Impairment-Aware Intelligent Mobility System 2 (I-AIMS2)

Purpose and goal

The I-AIMS2 project will investigate in a demonstrator vehicle, how safety, well-being, and fuel economy can be improved by monitoring the driver and allowing a large language model (LLM), with voice feedback, to regulate the driver’s cognitive-affective-risk state. The project will evaluate SmartEye´s "Sheila-Guard" LLM integrated into their driver monitoring system based on: a) in-the-wild driver data, b) simulation testing, c) embodied LLM interfaces (including a small robot head).

Expected effects and result

1) Sheila-Guard (LLM) to achieve TRL 6 or 7 for in-cabin use; 2) Documentation of the scenarios in which in-cabin based LLM feedback can mitigate cognitive impairments; 3) Documentation of the scenarios in which an in-cabin based ´embodied´ interface (robot head) can mitigate cognitive impairments: quantitative (effects on stress) and qualitative evaluation of interface design; 4) Documentation of tested operational boundaries of the deployed system with respect to safe and timely use of LLMs.

Planned approach and implementation

Two iterations of the project will be conducted to test Sheila-Guard in real-world environments and to evaluate its operational limits in controlled (simulated) environments. Interface designs (including robot heads) are tested both in real-world environments and against safe operational limits. Insights from I-AIMS (step 1) form the basis for the initial setup. Fuel efficiency is also evaluated in relation to the impact of LLM feedback on driver performance.