# A Multimodal Recurrent Model for Driver Distraction Detection
## 4 Discussion  
The validation results obtained from both the hold-out method and the leave-one-driver-out cross-validation provide valuable insights into the performance of the proposed models. The hold-out evaluations suggest a slight trend indicating that the inclusion of a temporal component improves the reliable detection of distractions. However, in certain cases, the sequential approach using a simple CNN outperformed more complex models in terms of classification accuracy. The evaluation results did not conclusively prove the superiority of recurrent neural networks, such as LSTM networks, for distraction detection. In particular, the hold-out method, which maintains a consistent split between training and validation, showed high variability in results across repeated training sessions. This was particularly evident when analyzing metrics for individual classes, where significant standard deviations were observed. This variability is likely due to the stochastic nature of neural networks, leading to different local minima during each training run.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **CNN** & **CNN Sequential** & **CNN-LSTM** & **CNN-LSTM-S** \\ \hline \multicolumn{5}{c}{validation driver: driver\_0} \\ Accuracy & 0.13 & 0.12 \((-0.01)\) & 0.83 \((+0.70)\) & 0.15 \((+0.02)\) \\ Precision & 0.49 & 0.64 \((+0.15)\) & 0.73 \((+0.24)\) & 0.26 \((-0.23)\) \\ Recall & 0.33 & 0.38 \((+0.05)\) & 0.71 \((+0.38)\) & 0.31 \((-0.02)\) \\ F1 Score & 0.12 & 0.10 \((-0.02)\) & 0.56 \((+0.44)\) & 0.13 \((+0.01)\) \\ Balanced Accuracy & 0.33 & 0.38 \((+0.05)\) & 0.71 \((+0.38)\) & 0.31 \((-0.02)\) \\ \hline \multicolumn{5}{c}{validation driver: driver\_1} \\ Accuracy & 0.91 & 0.94 \((+0.03)\) & 0.98 \((+0.07)\) & 0.99 \((+0.08)\) \\ Precision & 0.88 & 0.90 \((+0.02)\) & 0.97 \((+0.09)\) & 0.98 \((+0.10)\) \\ Recall & 0.73 & 0.80 \((+0.07)\) & 0.94 \((+0.21)\) & 0.95 \((+0.22)\) \\ F1 Score & 0.76 & 0.81 \((+0.05)\) & 0.95 \((+0.19)\) & 0.96 \((+0.20)\) \\ Balanced Accuracy & 0.73 & 0.80 \((+0.07)\) & 0.94 \((+0.21)\) & 0.95 \((+0.22)\) \\ \hline \multicolumn{5}{c}{validation driver: driver\_2} \\ Accuracy & 0.87 & 0.92 \((+0.05)\) & 0.89 \((+0.02)\) & 0.94 \((+0.07)\) \\ Precision & 0.84 & 0.94 \((+0.10)\) & 0.83 \((-0.01)\) & 0.89 \((+0.05)\) \\ Recall & 0.67 & 0.76 \((+0.09)\) & 0.76 \((+0.09)\) & 0.86 \((+0.19)\) \\ F1 Score & 0.73 & 0.82 \((+0.09)\) & 0.75 \((+0.02)\) & 0.87 \((+0.14)\) \\ Balanced Accuracy & 0.67 & 0.76 \((+0.09)\) & 0.76 \((+0.09)\) & 0.86 \((+0.19)\) \\ \hline \multicolumn{5}{c}{validation driver: driver\_3} \\ Accuracy & 0.93 & 0.96 \((+0.03)\) & 0.96 \((+0.03)\) & 0.97 \((+0.04)\) \\ Precision & 0.88 & 0.93 \((+0.05)\) & 0.96 \((+0.08)\) & 0.98 \((+0.10)\) \\ Recall & 0.80 & 0.87 \((+0.07)\) & 0.90 \((+0.10)\) & 0.91 \((+0.1 Leave-one-driver-out cross-validation was used to evaluate the sensitivity of the models to the specific combinations of driver data across training and test sets. The results showed that different validation drivers occasionally produced different results. For example, all models except the _CNN-LSTM_ model struggled with validation data from a driver experiencing difficult lighting conditions. However, this form of validation highlighted a trend where time-dependent LSTM networks outperformed the CNN models, both with and without a sequential approach. In addition, the leave-one-driver-out cross-validation suggested that the LSTM network incorporating sensor data tended to outperform the LSTM network processing only image sequences.  
Despite these findings, it remains inconclusive whether the inclusion of sensor data significantly improves prediction performance. Further research is needed to draw definitive conclusions about the impact of sensor data on driver distraction detection. Such research should include a larger dataset with a more diverse set of drivers and a greater emphasis on feature engineering. In addition, a broader range of model architectures would need to be explored to reliably address this issue.