2 research outputs found
Multimodal Polynomial Fusion for Detecting Driver Distraction
Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone.
Although there has been a considerable amount of research on modeling the
distracted behavior of drivers under various conditions, accurate automatic
detection using multiple modalities and especially the contribution of using
the speech modality to improve accuracy has received little attention. This
paper introduces a new multimodal dataset for distracted driving behavior and
discusses automatic distraction detection using features from three modalities:
facial expression, speech and car signals. Detailed multimodal feature analysis
shows that adding more modalities monotonically increases the predictive
accuracy of the model. Finally, a simple and effective multimodal fusion
technique using a polynomial fusion layer shows superior distraction detection
results compared to the baseline SVM and neural network models.Comment: INTERSPEECH 201
Driver distraction detection using experimental methods and machine learning algorithms.
Driver distraction causes numerous road accidents, which is
approximately equal to 25% of the total crashes according to the
reports by the National Highway Traffic Safety Administration.
Warnings can be helpful to mitigate the risks caused by driver
distraction. Previous studies on driver distraction detection have not
sufficiently found relevant input features to filter insignificant
information, thus limiting the improvement of efficiency. Moreover, the
disadvantages of driving simulators and public roads pose a
challenge in collecting suitable data for feature identification and
comparisons of performance among driver distraction detection
algorithms. While the previous research focuses on improving
prediction accuracy, shortening the prediction time is critical in giving
timely warnings to drivers.
This thesis aims at detecting driver distraction, which could provide
faster and accurate warnings to drivers. The developed method is
implemented by cutting the redundancy and irrelevant information fed
to the algorithms and instead selecting suitable algorithms that
achieve the balance between the prediction accuracy and prediction
time. Moreover, a closed testing field supplies an environment for
collecting more accurate information to identify the relevant features
and to determine suitable algorithms.
In this study, open-source data and experimental data are used. The
results show that a balance between the prediction accuracy and the
prediction time is achieved by feeding the relevant features and using
suitable machine learning algorithms (e.g. Decision Tree). Compared
with existing state-of-the-art methods, the prediction accuracy of the
method proposed in this study has reached approximately the same
level. More importantly, the efficiency has improved, including
reduced prediction time and fewer input features. Consequently, less
computer storage is used.PhD in Transport System