This contribution presents the results of the "SENSOVO" project initiated by the Flanders Institute for Mobility (VIM), executed by the University of Antwerp (UAntwerp), the Flemish Institute for Technological Research (VITO) and the Belgian Road Research Centre (BRRC), and supported by several other parties. Both road users and road managers could benefit from massively, continuously, automatized collecting of information on road surface distress (potholes, cracking, subsidence,…) by a fleet of vehicles equipped with low-cost sensors. Road users could have immediate information on road conditions while road managers could get year-round insight on the general performance of the road network in addition to the data they obtain from annual inspections with specialized monitoring devices. The project's objective was to investigate possibilities of road surface distress detection using data collected by a fleet of vehicles. Two scenarios were considered: a large fleet of ordinary cars and trucks transmitting collected relevant sensor data already available on the CAN-bus in such vehicles; a vehicle, equipped with one or more Time-of-Flight cameras (ToF). Data on the CAN-bus were collected using either a CAN-logger that sends its data to a central server using GPRS where it is processed for road surface distress detection, or using an OBD scan-tool that sends its data using Bluetooth to a smartphone that already processes the data into detection events, forwarding only these to a central server. The performances were tested by UAntwerp and VITO. A simple computation developed by UAntwerp on the speed of all four wheels and on the vertical accelerations often allows indicating road distress. Either the CAN data-logger or the smartphone delivers the GPS location of the event. Several cars equipped with this technology sent their observations to a central database. Several ToF cameras were benchmarked by UAntwerp. Road data were collected at 40km/h and 40frames/s with ToF-cameras of brands Mesa and Fotonic. Several image processing algorithms dedicated to the identification of road distress on a flow of images were developed by UAntwerp. It has been demonstrated that several types of "unevenness" of the road surface (including potholes) can be detected. Some ToF-camera observations were added to the central database. The BRRC developed and implemented an algorithm for treatment and interpretation of the collected events in the central database using the ArcGIS software, simulating both sending out alerts to road users and providing daily "quality scores" for each road section in the network to the road manager. For this, the network was defined from a geographical map provided by the Flanders Geographical Information Agency (FGIA/AGIV). As soon as "enough" observations are made taking into account the frequency of observations in time, the defect is considered as real and will be reported. When the defect is no longer observed, it is considered as being repaired. A "score" for each road section was computed daily, from ...

The results of an analysis of the transverse profile measurement data collected in the FILTER Experiment are presented. The purpose of the analysis was to determine the repeatabilities, reproducibilities, and accuracies of the measurements. Application of the International Organization for Standardization 5725 standard resulted in the detection of a significant proportion of outlying results with most if not all devices. After removal of the outliers, average standard deviations ( SDs) for repeatability were typically 0.1 percent for cross fall, 0.5 to 0.9 mm for rut depth, and 0.25 mm for water depth: cross-fall measurements exhibited an average SD for reproducibility of 0.5 percent; for rut depth measurements the values were 1.7 to 2.7 mm, and for water depth measurements the values were 2.1 to 2.2 mm. The overall SD for the accuracies of the profile measurements was found to be 1.9 mm. The rather wide range of (in)accuracy (0.8 to 3.2 mm) among the different test sections is explained by the influence of longitudinal unevenness (international roughness index). Operating speed in the speed range used in the experiment had no significant influence on most measurements obtained. Moreover, speed had no significant influence on the repeatabilities or the reproducibilities of the indices or on profile measurement accuracy. Averaging distance had a significant influence on the SD for repeatability, which decreased by a factor of 2 to 4 when the averaging distance increased from 50 to 500 m. The reproducibilities of the indices did not significantly depend on the averaging distance in any systematic way.