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The reliable detection of water and ice over road surfaces is an important issue in improving the traffic safety and in reducing the costs for maintenance, particularly during winter. A low-cost capacitive sensor for the estimation of road conditions is studied. A simulation model was developed to investigate the capacitance of the sensor when air, water, or ice is covering its surface and to assess the effect of the variation of an environmental temperature or of the thickness of water or ice. An algorithm for the estimation of the state of the sensor (dry, wet, or icy) was developed based on the results of the simulations, which indicated that the time derivative of the estimated capacitance provided optimal information. The accuracy and reliability of the estimates provided by the sensor were assessed in laboratory experiments, placing more sensors in a climatic chamber and investigating the estimated state of the sensors and the timing of the identification of wet-icy or icy-wet transitions. Reliable estimates were obtained by all the sensors, with a dispersion of the transition times on the order of a few minutes. The sensor was also investigated in the field. Two sensors (one of which was bituminized) were embedded in a road pavement to monitor continuously the road surface condition for a month. Both sensors provided indications in line with the environmental conditions, identifying properly the icy condition and indicating the wet state of the road during both rain and fog. Thus, the sensor is suggested as a feasible tool for monitoring the road conditions to support information systems improving the security and efficient maintenance of roads during winter. [ABSTRACT FROM AUTHOR]

This paper presents the main results obtained in the Agencies for PErception in environmental monitoring (APE) Project, funded by the Italian Ministry of University and Research, and aimed at studying the employment of advanced distributed artificial intelligence techniques in developing cooperative multiagent systems oriented to support environmental monitoring applications. In particular, this paper describes issues about communication between perceptive agents and two implemented multiagent systems for monitoring electromagnetic fields and for pollutant evaluation. [ABSTRACT FROM AUTHOR]