IoT4SafeDriving

logos
Aumentar Tamaño del texto Disminuir Tamaño del texto

Intelligent Driving Safety System under an IoT platform with low-cost devices [RTI2018-095143-B-C2]

 The general objectives are:

  • Design of an interoperable real-time software architecture based on IoT (Internet of Things) which allows to set up different levels of connected vehicles - vehicle-to-sensor on-board (V2S), vehicle-to-vehicle (V2V) or vehicle-to-road infrastructure (V2R) - to create driving safety systems by plug and play smart things connected to the vehicle network (sensors, actuators, observers and controllers).
  • Design an integrated vehicle dynamic control system in order to improve the global dynamic behaviour of the vehicle using information from low-cost sensors.
  • Design of an active acoustic system, based on the radar principle that allows estimating the position and the kinematic parameters of pedestrians and close objects on the path followed by the vehicle for an anti-collision system for pedestrians.

One advantage of the driving safety, which will be designed, is that it will be able to be applied in convectional vehicles (vehicle with driver) and autonomous vehicles. The IoT architecture enables the combination of different kinds of elements independently the manufacturer.

ImagenProyecto

The teams involved in this coordinated project are:

  • ISVA_UC3M team belongs to the Research Institute of Vehicle Safety (ISVA) of the Carlos III University of Madrid (UC3M). ISVA promotes interdisciplinary research in vehicle safety and its activities are organized through different Departments. The research project is going to be developed my ISVA’s members who belong to Mechanical Engineering Department and Computer Science and Engineering Department.
  • GPA_UVA team belonging to the Department of Signal Theory and Communications and Telematics Engineering is a GIR (Recognized Research Group) of the University of Valladolid. 

 

Publications

  •  Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach. L. Prieto, S. Sanz, J. Garcia-Guzman, M.J.L. Boada and B.L: Boada. Sensors, Vol 20(13), 3679. 2020. doi:https://doi.org/10.3390/s20133679 (registering DOI). 
logos_universidades