AI-Based Vehicular Network toward 6G and IoT: Deep Learning Approaches

In recent years, vehicular networks have become increasingly large, heterogeneous, and dynamic, making it difficult to meet strict requirements of ultralow latency, high reliability, high security, and massive connections for next generation (6G) networks. Recently, deep learning (DL ) has emerged as a powerful artificial intelligence (AI ) technique to optimize the efficiency and adaptability of vehicle and wireless communication. However, rapidly increasing absolute numbers of vehicles on the roads are leading to increased automobile accidents, many of which are attributable to drivers interacting with their mobile phones. To address potentially dangerous driver behavior, this study applies deep learning approaches to image recognition to develop an AI-based detection system that can detect potentially dangerous driving behavior. Multiple convolutional neural network (CNN )-based techniques including VGG16, VGG19, Densenet, and Openpose were compared in terms of their ability to detect and identify problematic driving.

Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network

In recent years, the electric vehicles (EVs) power management strategy has been developed in order to reduce battery discharging power and fluctuation when an EV requires high and rapid discharging power due to frequent stop-and-go driving operations. A combination of lithium-ion batteries and a supercapacitor (SC) as the EV’s energy sources is known as a hybrid energy storage system (HESS) and is a promising solution for fast discharging conditions. Effective power management to extensively utilize HESS can be developed if future power demand is accessible. A vehicular network as a typical form of the currently developed internet of things (IoT) has made future information obtainable by collecting information on surrounding data. This paper proposes a power management strategy for the HESS with the support of IoT. Since the obtained information from vehicular network could not directly be used to improve HESS, a two levels control structure has been developed to perform future data prediction and power distribution. A fuzzy logic controller (FLC) is utilized in the level one control structure to manage a HESS power split based on future information. Since FLC requires future information as a reference input, the future information is obtained by using an artificial neural network (ANN) in a level two control structure. The ANN prediction is direct, which could approximate the future power demand prediction with the assumption that the vehicular network scenario that is used to obtain surrounding information is deployed. Simulation results demonstrate that the average discharging battery power and power variation are reduced by 46.1% and 52.3, respectively, when compared to the battery-only case.