# Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
## 5 Study Cases
### BN Generation Examples
#### 5.1.2 Example 2 Application Monitoring with Mitigation: Tracking Application Example  
The FMEA table contains the possible errors that can decrease the QoS of the tracking application considering the context. Depending on environment factors, different parameters or versions of the tracking algorithm can be considered to achieve a good QoS, as shown in Table 6.  
Figure 20 shows the translation of the FMEA Table 6 of the tracking application into a BN model. The root node represents the QoS of the tracking application to maintain when an error context occurs. The **U_nodes** indicate the unobservable state of the errors of vibrations (**U_Vibration**), motion vector (**U_Loss motion vector**) and error of point tracking (**U_Loss point tracking**). These types of errors are monitored by physical (sensors) or software measurements denoted by **S_nodes** as (**S_IMU**, **S_Model**,...) and appearances context. Monitor nodes can also have a health status indicated by the **H_S_node** as (**H_S_M**) in a certain appearances context **A_H_nodes** (e.g., **Small R.O.I**). The **C_nodes** represent the solution proposals that can mitigate the observed type error (e.g., **C_Stabilization** in the case of vibrations error), and ensure to maintain the expected application QoS level.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Errors** & \begin{tabular}{c} **Possible** \\ **Monitoring** \\ \end{tabular} & \
\begin{tabular}{c} **Appearances** \\ **Context** \\ \end{tabular} &
\begin{tabular}{c} **Solution** \\ **(Algorithms)** \\ \end{tabular} \\ \hline Vibration & IMU (Inertial Measurement Unit) & Wind & Activate the \\  & Vibration sensor & Vibration & stabilization \\ \hline Tracking point & Model based on: & Drone speed & Improve the \\ lost & number of features & variations of & contrast \\  & detected (Harris) [36] & luminosity & \\ \hline Motion vector & Model based on: & Target speed & Raise the \\ lost & motion vector & Small R.O.I & R.O.I size \\  & between 2 images [36] & (Region Of Interest) & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Failure mode effects analysis applied to the tracking application.  
Figure 20: BN for tracking application with mitigation proposals.  
The mitigation capability of the BN is shown in Figure 20. The probabilities associated to the mitigation nodes (green nodes) are computed with the expected value of \(QoS\) equal to high. As mentioned previously, the nodes represent random variables. Each node has two states except the QoS node which has 3 states (high, medium, low).  
In this example, we observe a context with wind and target speed and we fix the probability (evidence) of QoS to be in the state \"high\". The wind introduces some vibrations and tracking error occurs. The target speedup can also lead to a wrong motion vector estimation if the R.O.I is too small regarding the target speed. These observations are reported in the BN by providing evidences on these context nodes, represented by the red nodes in Figure 20. We obtain a probability of 60% to activate the stabilization and 72% to activate new tracking version by increasing the window (R.O.I) size. In this example, the efficient solution is to increase the R.O.I size.