The study investigates societal acceptance of Innovative Air Mobility (IAM) operations, from a perspective of visual and audiovisual pollution in urban and rural environments. Participants’ perception of drone and eVTOL operations was examined through Virtual Reality (VR) simulations across diverse scenarios, including various drone types, flight paths, and the presence or not of audio input, to measure the impact on visual/audiovisual pollution. Two methodologies are developed to quantify drone acceptance: NLP-based and HCI-based Acceptance Analyses. The NLP approach employed sentence-level sentiment analysis on verbal input of the participants during simulations, to uncover underlying factors affecting drone operation acceptance and implied acceptance beyond self-stated numerical ratings. The HCI method analysed participants’ interactions by quantifying non-tolerated audiovisual/visual pollution periods through “clicks” during the VR simulations. Results showed drone type and environment influence public acceptance. Sensing drones received the highest acceptance, while lower societal acceptance was indicated for passenger drones through lower sentiment scores. English speakers demonstrated higher readiness to approve drone operations, potentially due to more frequent drone exposure or linguistic differences, with the reasons requiring further investigation. The strong performance of the XGBoost model in predicting non-tolerated audiovisual/visual pollution validates the indirect predictive approach using HCI-collected biometric data. These findings provide a comprehensive understanding of human perception and acceptance levels in human-UAV interactions, surpassing self-reported ratings. The methods provide substantial guidance to UAV stakeholders, urban planners, and policymakers to design IAM systems accounting for public comfort and societal expectations.

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Classification,Drones,Innovative air mobility,Machine learning,Natural language processing,Sentiment analysis,Societal acceptance,Virtual reality simulations,XGboost

• Matemáticas - Ciências Naturais
• Ciências da Computação e da Informação - Ciências Naturais

Esta publicação é um output do(s) seguinte(s) projeto(s):

• Impact and capacity Assessment Framework for U-space Societal Acceptance