Narrate-to-Sim: A Human Factors-Oriented Driving Simulator with Natural-Language-Supported Scenario Authoring

Abstract

Driving simulators support human factors and automotive user interface research, allowing repeated testing of unsafe or future-oriented scenarios within controlled and low-risk settings. However, researchers often invest additional effort because commercial platforms represent scenarios through vehicle behavior and timing, whereas researchers define trials through experimental conditions, critical events, and cues. Human factors studies also involve physiological sensors and post-trial questionnaires, which are often managed separately, risking misalignment among simulator events, sensor data, and responses. To address these challenges, Narrate-to-Sim uses deterministic, rule-based parsing to translate supported natural-language prompts into executable scenarios and synchronize simulator logs with physiological data via Lab Streaming Layer (LSL). It features a survey module for post-trial questionnaires and allows researchers to organize trials, sensors, and questionnaires into experiment playlists for data collection. Overall, this demo presents a lightweight driving-simulator platform that reduces engineering effort and allows researchers to focus on study design.