Emotune: Exploring Context-Aware LLM Support for Driver Emotion Regulation

Abstract

Driver emotions such as stress, frustration, and anxiety are associated with riskier behavior and reduced situational awareness, motivating in-vehicle systems that regulate affect in real time. However, emotion-only approaches capture how a driver feels, but not why, limiting appropriate support delivery. We present Emotune, a work-in-progress framework integrating emotional state assessment, driving-context awareness, and Large Language Model (LLM)-assisted intervention selection within the CARLA simulator. The framework uses a human-guided workflow, in which predefined interventions are selected by locally deployed LLM agents. As a methodological contribution, it enables studying how contextual information can inform emotion regulation in driving. We report a pilot study comparing a baseline emotion-aware agent with a context-aware agent that grounds interventions in driving situations. No statistically significant between-condition differences were found in the pilot study. Qualitative feedback instead highlighted contextual relevance, intervention timing, and perceived empathy as priorities for future investigation.