1. Mitchell Kossoris, Catelyn Scholl, Zhi Zheng
Classifying Physiological and Response Data to Detect Distracted Driving Events
Mitchell Kossoris, Catelyn Scholl, Zhi Zheng

2. Distracted Driving Dataset
Controlled driving simulator
68 volunteers
Same highway
4 driving stimuli types:
No stimuli
Cognitive stimuli
Emotional stimuli
Sensorimotor stimuli

3. Distracted Driving Dataset
Data collected:
Speed
Acceleration
Brake force
Steering
Lane position
Palm EDA
Heart Rate
Breathing Rate
Gaze position

4. Processing Normalization
Used to account for differences in each participant’s range of each feature
E.g. Person 1 has a resting heart rate of 70 bpm vs person 2 with 80 bpm

5. Processing - Interpolation
Forward fills missing data
Segments of less than 10 linear-filled
More data points to be analyzed
Less data removed

6. Processing - Data Removal
Continuous missing segments of data removed
Longer than 10 seconds
Fewer data inaccuracies over large segments

7. Processing - Rolling Mean
Mean of each consecutive 10 second interval
Dampen large differences caused by devices
Reduces outliers

8. Processing - Balancing
Ensures classifiers are not biased toward one class
Data is split evenly between classes
Trained and tested on balanced data

9. Classifiers K-Nearest Neighbor Support Vector Machines Random Forest
Naive Bayes
Neural Network

10. Evaluation
Accuracy
Baseline accuracy of our data, using a basic percentage of correctly-classified data points

11. Evaluation Mean Squared Error
Mean squared error regression loss between ground truth and estimated target values

12. Evaluation
F1 Scores
Weighted average of precision and recall where its best value is 1 and worst value is 0

13. Evaluation Confusion Matrix
Count of true positives, false positives, true negatives, and false negatives.

14. Evaluation K-Fold Cross Validation
The dataset was split into k consecutive folds
Each fold was then used once as a validation while the k-1 remaining folds formed the training set

15. Results Random forest: 99.38% accurate
K-Nearest Neighbor: 93.9% accurate
Support Vector Machines: 80.4% accurate
Neural Net: 79.2% accurate
Naive Bayes: 75.7% accurate