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Multi-Vehicle Path Following and Adversarial Agent Detection in Constrained Environments

Chintalapati, Veera Venkata Tarun Kartik
http://orcid.org/0000-0003-4427-0478
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613751238253121

Abstract Details

2020, MS, University of Cincinnati, Engineering and Applied Science: Aerospace Engineering.
The aim of this research is to investigate two fundamental challenges to vehicle platooning: security and path following. For the first part of this investigation, we focus on the security vulnerabilities of vehicle platoons that make them susceptible to external attacks. These attacks are designed to cause oscillations within the platoon in a manner that results in collisions and pile-ups. In order to address such a scenario, this research focuses on the design and detection of such adversarial agents in vehicle platoons. To achieve this, we consider a highway scenario and model a bi-directional predecessor-leader following the platoon. We then introduce an attacker that can disrupt the normal performance of the platoon and cause oscillations that amplify and eventually lead to collisions. Then, we compare methodologies for detecting and isolating the adversarial agent under various information availability scenarios. Finally, we prove that it is possible to identify a compromised vehicle with a high accuracy using only the noisy local sensor information available on-board to each vehicle. Secondly, we focus on the challenge of coordinated control in constrained environments. When navigating in challenging environments with very limited infrastructure or landmarks (mines, farms, etc), it is nearly impossible for modern-day autonomous vehicles to stay on the desired path. A cost-effective solution for such a challenge would be to have one manned "leader" vehicle that the other vehicles can follow. Therefore, we propose and implement a novel guidance algorithm that ensures multi-vehicle platoons to stay on the path when traversing such environments. We further investigate the string stability of the platoon and its dependence on the characteristics of the trajectory chosen.
Rajnikant Sharma, Ph.D. (Committee Chair)
Manish Kumar, Ph.D. (Committee Member)
Boyang Wang, Ph.D. (Committee Member)
54 p.
Aerospace Materials
Autonomous platoon; Cyber physical systems; Intrusion detection; Machine learning; string stability; path following

Recommended Citations

Citations

  • Chintalapati, V. V. T. K. (2020). Multi-Vehicle Path Following and Adversarial Agent Detection in Constrained Environments [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613751238253121

    APA Style (7th edition)

  • Chintalapati, Veera Venkata Tarun Kartik. Multi-Vehicle Path Following and Adversarial Agent Detection in Constrained Environments. 2020. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613751238253121.

    MLA Style (8th edition)

  • Chintalapati, Veera Venkata Tarun Kartik. "Multi-Vehicle Path Following and Adversarial Agent Detection in Constrained Environments." Master's thesis, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613751238253121

    Chicago Manual of Style (17th edition)

ucin1613751238253121
252
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This open access ETD is published by University of Cincinnati and OhioLINK.