Safety is a must for car manufacturers, and most of their customers consider this as one of the most important factors when buying a vehicle. Providing such safety is not only a matter of robustness, but also a matter of avoiding interactions with the car. Distractions caused by such interactions have turned to be the largest cause of accidents in the USA.
Thus, creating predictive systems that foresee the interaction of the driver with the car is a need. Some types of predictive systems are based on multivariable Knowledge Bases (KB) that record all the actions done by the user. The evolution of the actions over time, allow those KB systems predicting and correcting the actions of the driver, increasing the safety of the vehicle and its occupants.
However, managing those recommendations from the cloud entails different issues that can turn into problems, like the latency of the network which may delay predictions and imply higher accident chances, or the sharing of data with third parties that may be used against the user, causing data privacy breaching.
