RESEARCH ARTICLE


Effect of Driver Response on Efficiency of Vehicular Communication using Penalty Cost Function (EVCPCF)



Mahmoud Zaki Iskandarani1, *
1 Faculty of Engineering, Al-Ahliyya Amman University, Amman, Jordan


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Creative Commons License
© 2023 Mahmoud Zaki Iskandarani

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Faculty of Engineering, Al-Ahliyya Amman University, Amman, Jordan; E-mail: m.iskandarani@ammanu.edu.jo


Abstract

Background and Objective:

This study examines and takes into account three key timing factors that have an impact on the effectiveness of human-machine interfaces (HVI). A threshold-based mechanism is created to account for both cooperative driving and advanced vehicle control system (AVCS) scenarios. For AVCS and cooperative driving, the developed model takes into account on-board machine interface time, human interface time, and transmission time.

Methods:

A threshold function that represents the penalty cost of a slow driver reaction is presented in order to enable adaptive intelligence, enhance HVI design, and increase vehicle safety. The Penalty Cost Function (PCF) is used to make vehicle control systems intervene and take control in situations where the driver responds slowly to safety and warning messages. Additionally, this study demonstrates that AVCS-based vehicular systems are more responsive overall and are less impacted by the PCF function than cooperative systems.

Results:

The mathematical models created through this work allowed for a limiting efficiency value and capping for each driving scenario, according to comparative plots. This will improve the creation of more reliable control systems as part of a vehicle's mechatronics, impacting how vehicles communicate with one another in a cooperative setting. MATLAB simulation was used to verify the mathematical model. The simulation covered two limiting cases of 0.33 and 0.5 and used incrementing numbers of vehicles (10, 20, 30, 40, 50) to check the impact of increasing vehicle numbers on communication efficiency and examine whether both AVCS and AVCS with cooperative will have close levels and converge at limiting values.

Conclusion:

The successfully completed simulation demonstrated that throughput decreased as the number of vehicles increased, although in the limiting case, both scenarios and the driving system changed virtually by the same percentage.

Keywords: Driver response, HMI, HVI, Cooperative driving, Autonomous driving, Efficiency, Communication, Throughput, Data traffic.