E-way approach to sense the road condition using machine learning
Computer Engineer, Trinity College of Engineering, India
ABSTRACT
Smartphones are mainly functional to be adopted as a money-spinning and easy to execute tool for the measurement of road surface roughness condition, which is very essential for road monitoring and maintenance planning. In this study, an experiment has been carried out to collect data from accelerometers and gyroscopes on smartphones. The collected data is processed in the frequency domain to calculate magnitudes of the vibration. Road roughness condition that is modeled as a linear function of the vibration magnitudes, taking into account of both data from accelerometer and gyroscope as well as the average speed, achieves better estimation than the model that takes into account the magnitude from the accelerometer and the average speed alone. The finding is potentially significant for the development of a more accurate model and a better smartphone app to estimate road roughness condition from smartphone sensors.
