Real Time Road Condition Detection

Real-time detection of road conditions is an essential component of an intelligent transportation system, which allows for dynamic and effective management of traffic on the roads while ensuring proper safety services are provided. Through integration with other services, road conditions can be utilized in an effective manner while providing accident prevention services and dynamic congestion management services. Furthermore, integration can also ensure that data sharing is achieved among interested players in transportation, which will eventually help create effective response strategies as transportation systems become more intelligent with time and its integration with road condition detection systems becomes a necessity.   Precise and timely road condition information is crucial for the safe operation of autonomous and connected vehicles. Road surface anomalies directly influence vehicle control, braking, and path planning. The output of real-time detection systems is a key input to onboard decision-making algorithms, which increases situational awareness well beyond that achievable with onboard sensors. Shared knowledge of road conditions amongst the connected vehicle fleet enhances collective safety. As higher levels of automation are reached, the reliability of road condition detection will become increasingly important, making it one of the key enablers of autonomous mobility.   The application of real-time road condition detection system is beneficial in various ways and has considerable economic and social implications. Not only does it cut the long-run cost of repair, but it can also prolong the lifespan of the roads and other infrastructures. Furthermore, it can improve road safety, reducing the cost and enhancing the social benefits of the residents and the public. Other social benefits include reduced traffic congestion, lower vehicle maintenance costs, as well as providing equity in transport.   Future research directions in real-time road condition detection will be oriented toward achieving greater precision, scalability, and flexibility. Emerging trends such as improvements in sensor technology, ML, and edge computing will provide the momentum for future research directions. The challenges to be addressed in the future will be the handling of extreme conditions, generalization, and interpretability. This will be achieved through multidisciplinary research.   Therefore, real-time road condition detection is seen as a revolutionary innovation in transportation systems, enhancing road safety, efficiency, and resiliency. Among its many applications, this technology would be beneficial in providing critical knowledge related to road conditions. Recent and future studies in overcoming road condition challenges would be necessary to take this innovation to its next level. As road transportation systems become smarter and more connected, real-time road condition detection is expected to feature in redefining its future.

[1]  S. S. Yoon, K. Lee, and J. Kim, “Real-time road surface condition monitoring using vehicle-mounted sensors,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 10, pp. 3620–3631, 2019.   [2]   E. Eriksson, L. Girod, B. Hull, R. Newton, S. Madden, and H. Balakrishnan, “The pothole patrol: Using a mobile sensor network for road surface monitoring,” Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services, pp. 29–39, 2008.   [3]   R. Mednis, G. Strazdins, R. Zviedris, G. Kanonirs, and L. Selavo, “Real time pothole detection using Android smartphones with accelerometers,” International Conference on Distributed Computing in Sensor Systems, pp. 1–6, 2011.   [4]   J. Lin and S. Chen, “Road anomaly detection using smartphone-based accelerometer data,” Sensors, vol. 18, no. 3, pp. 1–19, 2018.   [5]   Y. Maeda, H. Sekimoto, T. Seto, T. Kashiyama, and H. Omata, “Road damage detection using deep neural networks with images captured through a smartphone,” Computer-Aided Civil and Infrastructure Engineering, vol. 33, no. 12, pp. 1127–1141, 2018.   [6]   A. Mohan, V. N. Padmanabhan, and R. Ramjee, “Nericell: Rich monitoring of road and traffic conditions using mobile smartphones,” Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, pp. 323–336, 2008.   [7]   H. Tsai, Y. Chiang, and C. Chu, “Road surface roughness detection based on vehicle vibration analysis,” Journal of Transportation Engineering, vol. 138, no. 8, pp. 1045–1052, 2012.   [8]   M. Douangphachanh and H. Oneyama, “Estimation of road roughness condition from smartphones under realistic settings,” Engineering Geology, vol. 181, pp. 82–92, 2014.   [9]   J. Fan, Y. Yu, and Y. Zhang, “Vision-based road crack detection using deep convolutional neural networks,” IEEE Access, vol. 7, pp. 105456–105465, 2019.   [10]   Z. Zhang, Q. Zou, Q. Li, and Q. Wang, “Deep learning-based road crack detection using adaptive image segmentation,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 10, pp. 3704–3716, 2019.