## 4 Experiments and Analysis
### Comparative Experiments of Existing Methods
The results indicate that lane-keeping and right lane change intent recognition achieved the most accurate results, with accuracy reaching 91.3% and 90.9%, respectively. The ability to recognize the intent to change lanes to the left is relatively lower, with an accuracy of approximately 70.8%, often confused with the intent to go straight. The accuracy for recognizing the intent to turn left is around 83.3%, and it is also prone to confusion with the intent to go straight. The accuracy for recognizing the intent to turn right is approximately 87.5%, with potential confusion with the intent to go straight and change lanes to the right. Through an analysis of misclassified samples, three main reasons are proposed. Firstly, for some lane-keeping maneuvers, drivers may be more inclined to perform left-check actions to ensure safe driving, making it easy to infer them as left lane change or left turn. Secondly, some right lane change intents are similar to maneuvers during lane-keeping, suggesting that drivers might occasionally perform right lane changes while maintaining their lane, although infrequently, potentially confusing the model. Thirdly, some right turn intents are very similar to maneuvers during right lane changes, which can confuse the model. These inferences suggest that in more complex traffic scenarios, drivers' maneuvers may adjust frequently based on the traffic conditions on both sides, posing a challenge for driver intent recognition. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Data Source** & **Param. (M)** & **Accuracy \(\pm\) SD (\%)** & **F1 \(\pm\) SD (\%)** \\ \hline \multirow{3}{*}{3 DResNet [4]} & in-cabin only & 240.26 & 83.10 \(\pm\) 2.5 & 81.7 \(\pm\) 2.6 \\  & external only & 240.26 & 53.20 \(\pm\) 0.5 & 43.4 \(\pm\) 0.9 \\  & in-cabin and external & 480.52 & 75.50 \(\pm\) 2.4 & 73.2 \(\pm\) 2.2 \\ \hline \multirow{3}{*}{ConvLSTM + 3 DResNet [13]} & in-cabin only & 46.22 & 77.40 \(\pm\) 0.02 & 75.49 \(\pm\) 0.02 \\  & external only & 160.41 & 60.87 \(\pm\) 0.01 & 66.38 \(\pm\) 0.03 \\  & in-cabin and external & 212.92 & 83.98 \(\pm\) 0.01 & 84.3 \(\pm\) 0.01 \\ \hline \multirow{3}{*}{**STA-Net (Ours)**} & in-cabin only & **60.56** & **87.31 \(\pm\) 0.56** & **85.32 \(\pm\) 1.71** \\  & external only & **60.56** & **71.12 \(\pm\) 0.58** & **73.10 \(\pm\) 0.63** \\  & in-cabin and external & **137.25** & **90.97 \(\pm\) 0.72** & **89.37 \(\pm\) 1.56** \\ \hline \hline \end{tabular}  
* The bold indicates the final result of this work.  
\end{table} 
Table 3: The comparison of different algorithms with different data inputs. Upon comparing our STA-NET's performance with the method described in [13], we acknowledge the variation in classification accuracy across different maneuvers. Specifically, the model in [13] exhibits superior performance in identifying left lane changes, left turns, and right turns, whereas STA-NET struggles with right lane change predictions. This discrepancy raises a pertinent discussion on the feasibility and potential advantages of employing a multi-method approach for driver maneuver prediction. Rationale for Multi-Method Approach:  
The diversity in driving behaviors and the complexity of road scenarios necessitate a nuanced approach to maneuver recognition. A single model may not optimally capture the intricacies associated with various maneuvers due to differences in visual cues, driver intentions, and environmental contexts. Therefore, leveraging the strengths of different predictive models based on the predicted maneuver could enhance overall performance and reliability. Methodological Considerations:  
To explore this possibility, we propose a framework where the predictive model dynamically selects between STA-NET and alternative methods, like the one presented in [13], based on the specific maneuver scenario. This selection could be informed by pre-defined criteria, such as the maneuver type, confidence levels of the predictions, or contextual factors like traffic density and road type. Potential Benefits:  
1.