A Neural-Network-Based Framework for Supporting Driver Classification and Analysis

The proliferation of info-entertainment systems in nowadays vehicles has provided a really cheap and easy-to-deploy platform with the ability to gather information about the vehicle under analysis. The infrastructure in which these information can be used to improve safety and security are provided by Ultra-response connectivity networks with a latency of below 10 ms. In this paper, we propose a service-oriented architecture based on a fully connected neural network architecture considering position-based features aimed to detect in real-time: (i) the driver and (ii) the driving style, with the goal of providing an architecture for increasing security and safety in automotive context. The experimental analysis performed on real-world data shows that the proposed method obtains encouraging results.