The fusion of multiple sensors' data in real-time is a crucial process for autonomous and assisted driving, where high level controllers need classification of objects in the surroundings and estimation of relative positions. This paper presents an open-source framework to estimate the distance between a vehicle equipped with sensors and different road objects on its path using the fusion of data from cameras, radars and LiDARs. The target application is an advanced driving assistance system which benefits the integration of the sensors' attributes to plan the vehicle speed according to real-time road occupation and distance from obstacles. Based on geometrical projection, a low-level sensor fusion approach is proposed to map 3D point clouds into 2D camera images.