Computational Intelligence for Predictive Analytics in IoT-connected Autonomous Vehicle Networks

Abstract

The fleet of vehicles on our roads is expected to incorporate more and more smart and emphasized capabilities. Road accidents are a significant societal problem leading to the loss of human lives, pain, wealth loss, harm, and financial liabilities. Their vehicles should be independent, open-curious, tolerant, and successful and respect traffic regulations. They can recognize their driving capabilities and quickly suspend and restart them. In traffic, the vehicles must have 'social abilities' and collaborate together and with traffic challenges. Smart energies and energy-effective vehicles are demanded by regulations. Consumers gain added benefits with new automotive user interfaces, multimedia, and infotainment activities like business models, documentation of legitimate evidence, etc. To satisfy these demands, sensors together with dynamic processing on board are sharp, including the real sense by artificial perception, forecast, and normal action control loop, the Architectural Perception instance. The current situation is a prototype. The predictive study within the automotive environment on the future event is most of them. It applies interdisciplinary techniques from data mining, data science, complexity theory, control systems, engineering, software engineering, quality risk management, verification and validation, human-computer interaction, safety, dependability, security, and ethics. Pedestrian-vehicle incident and external road system environmental understanding events are difficult. For instance, while driving at a fast pace in very close proximity to different users, they pose unique challenges in terms of, e.g., dynamic vehicle control. Medical networking and smartness will analyze essential vehicle data, search for suitable behavioral action, and apply the methods. Large datasets derived from automotive AI operate together to predict the events. To take alternatives, the car interface has to properly communicate that they are able to execute efficiently. Frameworks can plan good control oxidations. Diving and accelerative actions and can also accomplish reasons and constraints.