Open Access

ARTICLE

Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine

by Dildar Hussain¹, Muhammad Adnan Khan^2,*, Sagheer Abbas³, Rizwan Ali Naqvi⁴, Muhammad Faheem Mushtaq⁵, Abdur Rehman³, Afrozah Nadeem²

1 School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 02455, Korea
2 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
3 School of Computer Science, National College of Business Administration & Economics, Lahore, 54000, Pakistan
4 Department of Unmanned Vehicle Engineering, Sejong University, Seoul, 05006, Korea
5 Department of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan

* Corresponding Author: Muhammad Adnan Khan. Email:

Computers, Materials & Continua 2021, 66(1), 141-156. https://doi.org/10.32604/cmc.2020.013458

Received 07 August 2020; Accepted 21 August 2020; Issue published 30 October 2020

Abstract

The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries, including the transportation sector. The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features. One of these tasks is to ensure that vehicles are autonomous, intelligent and able to grow their repository of information. Machine learning has recently been implemented in wireless networks, as a major artificial intelligence branch, to solve historically challenging problems through a data-driven approach. In this article, we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try to focus on this emerging field. Deep Extreme Learning Machine (DELM) framework is introduced in this article to be incorporated in vehicles so they can take human-like assessments. The present GPS compatibility issues make it difficult for vehicles to take real-time decisions under certain conditions. It leads to the concept of vehicle controller making self-decisions. The proposed DELM based system for self-intelligent vehicle decision makes use of the cognitive memory to store route observations. This overcomes inadequacy of the current in-vehicle route-finding technology and its support. All the relevant route-related information for the ride will be provided to the user based on its availability. Using the DELM method, a high degree of precision in smart decision taking with a minimal error rate is obtained. During investigation, it has been observed that proposed framework has the highest accuracy rate with 70% of training (1435 samples) and 30% of validation (612 samples). Simulation results validate the intelligent prediction of the proposed method with 98.88%, 98.2% accuracy during training and validation respectively.

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Citations