## 7 Discussion, Limitations, and Future Research Trends  
### Discussion  
Recent literature highlights the increasing use of DL approaches for OD, yielding promising results under typical conditions. However, there is a pressing need for further advancements, particularly in adverse weather and complex scenarios. Enhancing OD in such conditions is crucial, especially for AVs, to prevent accidents and ensure safety. In the following discussion, we delve into the key insights and implications drawn from our research on OD in adverse weather conditions for AVs. Our research has explored both traditional and DL methods for AV object detection, focusing on vehicles, pedestrians, and road lanes. Traditional methods, despite their foundational role, struggle with high \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Approach** & **Practical Implications** & **KPIs** & **Challenges** & **Addressing Challenges** \\ \hline \multirow{4}{*}{[145]} & \multirow{4}{*}{CNN-based Human Detection at Nighttime for AVs} & Enhances pedestrian detection in low-light conditions, improving safety and navigation for AVs. & Detection Accuracy, False Negative Rate, Computational Efficiency & Limited visibility, background noise interference & Utilizes CNNs trained on nighttime images to recognize pedestrian features, potentially reducing false negatives. \\ \hline \multirow{4}{*}{[147]} & \multirow{4}{*}{Optimized Mask R-CNN for Low-Light Pedestrian Detection Detection at Nighttime for AVs.} & Talors the Mask R-CNN algorithm for low-light environments, maintaining high detection rates. & Detection Accuracy, Precision, Recall, Real-time Processing & Optimization for low-light conditions, computational complexity & Adjusts model parameters, uses data augmentation, and employs hardware acceleration for real-time processing. \\ \hline \multirow{4}{*}{[152]} & \multirow{4}{*}{YOLO-v5 for Pedestrian Detection and Nighttime} & Demonstrates the versatility of YOLO-v5 across different lighting conditions, ensuring consistent performance. & Detection Speed, Accuracy, Robustness & Balancing speed with accuracy in varying lighting & Fine-tunes the YOLO-v5 model on diverse datasets, ensuring generalization across different lighting scenarios. \\ \hline \multirow{4}{*}{[153]} & \multirow{4}{*}{DL Approaches for Adverse Detection Detection} & Improves detection reliability in adverse weather, crucial for AVs safety and functionality. & Detection Accuracy, Robustness, System Reliability & Degradation of performance due to weather conditions and integrate weather conditions & Uses DL models that learn to recognize patterns despite weather distortions and integrate multi-sensor data for enhanced detection. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Practical implications of DL approaches. computational demands, slow processing, and occasional misidentification. DL, on the other hand, excels by learning complex patterns from data, offering faster and more precise detection, especially in challenging weather conditions. This makes DL a more effective and adaptable solution for AV systems. Traditional vehicle detection techniques, which rely on appearance- and motion-based algorithms, face significant challenges in adverse weather conditions. Appearance-based algorithms like HOG, Haar-like, and SIFT are sensitive to exterior objects, adverse weather, and lighting variations, making them vulnerable in situations like heavy rain or fog. These algorithms often struggle to maintain accuracy due to the reduced visibility of vehicle features and the difficulty in differentiating vehicles from their surroundings. Motion-based detection methods, which track objects based on movement relative to the camera, also encounter issues in adverse weather. They can have trouble distinguishing between moving vehicles and other dynamic elements, such as pedestrians or debris, especially when motion cues are obscured by weather conditions.