Vol. 4 No. 1 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

Cognitive Load Analysis of Cybersecurity Interfaces in Autonomous Vehicle Control Systems

Dr. Neema Balakrishnan
Associate Professor of Information Systems, University of Dar es Salaam, Tanzania
Cover

Published 02-02-2024

Keywords

  • security systems,
  • human-machine interface (HMI)

How to Cite

[1]
Dr. Neema Balakrishnan, “Cognitive Load Analysis of Cybersecurity Interfaces in Autonomous Vehicle Control Systems”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 1, pp. 158–186, Feb. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/120

Abstract

To assure the functioning of such semi-autonomous systems, it is vital for the operator to cooperate efficiently with both the vehicular human-machine interface (HMI) and the security systems. The driver’s workload can increase due to both the manual control of the vehicle during the levels of low driving automation and the safety critical control to regain the manual driving of the vehicle from conditional and high driving-level automation based on mainly cyber and physical events systems failure. We shall delve into deep-dive details and outline analysis of the effects of the choices regarding the logic and HMI design of the cybertrail on the operation of the autonomous vehicles. The art-of-the-state interfaces to allow the operator to provide the Legal Request for The Control (LRfC) to gain the manual control during the cyber event and HMI designs to provide security aspects’ logic are vital concerning the sustainability of autonomous vehicles. Such UIs also describe the cyber events or follow the various warning levels and possess separate logic units to decrease the cognitive load of the operators and thereby make it easier for interest to achieve satisfactory security status for traffic [1].

Downloads

Download data is not yet available.

References

  1. Sadhu, Ashok Kumar Reddy, et al. "Enhancing Customer Service Automation and User Satisfaction: An Exploration of AI-powered Chatbot Implementation within Customer Relationship Management Systems." Journal of Computational Intelligence and Robotics 4.1 (2024): 103-123.
  2. Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
  3. Machireddy, Jeshwanth Reddy. "Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI." Hong Kong Journal of AI and Medicine 2.1 (2022): 10-36.
  4. Perumalsamy, Jegatheeswari, Muthukrishnan Muthusubramanian, and Selvakumar Venkatasubbu. "Actuarial Data Analytics for Life Insurance Product Development: Techniques, Models, and Real-World Applications." Journal of Science & Technology 4.3 (2023): 1-35.
  5. Devan, Munivel, Lavanya Shanmugam, and Manish Tomar. "AI-Powered Data Migration Strategies for Cloud Environments: Techniques, Frameworks, and Real-World Applications." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 79-111.
  6. Selvaraj, Amsa, Chandrashekar Althati, and Jegatheeswari Perumalsamy. "Machine Learning Models for Intelligent Test Data Generation in Financial Technologies: Techniques, Tools, and Case Studies." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 363-397.
  7. Katari, Monish, Selvakumar Venkatasubbu, and Gowrisankar Krishnamoorthy. "Integration of Artificial Intelligence for Real-Time Fault Detection in Semiconductor Packaging." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.3 (2023): 473-495.
  8. Tatineni, Sumanth, and Naga Vikas Chakilam. "Integrating Artificial Intelligence with DevOps for Intelligent Infrastructure Management: Optimizing Resource Allocation and Performance in Cloud-Native Applications." Journal of Bioinformatics and Artificial Intelligence 4.1 (2024): 109-142.
  9. Prakash, Sanjeev, et al. "Achieving regulatory compliance in cloud computing through ML." AIJMR-Advanced International Journal of Multidisciplinary Research 2.2 (2024).
  10. Venkataramanan, Srinivasan, et al. "Leveraging Artificial Intelligence for Enhanced Sales Forecasting Accuracy: A Review of AI-Driven Techniques and Practical Applications in Customer Relationship Management Systems." Australian Journal of Machine Learning Research & Applications 4.1 (2024): 267-287.
  11. Makka, A. K. A. “Implementing SAP on Cloud: Leveraging Security and Privacy Technologies for Seamless Data Integration and Protection”. Internet of Things and Edge Computing Journal, vol. 3, no. 1, June 2023, pp. 62-100, https://thesciencebrigade.com/iotecj/article/view/286.