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

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 OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 1226 reached 100% for datasets with near ideal environmental conditions and dropped down to 60% for datasets with shadows and obstacles.

Accuracy enhancement of roadway anomaly localization using connected vehicles

“…Methods that directly analyse the accelerometer signal stream from individual vehicles used shorttime spectral transforms to identify the signatures of anomalies (Ayenu-Prah and Attoh-Okine 2009). Other methods include attempting to recover the road profile by double integration of the accelerometer signal (Islam et al 2014, Nomura andShiraishi 2015), and signal classification using machine learning methods (Rajamohan et al 2015).…”

Dav – Data Analytics and Visualization System for Roads

“…The data fusion approach as mentioned in Maargha (Rajamohan et al, 2015) and (Gannu, Rajan, 2018) was a proof of concept developed to address the classification of roads by multi-sensor fusion uniquely and simply. Most of the systems mentioned above use a single source analysis to arrive at the result.…”

“…The classification of the data is performed using the algorithm reported in (Rajamohan et al, 2015). To summarize the process, tar roads vs. mud/concrete roads based on the intensity distribution of the scene and the mud roads are differentiated from concrete roads depending on the colorfulness of the image.…”

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

“…Other smartphone-based implementations have used 1) GNSS measurements of speed to attempt to remove the speed dependency in the accelerometer features describing a given anomaly [288]; 2) microphones to record pothole-induced sound signals [289]; and 3) OBD suspension sensors to measure potholeinduced compressions [255]. Although cameras have been popular in the general field of pothole detection [290], [291], camera-based approaches are often considered too computationally expensive for smartphone-based implementations [292]. Moving on to the road assessment, we note that there has been several proposals on how to increase the granularity of detection algorithms that just aim to identify road anomalies in general.…”