MAARGHA: A Prototype System for Road Condition and Surface Type Estimation by Fusing Multi-Sensor Data

Deepak Rajamohan,Krishnan Rajan,Bhavana Gannu

doi:10.3390/ijgi4031225

Abstract

Road infrastructure in countries like India is expanding at a rapid pace and is becoming increasingly difficult for authorities to identify and fix the bad roads in time. Current Geographical Information Systems (GIS) lack information about on-road features like road surface type, speed breakers and dynamic attribute data like the road quality. Hence there is a need to build road monitoring systems capable of collecting such information periodically. Limitations of satellite imagery with respect to the resolution and availability, makes road monitoring primarily an on-field activity. Monitoring is currently performed using special vehicles that are fitted with expensive laser scanners and need skilled resource besides providing only very low coverage. Hence such systems are not suitable for continuous road monitoring. Cheaper alternative systems using sensors like accelerometer and GPS (Global Positioning System) exists but they are not equipped to achieve higher information levels. This paper presents a prototype system MAARGHA (MAARGHA in Sanskrit language means an eternal path to solution), which demonstrates that it can overcome the disadvantages of the existing systems by fusing multi-sensory data like camera image, accelerometer data and GPS trajectory at an information level, apart from providing additional road information like road surface type. MAARGHA has been tested across different road conditions and sensor data characteristics to assess its potential applications in real world scenarios. The developed system achieves higher information levels when compared to state of the art road condition estimation systems like Roadroid. The system performance in road surface type classification is dependent on the local environmental conditions at the time of imaging. In our study, the road surface type classification accuracy reached 100% for datasets with near ideal environmental conditions and dropped down to 60% for datasets with shadows and obstacles.

Highlights

Connecting cities and hinterlands, roads infrastructure is the artery of a country’s economy
The training set was generated in a nearby locality at Hyderabad, India by driving through different types of roads and manually classifying the road surface type and road condition
The classification mode of the application takes in the sensor data and performs a K-Nearest Neighbor (K-NN) classification for both road condition and the road surface type data

Summary

Introduction

Connecting cities and hinterlands, roads infrastructure is the artery of a country’s economy. It enables cheap and quick transportation of people, services and goods inland. Of late, there has been increased focus and budget allotted for the construction and maintenance of highways. The local urban and rural roads have been neglected due to insufficient funds, poor planning and lack of coordination between municipal authorities and contractors. Under-maintained roads deteriorate over time and become unusable and go beyond repair. In India, the better funded national highways account only for 1.9% of the entire road network, it has the world’s second longest, length of roads

Methods

Results

Conclusion