ChatGPT on the Road: Leveraging Large Language Model-Powered In-Vehicle Conversational Agents for Safer and More Enjoyable Driving Experience

Studies on in-vehicle conversational agents have relied on pre-scripted prompts, constraining driver–agent interaction. The present study investigates an LLM-based in-vehicle agent capable of driver-initiated, multi-turn dialogues under manual driving conditions. Forty drivers participated in a driving simulator study using a within-subjects design comparing three conditions: no agent, pre-scripted agent, and ChatGPT-based agent. Results showed that the ChatGPT-based agent condition was associated with more stable driving performance across multiple metrics, including lower variability in longitudinal acceleration, lateral acceleration, and lane deviation. Subjective evaluations indicated higher competence, animacy, affective trust, and overall preference relative to the pre-scripted agent. Thematic analysis revealed diverse conversational patterns beyond driving tasks, includingassistance requests and anthropomorphic interactions. While using a Wizard-of-Oz setup, the findings suggest meaningful benefits for user experience and interaction quality in challenging situations; it suggests that generative, driver-initiated dialogue may support stable driving behavior and enriched user experience in emotionally challenging scenarios.