# How Does C-V2X Help Autonomous Driving to Avoid Accidents?
## 3 Key Technology for C-V2X VRUCW
### Ai  
Artificial Intelligence (AI) detection, object recognition and movement prediction for collision warning are used in our solution. In AI server, we implement SSD and ResNet-18 [25] network framework. SSD stands for Single Shot Detection, it means that AI server only needs to take one single shot to detect multiple objects within the image. Compared with the two-stage image detection, the inference and error rate are greatly reduced. The deep residual network (ResNet) is one of the most commonly convolution neural networks (CNNs) for the image feature extraction. ResNet-18 consists of 17 convolution layers and a fully connected layer as shown in Figure 7. This neutral network reduces the amount of calculation by using a 3 \(\times\) 3 small convolution kernel, so that the time consumption of handling process can meet the low latency required by C-V2X in 3 GPP Rel-14 [7].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Country** & **Spectrum (MHz)** & **Allocated Bandwidth (MHz)** \\ \hline Australia & 5855–5925 & 70 \\ China & 5905–5925 (trials) & 20 \\ Europe & 5875–5905 & 30 \\ Japan & 755.5–764.5 and 5770–5850 & 9 and 80 \\ Korea & 5855–5925 & 70 \\ Singapore & 5875–5925 & 50 \\ U.S. & 5850–5925 & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: ITS Spectrum [23].  
A confusion matrix [26] in Table 6 is a technique for summarizing the performance of a classification algorithm. Most widely-used metric is Equation (3):  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{3}\]  
But classification accuracy as an evaluation measure is not well-suited for imbalanced class. Detecting the rare class is usually more interesting (e.g., person with umbrella, baby carriage, etc). So we have alternative measures for classification performance evaluation in Equations (4)-(6).  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall}=\frac{2\times TP}{2 \times TP+FP+FN} \tag{6}\]  
According to Equations (4)-(6), two features person and vehicle are classified in testing set, experimental result is showed in Table 7.  
From Figure 8, the vulnerable road user as a detection result can be a single person, a baby carriage, car, or motorcycle, etc. No matter it is daytime or nighttime, these vulnerable road users can be protected by our C-V2X VRUCW solution. If any new target appears that does not belong to our existed AI database, but there is a possibility of collision between new target and autonomous vehicle, considering the risk of road accidents, new target will be treated as a person or a car in terms of default setting in our solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Actual Class** & **Predicted Class** & **Positive** & **Negative** \\ \hline
**Positive** & True Positive **(TP)** & False Negative **(FN)** \\
**Negative** & False Positive **(FP)** & True Negative **(TN)** \\ \hline \hline \end{tabular}
\end{table}
Table 6: 2-class Confusion Matrix [26].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Precision** & **Recall** & **F1** \\ \hline Person & 98.61\% & 91.79\% & 95.08\% \\ Vehicle & 95.73\% & 99.70\% & 97.68\% \\ Average & 97.84\% & 95.71\% & 96.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification Performance.  
Figure 7: ResNet-18 Network [25].