Precision Enhancement of Pavement Roughness Localization with Connected Vehicles
Abstract
Transportation agencies rely on the accurate localization and reporting of roadway anomalies that could
pose serious hazards to the traveling public. However, the cost and technical limitations of present methods prevent
their scaling to all roadways. Connected vehicles with on-board accelerometers and conventional geospatial position
receivers offer an attractive alternative because of their potential to monitor all roadways in real-time. The
conventional global positioning system is ubiquitous and essentially free to use but it produces impractically large
position errors. This study evaluated the improvement in precision achievable by augmenting the conventional geofence
system with a standard speed bump or an existing anomaly at a pre-determined position to establish a
reference inertial marker. The speed sensor subsequently generates position tags for the remaining inertial samples
by computing their path distances relative to the reference position. The error model and a case study using
smartphones to emulate connected vehicles revealed that the precision in localization improves from tens of metres
to sub-centimetre levels, and the accuracy of measuring localized roughness more than doubles. The research results
demonstrate that transportation agencies will benefit from using the connected vehicle method to achieve precision
and accuracy levels that are comparable to existing laser-based inertial profilers.