Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles

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 Intelligence Autonomous Driving Recurrent Neural Network Transfer Learning

Routing Approaches used for Electrical Vehicles Navigation: A Survey

“…However, these methods require a comparison of their results with other systems, especially due to a lack of data. The multidisciplinary approach [47] used in this system is based on the method of artificial intelligence, which uses deep learning as reinforcement learning to path learning and localize vehicles at the high and low levels with respect to the real-time decisions made by cloud facilities and advanced resources through transfer learning. Additionally, Recurrent Neural Network (RNN) is employed for power management to reduce cost and control power usage because it contains memory that is continuously learning using time series data.…”

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“…In step 3, the program creates a log, as shown in the above image. We train the ai model using tensorflow and keras(CNN) [7][8]. In step 4, the trained ML tries to drive the vehicle.…”

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