# A Multimodal Recurrent Model for Driver Distraction Detection
## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline  & **CNN** & **CNN Sequential** & **CNN-LSTM** & **CNN-LSTM-S** \\ \hline \multicolumn{4}{c}{class: drinking} \\ Precision & 0.82 (0.13) & 0.91 (0.12) & 0.71 (0.12) & 0.76 (0.12) \\ Recall & 0.79 (0.03) & 0.95 (0.02) & 0.94 (0.05) & 0.92 (0.05) \\ F1 Score & 0.80 (0.05) & 0.93 (0.06) & 0.80 (0.08) & 0.83 (0.07) \\ \hline \multicolumn{4}{c}{class: infotainment} \\ Precision & 0.79 (0.04) & 0.89 (0.04) & 0.83 (0.09) & 0.82 (0.08) \\ Recall & 0.76 (0.14) & 0.85 (0.14) & 0.57 (0.24) & 0.67 (0.22) \\ F1 Score & 0.77 (0.07) & 0.86 (0.07) & 0.65 (0.23) & 0.71 (0.14) \\ \hline \multicolumn{4}{c}{class: looking\_to\_passenger} \\ Precision & 0.91 (0.02) & 0.99 (0.02) & 0.93 (0.03) & 0.85 (0.06) \\ Recall & 0.40 (0.06) & 0.22 (0.08) & 0.58 (0.10) & 0.62 (0.21) \\ F1 Score & 0.56 (0.06) & 0.36 (0.12) & 0.71 (0.06) & 0.70 (0.14) \\ \hline \multicolumn{4}{c}{class: normal\_driving} \\ Precision & 0.91 (0.01) & 0.93 (0.01) & 0.95 (0.01) & 0.96 (0.02) \\ Recall & 0.98 (0.01) & 1.00 (0.00) & 0.99 (0.00) & 0.98 (0.01) \\ F1 Score & 0.95 (0.00) & 0.96 (0.00) & 0.97 (0.00) & 0.97 (0.01) \\ \hline \multicolumn{4}{c}{class: reaching\_behind} \\ Precision & 0.80 (0.11) & 0.89 (0.09) & 1.00 (0.00) & 0.91 (0.08) \\ Recall & 0.63 (0.09) & 0.74 (0.11) & 0.56 (0.05) & 0.63 (0.13) \\ F1 Score & 0.70 (0.04) & 0.80 (0.05) & 0.72 (0.04) & 0.74 (0.09) \\ \hline \end{tabular}
\end{table} 
Table 3: Results of different models on the validation set for different classes. Each cell represents the mean and standard deviations of the scores obtained from 5 training runs. All models are designed for multi-class classification, where the output corresponds to either a distraction class or the _normal_driving_ class. Consequently, in the previous evaluation metrics, errors between two distraction classes are weighted the same as errors between a distraction class and _normal_driving_. To evaluate the performance of the models when distinguishing only between distracted and normal driving, the problem was transformed into a binary classification task. However, no new models with modified architectures were trained specifically for this task. Instead, the results from multi-class classification were adapted to binary classification, which essentially aggregates all distracted classes. It is important to note that this approach is different from training new models explicitly designed to discriminate between these two classes. The overall results of the binary classification are in Table 4. Since confusion between different distraction classes is no longer a factor in binary classification, the results show some improvement over Table 2. The observed accuracies range from 92% to 96%, and the precision values are between 93% and 96%. Once again, the _CNN_ model had the lowest scores for all metrics, while the time-dependent approaches performed better. In addition, it can be observed that the LSTM-based models have slightly higher recall and balanced accuracy, both at 93%, compared to the sequential CNN approach. However, there is no significant difference between _CNN-LSTM_ and _CNN-LSTM-S_. Figure 7: Confusion matrices for the individual models. For each model type, the training run that achieved the median F1 score was selected. In addition to the hold-out validation, a leave-one-driver-out cross-validation was performed. The purpose of this cross-validation was to assess the sensitivity of the models to validation data from different drivers. The results of this method are visualized in Figure 8 using boxplots, which display various metrics for different models. Each boxplot represents the distribution of a particular score across different validation drivers. Notably, outliers can be observed in all metrics (except precision) for all models except _CNN-LSTM_. These outliers are consistently are associated with the same validation driver, indicating a failure of the models to generalize.