Two low-cost methods of estimating the road surface condition are presented in the paper, the first one based on the use of accelerometers and the other on the analysis of images acquired from cameras installed in a vehicle. In the first method, miniature positioning and accelerometer sensors are used for evaluation of the road surface roughness. The device designed for installation in vehicles is composed of a GPS receiver and a multi-axis accelerometer. The measurement data were collected from recorded ride sessions taken place on diversified road surface roughness conditions and at varied vehicle speeds on each of examined road sections. The data were gathered for various vehicle body types and afterwards successful attempts were made in constructing the road surface classification employing the created algorithm. In turn, in the video method, a set of algorithms processing images from a depth camera and RGB cameras were created. A representative sample of the material to be analysed was obtained and a neural network model for classification of road defects was trained. The research has shown high effectiveness of applying the digital image processing to rejection of images of undamaged surface, exceeding 80%. Average effectiveness of identification of road defects amounted to 70%. The paper presents the methods of collecting and processing the data related to surface damage as well as the results of analyses and conclusions.
A system setup for measurements of acoustic field, together with the results of 3D visualisations of acoustic energy flow are presented in the paper. Spatial sampling of the field is performed by a Cartesian robot. Automatization of the measurement process is achieved with the use of a specialized control system. The method is based on measuring the sound pressure (scalar) and particle velocity(vector) quantities. The aim of the system is to collect data with a high precision and repeatability. The system is employed for measurements of acoustic energy flow in the proximity of an artificial head in an anechoic chamber. In the measurement setup an algorithm for generation of the probe movement path is included. The algorithm finds the optimum path of the robot movement, taking into account a given 3D object shape present in the measurement space. The results are presented for two cases, first without any obstacle and the other - with an artificial head in the sound field.