With the emergence of vehicles outfitted with road-surface sensing capabilities and connected to the original equipment manufacturer (OEM), the idea of extensive pavement condition monitoring using existing vehicles to augment current standard methods (with specialized equipment) has presented an intriguing opportunity. The advent of smartphones, an analogous technology, has already resulted in work to provide lower-cost pavement evaluation. However, through the last decade there have been significant challenges, including repeatability, reliability, and accessibility. Harmonization of measurements from different sensors is necessary, both in terms of calibration of each instrument and estimates arising from multiple measurements. There is a need to identify potential sensor technologies and conduct benchmarking studies to investigate the possibility to augment standard survey data with OEM-connected sensor data. Because the data collection might not be a dedicated single-pass effort, there is a need to determine the requirements for a crowd-sourcing system that yields sufficient coverage of the highway network. This research will allow highway agencies to glean additional insights regarding maintenance and rehabilitation of their pavement assets.
The objective of this research is to identify available sensing technologies integrated within connected vehicles, their advantages and limitations, and the pertinent methodology for augmenting standard pavement condition survey data. This research would answer, as a minimum, the following questions: How do currently available OEM-connected technologies relate to standard technology for collecting each pavement condition measure/metric? How can we quantify the repeatability and reproducibility of each to compare across technologies? How can sensor data augment and enhance standard condition data and its application in pavement evaluation, design, maintenance management, and asset management? How can crowd-sourced data be integrated into pavement evaluation, maintenance, and operation?
To answer these questions, the research effort should include the following tasks or activities.
1. Conduct a detailed literature review of existing OEM sensing and crowd-sourcing technologies. Include a focus on data privacy and security issues since this is a known challenge.
2. Establish the current degree of utilization of this data in various management systems.
3. Identify and assess the principal components of the systems to conduct pavement evaluation: (a) sensor hardware and software producing the data and systems for on-board processing, (b) processing and analysis techniques to extract condition measures from the sensor data, (c) methods of data aggregation across calibrated and uncalibrated vehicles and fleets, (d) communication technologies (e.g., Internet of Things or IoT) for transmitting raw and processed data, and (e) data quality characterization and quality assurance, including correlation with reference measurements and repeatability and reproducibility of single-device and different-device measurements made on the same pavement sections.
4. Conduct a proof-of-concept data collection experiment with standard equipment and the new technologies side-by-side on a range of pavements to develop protocols and transformations needed to augment current pavement evaluation methods.
5. Provide guidance (e.g., AASHTO protocol) on how to use this data in state department of transportation applications.
