Aggressive human cut-ins against defensive CAVs are modeled as a game, translated into a macroscopic friction term, and shown to reduce capacity, with a predicted worst-case at about 45% CAV penetration.
Perceived risk and travel behavior: Evidence from a driving simulator study,
6 Pith papers cite this work. Polarity classification is still indexing.
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IRL models with engineered features substantially outperform a physics-based game-theoretic benchmark at predicting cut-ins, but the game-theoretic structure reduces to independent logistic classifiers.
ModernBERT-labeled analysis of 306k social posts yields six major public-sentiment clusters on Advanced Air Mobility spanning noise, safety, regulation, workforce, drones, and military use.
Machine learning on vehicle signals enables binary road surface classification into grip or slip conditions during cruising.
Analysis of a public Pittsburgh survey shows that age, cycling experience, and infrastructure are associated with how safe people feel around self-driving cars, but the findings are not statistically tested.
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