The document discusses various techniques for detecting driver drowsiness, emphasizing the impact of drowsiness on road safety and productivity. It details approaches including physiological, behavioral, and vehicle-based methods, highlighting the effectiveness of convolutional neural networks (CNNs) and other machine learning models in estimating drowsiness and distraction. The paper concludes that behavioral measures, which require minimal device installation, are the most effective and non-invasive solutions for monitoring driver fatigue.

1. Driver Drowsiness Detection
Asaad Waqar
Former System Engineer
TUOL
asadwaqar16@gmail.com
+923009183381

2. Driver’s Drowsiness
• We have heard of many accidents that involve drivers lacking focus.
Most cases are due to the sleep factor(drowsiness)
• Psychological studies have demonstrated that drowsiness can
significantly impair productivity and the quality of work outcomes.
For example, drowsy driving, usually caused by sleep loss, nights or
very long working hours, is reported as one of the main causes of
serious accidents.
• We can divide the driver’s risky driving as:
– drunk driving
– distracted driving
– drug impaired driving
– drowsy driving
– unfastened seat belts, and speeding

3. Drowsiness & Distraction
• To overcome these mentioned problems
– driver alcohol detection system, distraction
detection system, drowsy detection system and
seat belt detection are actively studied.
• Besides drowsiness, distraction also plays its
part in accidents such as: looking away from
the drive way, not mentally active, using
mobile while driving, eating etc.

4. How to avoid this problem?
• Physiological measures
• Behavioral measures
• Vehicle-based s

5. 1. Physiological measures: Electrocardiogram (ECG),
electromyogram (EMG), and electroencephalogram (EEG),
start to change in earlier stages of drowsiness. However, it
is inconvenient to drive a vehicle wearing devices to
receive physical signals.
2. Behavioral measures: have relatively high accuracy and do
not cause discomfort to a driver when a device for
behavioral drowsiness detection is installed on the
vehicle. Such as installing a webcam and monitoring
through it. Therefore, this tech is actively studied in the
automotive industries
3. Vehicle-based: Use embedded sensors to sense the path
speed and direction of vehicle.
Not applicable in all regions, especially where there
are more hurdles and turns. E.g.: In Pakistan.

6. 1st Technique
• A neural-network-based investigation of eye-related movements
for accurate drowsiness estimation
• Focuses on drowsiness estimation by eye-related movements. That
includes eyelid movements Xel(t) and eyeball movements Xeb(t).
• Eye-related movements, e.g., eyelid movements and eye- ball
movements are reported to be important and useful for accurate
drowsiness estimation by previous studies. The former (el) is usually
described by the degree of eye closure, including eyelid droops and
blinks; while the latter (eb) often indicates gaze.
• CNN-Net and CNN- LSTM-Net, for better drowsiness modeling
• The experiments and results have shown, the eyeball movements
alone are not an effective enough feature to estimate drowsiness
while eyelid movements or joint movements (both) are strongly
related to drowsiness status.
– CNN-LSTM-Net result for eyelid movements
10s 30s 60s
CNN-LSTM 0.74 0.77 0.80

7. 2nd Technique
• Study on Training Convolutional Neural Network to Detect
Distraction and Drowsiness
• focuses on proposing a method to detect both distraction
and drowsiness using a single convolutional neural
network.
• uses single convolutional neural network model, Mobile
Net, to detect both distraction and drowsiness with eye
and mouth. It has less parameter to be trained.
• Experiments are performed on three datasets, A, B and C
• Dataset A consists of normal and distraction classes.
• Dataset B was the first attempt to train model that could
detect both distraction and drowsiness.
Dataset A Dataset B Dataset C
Precision 94.10% 91.26, 85.88% 95.75%

8. 3rd Technique
• The third technique involves driver drowsiness detection
based on multimodal using fusion of visual-feature and
bio-signal.
• In this method researchers combine physiological data and
visual data to see the effect of drowsiness.
• We use a deep learning network consisting of Long Short-
Term Memory (LSTM) to gather the driver’s condition.
• After extracting visual data through some device like
camera, different methods are used on the data to get
evaluable results like MTCNN (Multi-task Cascaded
Convolutional Networks) to extract feature.
• BMP to evaluate each pixel separately.
• Reconstruct data and then match it with original one

9. 3rd Technique
Work Accuracy
LSTM (eye) 79.1 (%)
LSTM (mouth) 67.2 (%)
LSTM ( bpm) 85.4 (%)
LSTM (multi-modality) 90.5 (%)

10. 4th Technique
• The fourth technique detects driver drowsiness
through 3D-deep convolutional neural network
(CNN) .
• This framework consists of four models: Spatio-
temporal representation learning, Scene
condition understanding, Feature fusion,
Drowsiness detection.
• This method is incorporating various driving
conditions i.e: a driving time such as day and
night, a driver’s appearance like wearing glasses
or sun glasses or night glasses.

11. Scenario Glasses and
Illumination
Head Mouth Eye
Day bare Face .99 .99 .98 .89
Day Glasses .97 .93 .95 .81
Day Sunglasses .98 .97 .78 .78
Night bare Face .99 .95 .97 .82
Night Glasses .97 .96 .88 .92
Average .98 .96 .912 .844
Total Average .924
4th Technique

12. 5th Technique
• The fifth technique detects drowsiness using
Multilayer Perceptron Classifier (MLP).
• This framework consists of five steps:
Extracting Videos from NTHU Database,
Extracting Images from Video Frames,
Extracting landmark coordinates from images,
Training the algorithm, Model extraction.

13. Category Accuracy
With glasses 84.848
Night Without glasses 81.40
Night With glasses 76.152
Without glasses 87.12276
With sunglasses 75.115
All 80.9274
5th Technique

14. 6th Technique
• The sixth technique detects drowsiness using
Filter-Pruned 3D Convolutional Neural Network.
• Drowsiness Detection Dataset (DDD) used in this
model that is collected by Weng et al from
National Tsing Hua university.
• This framework consists of four models for each
video: driver status (drowsy/stillness), eye status
(sleepy/stillness), head status
(nodding/looking/stillness), and mouth status
(yawning/talking/stillness).

15. 6th Technique
Method Drowsiness F1-score (%) Nondrowsiness F1-score (%) Accuracy (%)
Scaled Model 76.46 73.15 75.02
Scale-Pruned Model 76.55 73.22 75.10
Baseline 74.55 72.02 73.53
l1 norm-Pruned 73.26 70.56 72.21
Random-Pruned 66.84 63.75 65.79
Scale-Pruned Model +Smoothing 79.55 77.02 78.48

16. CONCUSION
• There are many techniques that are used and can be used for
drowsiness detection among which the most commonly used are
discussed in this paper, which involves behavioral measures,
machine learning techniques, eyelid moments.
• This paper provides the analysis of different techniques, although
many techniques exist but the main purpose of them is the same;
to detect any change in the facial expression of driver to detect any
drowsiness signs.
• The main focus of work is the behavioral measure because they are
non-invasive and do not require a large system to be installed in
vehicle.
• So we concluded from the differences among the performance of
techniques that convolutional neural networks performed better
than the other techniques