Cloud-Assisted Real-Time Road Condition Monitoring System for Vehicles

Abstract

Road infrastructure is the life line of the transportation industry and it should be monitored at regular intervals to ensure that it provides a smooth riding experience, safety to the passenger and causes less damage to the vehicles. Road conditions are affected by several factors such as weather conditions, accidents that have occurred, and regular wear and tear, hence it is difficult to monitor them in real-time. Previous research works on road monitoring systems can be broadly classified into three groups; the first group uses sensor data to detect road conditions based on a given threshold, the second employs a machine learning algorithm to acquire sensor data from the vehicle, while the third uses machine learning at the server then transmits the result back to the vehicle. The learning algorithms of groups two and three provide better results than group one. Group three yields the more accurate results, but at the cost of time. Therefore, in this paper, we propose a novel system that monitors road conditions in realtime by learning from the data obtained from built-in sensors of a smartphone that is mounted inside the vehicle. We have designed a lightweight learning algorithm that improves accuracy by interacting with the server and monitoring road conditions in real-time. The algorithm is based on k-means clustering and our experimental results show that it can classify road conditions based on the accelerometer data with 88.67% accuracy.