# Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
## 7 Conclusions  
The BN model is interesting to elaborate intelligent FDIR monitors incorporating the diagnosis and the error recovery modules. These ones can monitor the different elements of the system (sensors, hardware component, memories) but also the applications. Some system constraints (timing constraints and resources constraints) can also be introduced into these BN models. As the direct specification of the BN is not so easy, we propose a friendly method driven by pattern recognition and FMEA-like table specification to elaborate them. In a second part, we propose an efficient HW implementation of the BN monitors onto FPGA-SoC platform. The FPGA implementation can present a real interest for \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **MDP-Navigation** & **MDP-Landing** & **MDP-Tracking** \\ \hline BRAM & 4\% & 4\% & 5\% \\ DSP & 10\% & 12\% & 4\% \\ FF & 8\% & 9\% & 10\% \\ LUT & 19\% & 21\% & 23\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: FPGA resources for the MDP implementations. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MDP-Navigation** & **MDP-Landing** & **MDP-Tracking** \\ \hline Speed up & 2.86 & 1.22 & 1.14 \\ Execution time (SW clock frequency 1 GHz) & 2.7 ms & 3.1 ms & 8.6 ms \\ Execution time (HW clock frequency 100 MHz) & 0.9 ms & 2.6 ms & 7.6 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 9: MDP speed up and SW/HW execution time. 2 reasons: speed-up and online updates. We give significative examples. We show with a BN monitor of 270 nodes that the corresponding FPGA implementation can offer a speed-up upto 16 compared to the SW version. It can be useful for a real-time monitoring. With an FPGA implementation, we are able to provide also non-intrusive monitors and we are able to perform the online updates by modifying the parameters of the networks, if necessary. Endly, the BN monitors can be incorporated into a decision-making model such as Decision Network or MDP to elaborate a high-level decision-making process of the mission manager. Embedded implementations of the cooperative diagnosis-decision making mechanism are also proposed on a FPGA-SoC support. We show that a co-design approach can provide an interesting trade-off for the decision-making implementation of the mission manager: diagnosis in HW and pure decision-making mechanism in SW. Actually, the BN monitors are the part that can mainly take benefit of an FPGA implementation while the decision-making mechanism is not so critical to be HW implemented. As future works, we will consider the distributed approach of the BN monitors in a context of swarms including both the safety and the security constraints. In the case of a single drone, we have already explored a concurrent MDP specification incorporating BN monitors. This model can be applied for a cooperative mission between UAVs. Nevertheless, different challenges are still to be addressed. The first one concerns the adaptability of the UAV networks concerning a decision. This adaptability is necessary because the environmental context of the mission can change in real-time (strong wind, unexpected obstacle), because some security threats can be detected (GPS spoofing, deny of service,...), and because some failure may appear at different levels (sensor, motor, autopilot, companion board). To adapt the decision inside a swarm, we need to organize the global decision through the local ones. This means that the priorities should be fixed at the network level to make each single decision more appropriate and this must be done at real-time. Another challenge is the real-time feature that can be supported by an specific parallel hardware platform such as FPGA-SoC board. Our future work will propose a new methodology and new embedded implementations of the decision-making engine to be distributed onto a swarm in an adaptive and real-time way that includes the local monitors of each UAV. Predictions and learning are also under considerations to improve the BN monitoring and the decision-making. Conceptualization, C.D.