ELI: A Conversational LLM-Based Interface for Human–AI Driving Teams and Its Impact on Performance and Driver Status
Year: 2026
Type: article
Abstract: Highly automated vehicles often rely on takeover requests (TORs) that lack contextual transparency, treat drivers as passive fallbacks, and lead to poor situational awareness. To address this challenge, this study presents the Empowering Language Interaction (ELI) framework, a conversational interface powered by a large language model that supports bidirectional negotiation and collaborative human-AI teamwork. Using the CARLA driving simulator, 28 participants compared ELI with a conventional TOR baseline in both urban and peri-urban driving scenarios. The study employed a multidimensional evaluation approach, integrating telemetry data on driving performance with continuous monitoring of physiological indicators (electrocardiogram and electrodermal activity) and subjective questionnaires to assess driver trust and engagement. Results indicated that ELI sustained continuous driver engagement and improved the subjective comprehension of the vehicle's state. Physiologically, the conversational interface maintained active cognitive load, preventing the abrupt autonomic spikes characteristic of traditional takeover requests. Furthermore, ELI outperformed the TOR baseline in safety metrics by reducing out-of-lane events and maintaining greater safety margins. Conversational interaction has shown potential to transform drivers from passive supervisors into active teammates, improving joint decision-making without inducing over-reliance on the automated system.
Source: Sensors
DOI: 10.3390/s26165228
Institutions: University of Puerto Rico-Mayaguez, Kean University
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7203678588