Fully Autonomous Vehicle-Borne Improvised Explosive Devices- Mitigating Strategies is a Naval Postgraduate School thesis by Kevin S. Knopf.

Integrating technology into fully autonomous vehicles (FAVs) is poised to become a significant homeland security concern. With investments ranging from major corporations to small startups reaching billions of dollars, the widespread availability of fully autonomous vehicles to the general public is anticipated in the coming years. As these vehicles become more accessible to individuals and private entities, potential impacts on safety, both at the individual and community levels, are expected.

This thesis outlines the projected threat stemming from the malevolent use of fully autonomous vehicles, specifically as potential vehicle-borne improvised explosive devices (VBIEDs). It highlights the ease with which autonomous vehicles can be repurposed for explosive delivery and explores proactive technological solutions to mitigate this threat. There is a critical need for the implementation of secure communications, robust user authentication, law enforcement override mechanisms, and payload interrogation protocols from the outset of the system design process.

Without adopting a security-centric approach to system design, the nation risks responding reactively rather than preventing the use of autonomous vehicles as explosive delivery systems. The overarching goal of this thesis is to underscore the potential outcomes achievable through collaborative public-private partnerships addressing strategic issues related to public safety in the United States.

Publication Date– March 2019

Fully Autonomous Vehicle-Borne Improvised Explosive Devices- Mitigating Strategies contains the following major sections:

  • Introduction
  • Emerging FAV Threats
  • FAVBIED Mitigating Strategies
  • A Matter of Perspective
  • Moving Forward

Approved for public release.  Distribution is unlimited.

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Author- Kevin S. Knopf

Other Works from the Naval Postgraduate School-

Analysis of Multi-Layer System of Systems to Counter UAS

Combatting Drone Swarms with Machine Learning

Feasibility of Indirect Fire for Countering Swarms of sUAS

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