Road Safety with Android Auto and Machine Learning


This thesis aims to research the question of how to predict road safety and how a driver can safely receive relevant information on road safety during a drive. This has become a relevant field of research, with sophisticated computing hardware available as a feature in cars. Additionally, operation areas and computation capability of mobile devices are expanding. The results of the experiment in this thesis has been an Android application which implements Machine Learning Models and Statistical Models to predict accidents, based on the current situation of the user. The Machine Learning Models do not provide valid scientific evidence for the predictions to be correct, due to the supervised historical traffic data, used to train the Machine Learning models, having inconsistent patterns of how accidents happen. The Machine Learning models are activated by Statistical Models using historical traffic data. The models are only compatible to some extent. This is limited by a historical weather data set, which only enables the model to predict accidents within a range incorrect with a level of abstraction. Thus the Statistical Models and the Machine Learning Models are implemented in the application using the Android System compatible with the Android Auto subsystem. Android Auto enables a safe communication channel with the drive. The application is distributable to Android Users and compatible with 60.3% of all android devices. In the future the models predictions might be invalid, as the behaviour of a car might change. Although the experiment does not provide any sophisticated pipeline for extending the models with new data.