Load Energy Control of Autonomous Connected Vehicles Based on ML Approach

Connected Automated Vehicles (CAVs) must be equipped with advanced control and precision units in order to decide automatically on the basis of any traffic condition and available energy amount. In fact, it is very essential for such vehicles to be equipped with autonomous dashboards to manage their power consumption according to various criteria related to the driving environment. Many devices on CAVs have an important energy consumption level. Certain of them are primordial for road users safety as LIDAR, the navigation sensors and the V2X communications module. Some optional features, which improve the driver comfort and luxury, such as the air-conditioning system, are managed according to the available energy and the required energy for remaining distance, as well. In this paper, we propose a strategy for automatic air conditioning system state control under some driven environment criteria based on machine learning using Support Vector Machines (SVM).