This document describes a study that aimed to develop a fit-for-duty test to estimate drivers' sleepiness levels. The study combined a sleepiness model with eye movement performance tests. Results showed that combining the two approaches improved performance over using each method individually. However, the integrated test still struggled with individual differences and other variables. The researchers concluded that more work is needed to create a reliable and robust fit-for-duty test that can accurately assess driver sleepiness.

1. Fit-for-duty test for estimation
of drivers' sleepiness level
Christer Ahlström1, Marcus Nyström2, Kenneth
Holmqvist2, Carina Fors1, David Sandberg1, Anna
Anund1, Göran Kecklund3, Torbjörn Åkerstedt3
1Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden
2Humanities Lab, Lund University, Lund, Sweden
3Stress Research Institute, Stockholm University, Stockholm, Sweden

2. Motivation
- 10 and 20% of all crashes are sleep related
- In Sweden
- 38 % of all fatal crashes on motorway are sleep related
- 33 % of all crashes with slight and severe injuries are sleep related
- About 43 % of insurance claims (large property damage) are single-
driver accidents

3. How to measure sleepiness??
- General proneness to fall asleep
- Multiple sleep latency test
- Maintenance of wakefulness test
- Polysomnography
- Sleepiness in active individuals
- Electrophysiology
- Imaging techniques (fMRI, …)
- Practically feasible techniques
- Subjective self rating of sleepiness
- Subjective observer rating of sleepiness
- Sleepiness models
- Behaviour
- Balance tests
- Eye movements

4. Common problems in sleepiness assessment
- Individual differences
- Covariates
- Voluntary/involuntary sleepiness
- Noise
- …

5. Project idea
- Device a test to see if the driver is fit to drive
- Combine a model of sleepiness with an eye movement performance test.
- Strengthen both methods by adding complementary information.
- Reduce the impact of individual differences
Data
Fusion
Fit to drive?

6. Sleepiness model
- Homeostatic effects of time awake and amount
of prior sleep (S)
- Circadian component that represents the
influence of the biological clock (C)
- Ultradian component (U)

7. Eye movement tests
- Pro-saccades
- Anti-saccades
- Smooth pursuit
- Fixation stability
- Pupillometry

8. Example, anti-saccades

9. Study design
- 24 participants
- Four eye tracking measurements over 24h
- Three driving sessions in real traffic

10. Results
- Differences between recording sessions (time of day)
- Estimate current KSS (three level scale)
- Predict future dangerously high KSS levels (KSS<8 → 0, KSS≥8 → 1)
Recording 1 Recording 2 Recording 3 Recording 4 Session
(df =3)
KSS (1-9) 4.09 ± 1.63 4.09 ± 1.15 5.50 ± 1.22 7.32 ± 1.04 47.78
(<0.001)
Current 1.18 ± 0.39 1.09 ± 0.29 1.55 ± 0.60 2.36 ± 0.58 44.29
sleepiness level (<0.001)
Future 0.23 ± 0.43 0.23 ± 0.43 0.86 ± 0.35 44.20
sleepiness level (<0.001)
Fit for duty test 1.18 ± 0.14 1.09 ± 0.16 1.69 ±0.28 2.19 ± 0.28 3550.04
estimate KSS (<0.001)
Fit for duty test 0.31 ± 0.15 0.20 ±0.19 0.83 ±0.16 1.00 ± 0.01 1302.49
predict KSS (<0.001)

11. The whole truth?
- Cross-validated estimation of the current KSS value (three-level
scale)
3.5
3
2.5
Predicted values
2
1.5
1
0.5
1 2 3
KSS (1-3)

12. The whole truth?
- Cross-validated prediction of driving sessions where the driver
reach dangerously high KSS values (KSS≥8).

13. Conclusions
- The model works well in general, but is unable to cope with individual
differences, situation variables and context variables.
- Some eye movements works well in general, but suffers from individual
differences and covariates.
- Bringing the two approaches together improves the performance.
- Depending on how you present the results, you get a different impression of
the performance.
- A fit for duty test needs to be reliable and robust – we are not there yet.

14. Thank you for listening!
Christer Ahlström
christer.ahlstrom@vti.se
Many thanks to Trafikverket, Saab, Autoliv
and Vinnova for supporting this study.