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Abstract
An electric vehicle with autonomous driving is a possibility provided technology innovations in multi-disciplinary approach. Electric vehicles leverage environmental conditions and are much desired in the contemporary world. Another great possibility is to strive for making the vehicle to drive itself (autonomous driving) provided instructions. When the two are combined, it leads to a different dimension of environmental safety and technology driven driving that has many pros and cons as well. It is still in its infancy and there is much research to be carried out. In this context, this paper is aimed at building an Artificial Intelligence (AI) framework that has dual goal of “monitoring and regulating power usage” and facilitating autonomous driving with technology-driven and real time knowledge required. A methodology is proposed with multiple deep learning methods. For instance, deep learning is used for localization of vehicle, path planning at high level and path planning for low level. Apart from this, there is reinforcement learning and transfer learning to speed up the process of gaining real time business intelligence. To facilitate real time knowledge discovery from given scenarios, both edge and cloud resources are appropriately exploited to benefit the vehicle as driving safety is given paramount importance. There is power management module where modular Recurrent Neural Network is used. Another module known as speed control is used to have real time control over the speed of the vehicle. The usage of AI framework makes the electronic and autonomous vehicles realize unprecedented possibilities in power management and safe autonomous driving.Key words:Artificial IntelligenceAutonomous DrivingRecurrent Neural NetworkTransfer Learning
Highlights
Among the new technological trends have been arisen in the last decade, Autonomous Driving gained a lot of attention, with significant effort and resources have invested by both academia and enterprises
The system is aimed at self-learning as humans do and acquire business intelligence (BI) incrementally leading to more intelligent vehicle in realizing autonomous driving
There are many considerations while dealing with Electric Autonomous Vehicle (EAV). They include speed control, driving path learning, power management and a host of other activities that are essential to learning path and movement in the current state of observations
Summary
Among the new technological trends have been arisen in the last decade, Autonomous Driving gained a lot of attention, with significant effort and resources have invested by both academia and enterprises. In this paper we build an AI based API that governs autonomous driving by exploiting diversified set of sensors, multi-modal learning, edge and cloud computing in real time to ascertain traffic, making well informed decisions on movements and steering with highly accurate judgements. It is a software product working at application level dealing with data of different sensors associated with autonomous vehicle. It should be so as the vehicle is expected to drive itself without a human expert driver. The system is aimed at self-learning as humans do and acquire BI incrementally leading to more intelligent vehicle in realizing autonomous driving
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