| Resumo: | With the increase in vehicles on the roads and the consequent road traffic around the world, numerous problems have arisen, such as air pollution, congestion and road accidents. Smart strategies are needed to minimize these issues. One of the main situations, that unfortunately continues to cause many victims, is road accidents, which are often caused by adverse weather conditions, such as heavy rain, fog, insufficient light, or black ice. The TRUST project seeks to prevent these conditions from being the cause of road accidents. It also promotes the development of a meteorological and environmental monitoring system in order to identify potential risks that may lead to dangerous driving conditions, through cooperative communication between vehicles and infrastructures and using sensors and technologies for intelligent transport systems. The project points out, as one of its most important solutions, a mobile application capable of alerting drivers in real time about these conditions as well as serving as a tool for monitoring these parameters. The following dissertation occurs within the scope of this project and aims to develop new functionalities and tools for the application and system. Some of these functionalities/tools are: the collection of luminosity data from the environment, videos of road and weather conditions via smartphone, the use of image processing to aid in the development of automatic mechanisms for the detection and identification of adverse weather conditions, such as rain or insufficient light on the road, a real-time speed recommendation algorithm based on existing incidents, among others. With these features implemented, the application increases user safety by providing a real and assertive recommended speed limit for the driver’s location. It also improves the TRUST project monitoring service, by automatically analyzing and identifying dangerous weather events for drivers and by sharing this information quickly with the rest of the system, which can prevent and reduce the number of road accidents.
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