Vol. 3 No. 2 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Machine Learning for Autonomous Vehicle Fleet Management and Optimization

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  • Dr. Serkan Gürsoy
Dr. Serkan Gürsoy
Associate Professor of Electrical and Electronics Engineering, Istanbul Technical University (ITU)
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Published 30-12-2023

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How to Cite

[1]
Dr. Serkan Gürsoy, “Machine Learning for Autonomous Vehicle Fleet Management and Optimization”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 86–106, Dec. 2023, Accessed: Nov. 21, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/103

Abstract

[1] A smart city integrates ICT and communication technologies into urban infrastructure to improve the quality of life for citizens. Autonomous cars are leading components of smart cities, and they represent the future of transportation. Urban fleet management is considered to be an efficient method for the deployment of autonomous vehicles in smart cities, including enterprise-level Fleet Management Systems (FMS) and consumer vehicle-sharing platforms. Such aspects show the potential of machine learning (ML) and artificial intelligence (AI) methods to dynamically optimize the operation of vehicles connected to the Internet of Things (IoT). It is the main challenge to coordinate the operations of autonomous vehicles participating in a fleet to achieve an optimal operation zone between picked up location and requested drop-off location, and meet the specific mobile metrics (∀i λi(si, ti) < T) related to passenger-related comfort levels for each passenger pick up etc.[2] This chapter represents a comprehensive review related to the use of ML and AI algorithms and systems developed for an autonomous driving system. In particular, the discussion in this chapter is also about researched developments in the deep learning domain for a vehicle driving scenario. Moreover, the discussion also includes research based on efficient switches and methods suggested to keep the performance optimal while keeping the route calculation energy efficient. In summary, in this chapter the authors discuss the future challenges and the proposed solutions, which are important for developing autonomous driving as well as smart transportation systems.

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