## 7 Discussion, Limitations, and Future Research Trends
### Discussion
DL approaches are more accurate and perform better, as shown in Tables 4 and 5. A comparison of traditional and DL approaches is shown in Table 7. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Approaches** & **Technique** & **Superiority** & **Limitations** & **Additional Considerations** \\ \hline
[154,155] & Color-based Lane Detection & Traditional & Simple to implement, low computational cost & Limited in adverse weather, reliance on good weather conditions for high accuracy & Research into adaptive color models or fusion with other sensor data could improve performance in challenging conditions. \\ \hline
[158,159] & LiDAR Integration & Traditional & Reduces reliance on visual data, robust in various weather & High cost, sensitivity to unfavorable weather conditions. & Cost reduction and miniaturization of LiDAR sensors could make this technology more accessible for widespread use. \\ \hline
[160] & HSV Color Space & Traditional & Works well in daylight or with white light; uses color correspondence & Requires global thresholding, less effective in tunnel environments with distinct color illumination. & Enhancing the color model with machine learning could improve its adaptability to different lighting conditions. \\ \hline
[161] & SafeDrive Technique & Traditional & Utilizes historical data and vehicle location for lane detection in low-light areas & May not generalize well to all environments, relies on available historical data. & Incorporating real-time weather data and vehicle dynamics could improve the technique’s robustness. \\ \hline
[163] & CNN-based Real-time Road Condition Detection & DL & High accuracy with transfer learning automates real-time condition & Requires pre-trained models, may not adapt quickly to new conditions & Continuous learning and model updating could help maintain high accuracy as conditions change. \\ \hline
[165] & Lane Marker Detection in Rainy Weather & DL & Prioritizes feature selection to counteract rain-induced blurriness & May require extensive training data, complex model architecture & Using smaller, more specialized models could reduce computational demands and improve real-time performance. \\ \hline
[141] & Generative Adversarial Networks (GANs) & DL & Can generate realistic data for training, improve feature extraction & Requires significant computational resources, may struggle with certain types of adverse weather. & Research into GANs for adverse weather simulation could provide more robust training data for DL models. \\ \hline
[152] & Multispectral Imaging & DL & Combines different imaging modalities for improved detection & Requires specialized hardware; may be complex to integrate & Further development of multispectral imaging techniques could lead to more reliable detection systems. \\ \hline
[163] & Transfer Learning & DL & Allows models to adapt to new tasks with fewer data & May not perform as well as task-specific models if the transfer is not well-aligned. & Fine-tuning transfer learning models for specific AV tasks could enhance their effectiveness. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of traditional and DL approaches. Figure 13 presents the overall percentage distribution of papers, while Figure 14 describes the papers of traditional and DL approaches related to the studied three object detection issues. From Figure 13, one can see that vehicle detection is frequently studied using traditional and DL approaches, followed by pedestrian and road lane detection. As shown in Figure 14, DL approaches were more frequently used for all three issues in AVs compared to traditional approaches. Prior to 2008, traditional feature extraction methods were prevalent for detection and classification but had limitations in adverse conditions. Manual feature extraction made them less suitable for complex applications. DL has become much more popular and prominent than regular algorithms in recent times.