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

Hosts: TUD & PSA-LAB


  • 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