Vol. 3 No. 2 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

Cognitive Modeling for Human-Vehicle Interaction - Implications for Cybersecurity in Autonomous Vehicles: Utilizes cognitive modeling techniques to understand human-vehicle interaction and its implications for cybersecurity in Avs

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  • Dr. Sun-Young Park
Dr. Sun-Young Park
Professor of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST)
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Published 14-09-2023

Keywords

  • Cognitive Modeling,
  • Human-Vehicle Interaction,
  • Autonomous Vehicles
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Sun-Young Park, “Cognitive Modeling for Human-Vehicle Interaction - Implications for Cybersecurity in Autonomous Vehicles: Utilizes cognitive modeling techniques to understand human-vehicle interaction and its implications for cybersecurity in Avs”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 2, pp. 85–95, Sep. 2023, Accessed: Nov. 07, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/75

Abstract

The emergence of autonomous vehicles (AVs) presents a transformative shift in transportation, promising increased safety, efficiency, and accessibility. However, the complex human-vehicle interaction (HVI) dynamics in AVs introduce novel cybersecurity challenges. Cognitive modeling techniques offer valuable insights into how humans process information, make decisions, and interact with automated systems like AVs. This research paper explores the application of cognitive modeling for understanding HVI in AVs and its implications for cybersecurity.

We begin by outlining the current state of AV development and highlighting the critical role of HVI. The paper then delves into cognitive modeling, explaining its principles and various approaches, such as ACT-R (Adaptive Control of Thought-Rational) and EPIC (Executive-Process Interactive Control). We discuss how these models can be adapted to simulate human behavior within AV scenarios.

Next, the paper examines the key cognitive factors influencing HVI in AVs. These include perception, attention, situation awareness, decision-making, and trust calibration. We explore how cognitive models can be used to analyze potential vulnerabilities arising from these factors. For instance, a model simulating a driver's trust calibration in an AV could reveal scenarios where a cyberattack manipulates the system's behavior, leading the driver to surrender control despite an unsafe situation.

Furthermore, the paper explores the implications of cognitive modeling for designing secure AV systems. By understanding how humans interact with and trust AVs, we can develop more robust cybersecurity measures. This includes designing interfaces that provide clear information about system state and limitations, mitigating automation bias, and implementing safeguards against manipulation of trust signals.

Finally, the paper discusses the limitations of cognitive modeling in the context of AV cybersecurity. While models offer valuable insights, they are simplifications of the human mind and may not capture the full range of human behavior.

In conclusion, this research paper demonstrates the importance of cognitive modeling for understanding HVI in AVs and its crucial role in enhancing cybersecurity. By leveraging these models, we can develop AV systems that are not only technologically advanced but also human-centered and secure.

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References

  1. Goodall, Nicholas, et al. "A Level 5 Autonomous Vehicle Capability Definition and Taxonomy." SAE International Journal of Passenger Cars - Electronic and Electrical Systems 1 (2018): 109-128.
  2. SAE International. "Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles." SAE Standard J3016_202104 (2021).
  3. Petit, Yousuf, et al. "Remote Exploitation of Unmanned Aerial Vehicles: Hacking Drones." Communications of the ACM 57.7 (2014): 74-85.
  4. Anderson, John R. "ACT-R: A Theory of Local Models and Mental Leaps." Erlbaum (1990).
  5. Anderson, John R., et al. "An Integrated Theory of Attention and Decision Making in Human Performance." Psychological Review 103.3 (1996): 61-100.
  6. Parasuraman, Raja, et al. "Models of Information Processing and Cognitive Control in Human–Machine Interaction (HMI)." Human Factors 52.1 (2010): 3–47.
  7. Tatineni, Sumanth. "Cloud-Based Business Continuity and Disaster Recovery Strategies." International Research Journal of Modernization in Engineering, Technology, and Science5.11 (2023): 1389-1397.
  8. Vemori, Vamsi. "From Tactile Buttons to Digital Orchestration: A Paradigm Shift in Vehicle Control with Smartphone Integration and Smart UI–Unveiling Cybersecurity Vulnerabilities and Fortifying Autonomous Vehicles with Adaptive Learning Intrusion Detection Systems." African Journal of Artificial Intelligence and Sustainable Development3.1 (2023): 54-91.
  9. Shaik, Mahammad, Leeladhar Gudala, and Ashok Kumar Reddy Sadhu. "Leveraging Artificial Intelligence for Enhanced Identity and Access Management within Zero Trust Security Architectures: A Focus on User Behavior Analytics and Adaptive Authentication." Australian Journal of Machine Learning Research & Applications 3.2 (2023): 1-31.
  10. Tatineni, Sumanth. "Security and Compliance in Parallel Computing Cloud Services." International Journal of Science and Research (IJSR) 12.10 (2023): 972-1977.
  11. Parasuraman, Raja, and Daniel M. Wickens. "A Model for Trust and Attention in Human–Computer Interaction." Human Factors 52.3 (2010): 408-428.
  12. Liu, Yiwen, et al. "A Human-Centered Framework for Cybersecurity in Autonomous Vehicles." Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2017.
  13. Shalev, Daniel, et al. "Toward a Theory of Automation Bias: A Cognitive Architecture Perspective." Human Factors 52.1 (2010): 163-181.
  14. Nass, Clifford, and Youngme Moon. "Machines and Morality: The Easy Ride Down the Slippery Slope." Minds and Machines 10.3 (2000): 351-365.
  15. Dzindolet, Matthew T., et al. "Walking to Work With a Robot: The Effects of Automation on Physiological and Cognitive Workload in Office Workers." Human Factors 55.5 (2013): 638-650.
  16. Cacchiani, Matteo, et al. "A Cognitive Model for Driver Behavior in Lane Change Tasks." Transportation Research Part C: Emerging Technologies 18.1 (2010): 167-178.
  17. Endsley, Mica R. "Toward a Theory of Situation Awareness in Dynamic Systems." Human Factors 37.1 (1995): 32-64.
  18. Jian, Yuhua, Niels Moray, and Eric Kantowitz. "An Adaptive Automation Framework." Le Travail Humain 60.3 (1997): 293-310.
  19. Lee, Jung Hyoun, and Myung Seok Chung. "Development of a Human-Centered Design Framework for Autonomous Vehicles." International Journal of Industrial Ergonomics 68 (2019): 28-39.
  20. Xu, Can, et al. "A Review of Human Factors Research on Automated Vehicles: Recent Progress and Future Directions." Human Factors 59.1 (2017): 172-185.

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