This internship focuses on defining and validating robust driver state metrics to support future ADAS safety and compliance (e.g., future Euro NCAP needs). The goal is to translate “driver states” into actionable, measurable Key Performance Indicators (KPIs) using Driver Monitoring System (DMS) camera signals and driving performance indicators:

• Review and understand state‑of‑the‑art driver state metrics related to attention, distraction, understanding (confusion), mental load, and stress.
• Define a candidate KPI set based on DMS and driving performance signals (e.g., gaze dispersion/entropy, off‑road glance patterns, fixation/saccade patterns, reaction time, lane deviation, steering micro‑corrections, …).
• Data analysis to investigate KPI patterns and translate them into driver state insights.
• Collaborative design and execution of simulator study, including: Definition of driving scenarios and driver tasks that reliably induce the desired changes in driver state (e.g., secondary task distraction) and repeatable, robust collection of user data.
• KPI validation (sensitivity, robustness, interpretability) and application to driver-ADAS interactions.

• You are fluent in English (TME’s business language)
• You are a Master student in Engineering, Industrial design, Cognitive Scene, or another relevant field
• Interested in human factors and the interaction with safety systems.
• You have demonstrated programming skills in MATLAB or Python
• For example, applied to time-series data analysis, basic statistical analysis, and/or data interpretation.
• The following skills are not required, but consider an asset:
• Experimental UX design and behavioural research methods
• Prior exposure to eye-tracking or other driver monitoring systems
• Experience working with driving simulators.
• You are a team player with strong organisation skills and curiosity to learn.

It is mandatory that applicants are students for the entire period of the internship.