mounted in the car. The new method called F-THRESH has been introduced, which is adaptively adjusting threshold for road artefacts detection by the fuzzy system means, allowing for outlier detection in chaotic time streams. First, the road quality is being calculated, then the difference between the current data point and mean acceleration is calculated and those two values are used as the input for the fuzzy system, which is calculating threshold to classify data point as an outlier. The proposed method has been compared to the previously implemented method and has an accuracy over 94% with 1.3% of False Positive Rate for the same problem which makes it a great candidate to be implemented in the IoT Edge scenarios, for reducing amount of data being sent to the cloud analyzing system.

[1]  Hugo Jair Escalante,et al.  Learning Roadway Surface Disruption Patterns Using the Bag of Words Representation , 2017, IEEE Transactions on Intelligent Transportation Systems.

[2]  Gurdit Singh,et al.  Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing , 2017, Pervasive Mob. Comput..

[3]  Subrata Nandi,et al.  Crowdsourcing from the True crowd: Device, vehicle, road-surface and driving independent road profiling from smartphone sensors , 2020, Pervasive Mob. Comput..

[4]  Tomasz Cieplak,et al.  The Cloud Computing Stream Analysis System for Road Artefacts Detection , 2016, CN.

[5]  Yiik Diew Wong,et al.  Response-based methods to measure road surface irregularity: a state-of-the-art review , 2019, European Transport Research Review.

[6]  Éric Renault,et al.  Road Anomaly Detection Using Smartphone: A Brief Analysis , 2018, MSPN.

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

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

[9]  Ahmad Aljaafreh,et al.  Fuzzy Inference System for Speed Bumps Detection Using Smart Phone Accelerometer Sensor , 2017 .

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

[11]  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).

[12]  Adam Kiersztyn,et al.  Fuzzy Set-Based Isolation Forest , 2020, 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[13]  Robertas Damasevicius,et al.  Human Activity Recognition in AAL Environments Using Random Projections , 2016, Comput. Math. Methods Medicine.

[14]  C. A. Murthy,et al.  Fuzzy thresholding: mathematical framework, bound functions and weighted moving average technique , 1990, Pattern Recognit. Lett..

[15]  Jorge I. Galván-Tejada,et al.  Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach , 2018, Sensors.

[16]  Nguyen Van Khang,et al.  Cooperative Sensing and Analysis for a Smart Pothole Detection , 2019, 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC).

[17]  Lotfi A. Zadeh,et al.  Is there a need for fuzzy logic? , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[18]  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).

[19]  Ramachandran Ramjee,et al.  TrafficSense: Rich Monitoring of Road and Traffic Conditions us ing Mobile Smartphones , 2008 .

[20]  Hamid R. Tizhoosh,et al.  Image thresholding using type II fuzzy sets , 2005, Pattern Recognit..

[21]  Maria Skublewska-Paszkowska,et al.  A method for collision detection using mobile devices , 2016, 2016 9th International Conference on Human System Interactions (HSI).