## 4 Experiments and Analysis
### Comparative Experiments of Existing Methods  
In Table 2, we summarized and compared our work with other relevant studies, utilizing the most commonly used metrics: precision, recall, and time-to-maneuver (TTM). Time-to-maneuver is defined as the time interval between the time of the model's prediction with the greatest confidence and the actual start of the maneuver. It is observed that our model's precision and recall are on par with the average levels. Table 3 presents the comparative results of the Brain4 Cars test set. We compared STA-NET with three widely used end-to-end studies [4; 13] because they have demonstrated better performance in driver intent recognition tasks than traditional machine learning \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Module**} & \multicolumn{1}{c}{**Accuracy \(\pm\) SD (\%)**} & \multicolumn{1}{c}{**F1 \(\pm\) SD (\%)**} \\ \hline
**CSAM and MCFM** & **ICELF** & & 81.35 \(\pm\) 0.52 & 80.39 \(\pm\) 1.63 \\ \(\surd\) & \(\surd\) & 82.16 \(\pm\) 5.18 & 83.73 \(\pm\) 2.96 \\ \(\surd\) & & 88.67 \(\pm\) 0.63 & 88.32 \(\pm\) 3.72 \\ \(\surd\) & \(\surd\) & **90.97 \(\pm\) 0.72** & **89.37 \(\pm\) 1.56** \\ \hline \hline \end{tabular} \(\surd\) indicates that this module was added, the bold indicates the final result of this work after adding all modules.  
\end{table} 
Table 1: The results of the ablation experiments on the Brain4 Cars dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Paper** & **Data Source** & **Method** & **Precision** & **Recall** & **TTM (s)** \\ \hline [PERSON] et al. [3] & in-cabin and external & AIO-HMM & 77.4\% & 71.2\% & 3.53 \\ [PERSON] et al. [11] & in-cabin and external & RNN-LSTM & 84.5\% & 77.1\% & 3.58 \\ [PERSON] et al. [8] & in-cabin and external & DA-RNN & 92.3\% & 90.8\% & 3.98 \\ [PERSON] et al. [7] & in-cabin and external & LSTM & 91.7\% & 90.7\% & 3.30 \\ [PERSON] et al. [41] & in-cabin and external & Dilated CNN & 91.8\% & 92.5\% & 3.76 \\ Ours & in-cabin and external & CNN + Trans & 90.8\% & 91.1\% & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The summary of the performance of related works on driver intention prediction. methods. In [4; 13], researchers applied video action recognition methods to driver intent prediction, such as 3 DResNet and ConvLSTM + 3 DResNet. All models were pretrained using the Kinetics-400 dataset [36], and all video data with a duration of 5 s were used as input. The table shows results for three different inputs, including only in-cabin driver maneuver videos, only external traffic scene videos, and both in-cabin and external videos. Average accuracy and F1 scores based on five-fold cross-validation are used to illustrate the performance of different methods, where \"SD\" denotes standard deviation. Based on Table 3, when exclusively using in-cabin driver maneuver data, all algorithms achieved satisfactory results, with accuracy ranging from 77 to 84%, making further improvements challenging. STA-NET achieved the highest accuracy of 90.97% and an F1 score of 89.37% when provided with dual perspectives--both inside and outside the vehicle. The experiment indicates that the accuracy of intent recognition increased by approximately 3.66% when the STA-NET model used both in-cabin driver maneuver and external traffic scene as inputs. This result strongly suggests that external traffic scene features and in-cabin driver maneuver features contain complementary information for driver intent recognition. Moreover, our model significantly reduces the number of parameters compared to previous methods. Given the potential computational costs associated with model complexity, models with low resource requirements are preferred for automotive applications. Our model achieves the extraction of valuable features with fewer parameters, facilitating easier deployment in resource-constrained environments such as onboard vehicle systems. The confusion matrix for the proposed method is illustrated in Figure 5, showcasing the classification performance for the five intents.