Project 3.4 (2020)
Background
Pavement deterioration such as potholes, cracks and reduced surface friction can damage vehicles and increase safety risks. Information about pavement conditions could help road users to avoid damaged roads and keep the relevant agencies informed so that remedy actions can be taken in time. However, conventional methods for pavement condition monitoring and assessment, especially for large road networks, are costly and time-consuming. Recent development in sensor hardware and data analytics algorithms, as well as the prevailing use of smartphones, provides new opportunities for collecting asset data in an accurate, timely, and cost-effective way. In addition, sensor systems aboard modern vehicles (e.g., ABS, suspension travel detectors, cameras) have the potential to provide additional data that can be used to assess pavement conditions.
Project Objectives
1. To undertake a thorough review of existing techniques and practices in road surface condition monitoring
2. To establish a crowdsourcing framework for effective monitoring of road surface anomalies
3. To develop robust algorithms and prototype smartphone applications for road surface anomaly detection, assessment, and reporting
4. To test and validate the proposed approaches in controlled and open road environments
- Dr Yihai Fang (LCI - Monash University)
- Professor Mark Wallace (Monash University)
