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

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

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

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

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

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

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

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

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

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

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

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

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

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

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