| Resumo: | Given the increasing use of vehicles and the need to protect passengers, road safety has been of great importance in recent times. One of the main causes of road accidents is directly related to weather conditions, namely rain and wet conditions, which respectively decrease the visibility and stability of the car. Currently, weather stations are used to determine only the weather conditions. However, intelligent transportation systems are becoming more complex and adopting new models to interpret the conditions of the environment and disseminate the information more quickly and efficiently. Cooperative Intelligent Transport Systems are meant to achieve this purpose based on cooperative sensors and the Internet of Things (IoT), while ensuring greater coordination, either by the information made available to the driver, from communication systems such as Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I), or by using the vehicle sensors as a reference for traffic conditions. An interactive algorithm was developed, alongside the driver, to identify the necessary parameters to study the weather conditions. This algorithm is divided into several processes, that are specified within the intra-vehicular communication through proprietary signals, such as the state of the windshield wiper to detect rain, headlight position as a way to assess visibility and wheel speed to analyze the state of the surface and possible accident causes. The algorithm is composed by several methods, due to the different representations presented by the parameters. Thourght implementing different processes of detection parameters represented by only one bit of information and for parameters that have one or more bytes of information. Through using reverse engineering, the purpose is not only to interpret the transitions that occur in the signals and, after a filtering process, detect the position and the message identifier of the desired parameter, but also to correlate and compare these signals with the OBD-II (On-Board Diagnostic) diagnostic responses without a large set of data and in a short period of time. Afterwards, the parameters are transmitted to an On-Board Unit (OBU) platform that broadcasts the data using cooperative messages to the vehicle’s network. Tests were performed in two cars and the results obtained were satisfactory. All binary parameters were found in the first car, but not in the non-binary parameters, whereas on the second car is founded. However, these events allowed to acquire knowledge about the matter in which car manufacturers develop their intra-vehicular systems. It can, therefore, be concluded that this work adds an important step in this field.
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