Neelakshi Majumdar | Searcharxiv

arXiv2026

An ArcGIS Framework for Mapping Human-Centered Noise Annoyance for AAM Infrastructure Planning

Advanced Air Mobility (AAM) represents a transformative shift in urban transportation; however, successful implementation depends strongly on public acceptance, with noise emerging as a major concern for low-altitude electric vertical takeoff and landing (eVTOL) operations. Existing studies commonly describe eVTOL noise using acoustic metrics such as A-weighted sound level and day-night average sound level. This study develops a Geographic Information System (GIS)-based framework that translates eVTOL acoustic outputs into maps representing the percentage of the population that is highly annoyed (%HA) for a representative medical delivery route in Northwest Arkansas. The results show that noise and annoyance generally decrease with distance from the route, while the highest annoyance occurs during descent, followed by climb and cruise. Census population data are integrated to estimate the number of highly annoyed individuals and identify spatial impact hotspots. Noise-annoyance results are then combined with route distance and airspace factors to evaluate alternative routes and identify balanced routing strategies. The proposed framework connects acoustic assessment with human response and supports the identification of noise-sensitive areas, comparison of route alternatives, and socially sustainable AAM infrastructure planning.

eess.SY↗

arXiv2026

A Systems Engineering Framework for Vision-Language-Enabled UAV Triage and Disaster Response

Recent advances in Vision Language Models (VLMs) have created new opportunities for disaster response, where responders must interpret large volumes of sensor data under time pressure. Current VLM applications include social media monitoring for situational awareness, generation of draft action plans, and translation of technical alerts into public-facing messages. While these efforts can accelerate information flow, they remain largely limited to decision-support roles. Such approaches can increase operator burden because humans must still translate outputs into coordinated actions across teams and robotic assets. This study explores the viability of embedding VLMs as coordination agents within the human-UAV loop. The proposed architecture integrates natural language interaction, mission-level task coordination, software-in-the-loop implementation, and communication aligned with the Incident Command System (ICS). Rather than functioning solely as advisory tools, VLMs facilitate communication between human operators, mission control logic, and UAV task execution. The framework was developed using a Model-Based Systems Engineering (MBSE) approach, with use case and block definition diagrams representing system roles, internal structure, and component interactions. Three key elements, the VLM Coordinator Agent, UAV Mission Control, and Task Allocator, were implemented within an integrated simulation and control environment. A preliminary human-factors evaluation with seven participants showed reduced perceived workload across mental demand, effort, and frustration, along with high ratings for AI trust and communication clarity. By integrating MBSE, software-in-the-loop testing, and human-factors evaluation, this work advances scalable human-autonomy teaming for high-stakes disaster response, with broader implications for aerospace autonomy and civil safety.

cs.RO↗