Image and command hybrid model for vehicle control using Internet of Vehicles

Abstract

AbstractThe growing volume of traffic generates the ultimate challenge on the research for traffic control. Vehicle automation is getting closer to reality due to the massive mobile communication capacity provided by fifth‐generation (5G) wireless sensor networks (WSNs). Nowadays, manually controlling and monitoring vehicles at traffic rush hours is becoming difficult for safety control. Thus, the present condition requires providing control services and devices, which are functioning in real‐time worldwide mode through soft Internet of Vehicles (IoV) services over a software‐defined network (SDN). The SDN makes software integration and progress monitoring much easier and convenient. This article focuses on WSN‐based vehicle automation following controlling and observation through speech recognition and image processing. The article also demonstrates the working prototype of the proposed mechanism to automatically drive a car and to detect obstacles through voice and image‐based deep learning, respectively. Moreover, obstacle detection on the road is a major concern in vehicle controlling systems. The system detects every obstacle on roads using cameras mounted to cover the view for driver assistance. Once the obstacle found, it generates alert for the driver to stop a collision. All these experiments require a high‐speed communication platform for evaluations. A case study is used on vehicle observation and controlling using speech commands and image processing with real‐world on‐road vehicle data sets.