## 4 Experimental Results and Analysis
### Road Data Extraction
#### 4.2.3 Traffic Signs Detection  
This section looks into traffic sign detection. These unassuming markers hold immense significance, shaping both route conditions and vehicle behavior. Their impact extends beyond visual cues, influencing road state and emissions. Figure 6: Traffic light detection showcasing examples from our dataset. (a) Red light detected. (b) Green light detected. Traffic signs act as dynamic regulators, directing motorist behavior and safety. Furthermore, they impact emissions discretely through speed restrictions and operational changes. This is not a monolithic environment; the many signs reflect the intricacies of modern highways. From speed limits to stop signs, each sign contributes to our overall awareness of the road. Traffic sign detection requires interpreting visual symbols and contextual meanings. This fusion of technology and human dynamics results in safer travel and less environmental effect. We adopted a detection strategy hinging on pre-trained YOLOv3 and CNN models [27]. This approach, introduced in 2020 and documented by two pivotal articles [28; 29], has consistently yielded robust outcomes. The architecture of this approach is shown in Figure 7. It commences with YOLOv3, proficient in detecting and identifying traffic signs. Subsequently, a CNN model undertakes the classification of these detected signs, attributing distinct labels to each. The progression of loss and mAP (Mean Average Precision) throughout the training process, involving a total of 8000 iterations, is noteworthy. These models were trained on the German Traffic Sign Detection Benchmark (GTSDB) dataset. This dataset, replete with authentic road sign images, encompasses 40 different classes and a total of 50,000 images (Stop, Yield, Speed limit, etc.). The progression of loss and mAP (Mean Average Precision) throughout the training process, involving a total of 8000 iterations, is noteworthy. Notably, Mean Average Precision (mAP) is a crucial metric in object detection tasks, serving as a comprehensive measure of a model's accuracy in localizing objects within an image and assigning accurate labels to them. It strikes a balance between precision, which assesses the accuracy of positive predictions, and recall, which measures the model's ability to identify all relevant instances. The mAP score is determined by calculating the Average Precision (AP) for each class and then averaging these values across all classes. The Average Precision is calculated using Formula (1):  
\[AP=\frac{\sum_{n}(P(n)\times\text{precision at }n)}{\text{Total number of positive predictions}} \tag{1}\]  
where \(n\) ranges over the sorted predictions by confidence score, and \(P(n)\) is an indicator function that equals 1 if the prediction at position \(n\) is a true positive, and 0 otherwise. Therefore, achieving a 97% mAP on the 5700 th iteration is a significant milestone. This high mAP value indicates that the model has excelled in both accurately localizing objects and assigning accurate labels, making it an optimal point to capture the parameters of the model. Subsequently, we applied this model to our gathered images, yielding commendable results and accurate detections, as shown in the figures below. Figure 8 illustrates a subset of the successful detections obtained from our image collection, and Table 3 shows the number of detections gathered from our real-world test cycles. Figure 7: Architectural workflow—YOLOv3 detection followed by CNN classification. Our main focus gravitates toward specific categories, notably encompassing speed limits spanning 20 to 120 km/h, stop signs, animal crossings, road working zones, and school  
Figure 8: Exemplars of traffic sign detection from our dataset. (**a**) Detection of animal crossing sign. (**b**) Detection of speed limit sign and yield sign.