A Stochastic Framework for the Algorithmic Design and Analysis of Military Entry Control Facilities
| Metadata Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Ku, Wei Shinn | |
| dc.contributor.author | Tuttle, Benjamin | |
| dc.date.accessioned | 2026-08-05T16:41:28Z | |
| dc.date.available | 2026-08-05T16:41:28Z | |
| dc.date.issued | 2026-08-05 | |
| dc.identifier.uri | https://etd.auburn.edu/handle/10415/10571 | |
| dc.description.abstract | Military Entry Control Facilities (ECFs) are designed to delay a threat vehicle long enough for security forces to respond before it reaches a protected point. The Vehicular Threat Delay Calculator (VTDC) supports this design by estimating how long a vehicle takes to traverse an approach road, but its current form is deterministic: it assumes fixed parameters, a compliant driver, and it leaves barrier placement to engineering judgment. As a result, it can misestimate the available delay and give inaccurate insight into the fastest approach an attacker might actually drive. These limitations are addressed by introducing stochasticity into the model, producing a clearer picture of the range of approaches a threat actor might realistically take in a given scenario. Path-search algorithms are then applied to ground the most consequential of those possibilities in physically achievable driving behavior. Finally, an obstacle planner identifies advantageous obstacle placements that strengthen the road's ability to delay an approaching vehicle. | en_US |
| dc.rights | EMBARGO_GLOBAL | en_US |
| dc.subject | Computer Science and Software Engineering | en_US |
| dc.title | A Stochastic Framework for the Algorithmic Design and Analysis of Military Entry Control Facilities | en_US |
| dc.type | Master's Thesis | en_US |
| dc.embargo.length | MONTHS_WITHHELD:60 | en_US |
| dc.embargo.status | EMBARGOED | en_US |
| dc.embargo.enddate | 2031-08-05 | en_US |
| dc.contributor.committee | Rilett, Laurence | |
| dc.contributor.committee | Cottam, Adrian | |
| dc.creator.orcid | 0009-0008-1484-6943 | en_US |
