DC5. AI to mitigate driver distraction and drowsiness at different levels of automation

Hosts: TUD & PSA-LAB

Objectives:

• To understand factors contributing to driver distraction and drowsiness in different levels of automation (e.g., ADAS, maintaining
• driver’s readiness in SAE level 2 vs levels 0 & 1)
• To develop AI-based models to detect, monitor and predict the precursors of distraction and drowsiness among drivers
• To understand the relationship between distraction/drowsiness and other risky driving behaviours such as speeding, short headways,
• delayed reaction time etc.

Expected results:

• A list of factors as precursors (or predictors) of drowsiness and distraction
• Cutting-edge analytical models for predicting distraction and drowsiness and their impact on road safety
• Recommendations about distraction and drowsiness in the presence of technology, AI, and automation

Planned secondment(s):

• TUD-NL, Purpose: to enrol in DE programme and review state-of-the art and methodology development