Sensing and Signal Processing for a Distributed Pavement Monitoring System

Estimation of road profile parameters and detection of road pavement failures using in-vehicle sensors such as vertical accelerometers or position sensors is of immense value in a futuristic distributed highway monitoring system. In this paper, a novel road elevation profile model, well suited for analysis using statistical signal processing tools, is developed to characterize road features. We then apply maximum likelihood (ML) and minimum mean square error (MMSE) estimation techniques to estimate model parameters using in-vehicle vertical acceleration and displacement measurements. In addition, we show that the estimates of the road model parameters can be used to detect sudden disturbances in the road or pavement failures