Intelligent road surface quality evaluation using rough mereology

INTELLIGENT ROAD SURFACE QUALITY

EVALUATION USING

ROUGH MEREOLOGY

Mohamed Mostafa Fouad

Assistant Professor at Arab Academy for Science, Technology, and Maritime TransportMember of SRGE Research Group.

Postdoctoral Fellow at VSB-Technical University of Ostrava, Ostrava, Czech Republic

Scientific Research Group in Egyptwww.egyptscience.net

Agenda

Introduction and problem domain

Aim of research

System Architecture

Data Acquisition Phase

Pre-processing Phase

Rough Mereologoy Phase

Experimental Results

Discussion

Research Obstacles

Future Work

Introduction

Smart applications nowadays are utilized to address

many common-day problems to find a convenient

solution affordable by the common citizen.

Road surface condition is an important matter in many

countries that suffer from bad road conditions.

Existence of potholes and road bumps with bad

design (homemade) can cause accidents and vehicle

damage over time

Aim of research

Provide an easy way to offer a smart distributed analysis of the road

by using a mobile application, that sends alarm for road users

before hitting road bumps or pot holes

The application measures the changes in the gravity orientation

through a gyroscope and the shifts in the accelerometer’s

indications, both as an assessment for the existence of speed

bumps.

Give the government an easy way to prioritize the process of fixing

the road conditions

Decrease the rate of accidents for vehicles’ drivers

System architecture

The architecture description:

The data acquisition phase starts by a mobile application

attached to a vehicle to detect the presence of road anomalies.

Data acquisition phase collects triple sensors values; the

Accelerometer, the Gyroscope, and the GPS. The main

intention of using the gyroscope, which represents variation

around gravity rotation, is to confirm the acceleration readings

for indicating road anomalies.

Rough mereology phase is used to rank the collected data in

order to make a useful recommendation to road user.

Data Acquisition Phase

Pre-processing Phase

The data was collected as tuple form < Sensor Type, X-coordinate, Y-coordinate, Z-coordinate; time(in millisecond) >

The gyroscope readings have been converted from radians form into

degrees form in order to enhance the scatter point curve.

Gyroscope gravity readings around X-axis

Rough Mereology Phase

The role of the rough mereology

phase is to rank the modified data in

order to make a useful

recommendation.

The returned result of this phase is

a similarity matrix of items.

EXPERIMENTAL RESULTS

Evaluation criteria is mainly based on the computation of both the recall

and precision statistical equations.

Also with a statistical precision measurement method adopts the MAE

(Mean Absolute Error) in order to measure the recommendation quality.

EXPERIMENTAL RESULTS

Through the manual annotation of bump places we recognized that it is

always lies in the gyroscope readings in [10, 30] degrees.

According to classification rates obtained in Table

1, the precision of rough mereology in speed

bumps classification reached to 0.754, while the

recall statistical evaluation reached 0.165.

Therefore the rough mereology as a classification

algorithm provides total accuracy equals 75% with

MAE= 8.36%.

Discussion

Classification based on rough mereology does not provide promising

results as the work of [Astarita Vittorio et al. ”Automated Sensing System for

Monitoring of Road Surface Quality by Mobile Devices”], but on the other hand, it

still produces better results than the one obtained in [Mikko Perttunen et al.

, ”Distributed road surface condition monitoring using mobile phones”].

Research Obstacles Sensors readings differ from different mobile vendors (Nokia Lumia,

Nexus tablets, Samsung mobiles, …)

High error detection rate as many roads are already in a bad

structure.

Alerts of a coming speed bump will based on vehicle speed.

Readings will differ upon various vehicle models according to their

suspensions systems.

Mobile based application will suffer from high rate power

Consumption (sensing, and data transmission)

Privacy

Future Work

Try other machine learning algorithms in speed bump’s

detection process.

Support different mobile platforms.

For further questions:

Mohamed Mostafa Fouad

Email: [email protected]