# Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
## 1 Introduction
In this paper, we propose to respond to the three following challenges during elaborating the intelligent estimators/monitors based on BN:  
* a simple (friendly) way to generate their BN specifications
* a design methodology to elaborate the efficient BN implementation on parallel FPGA support
* a practical way to incorporate the BN monitors into the decision-making mechanism for the mission management of an autonomous vehicle. Compared to the literature, our proposal shows different advantages at different levels. At the model level, we propose different BN structures to define the diagnosis and the error mitigation modules. These models can be easily elaborated from FMEA-like tables using design pattern techniques. The BN models and the way to specify them present an advantage compared to [7; 8] where the BNs are only elaborated by expertise. Our proposal does not need any BN expertise. This paper proposes also a design methodology to implement the BN monitors onto FPGA. The main advantage of this methodology compared to the literature is a proposal of a complete design flow from FMEA-like tables towards FPGA support. In [10], a dedicated FPGA tool is proposed but does not take as input high-level specifications such as FMEA-tables and does not propose a complete design flow. To elaborate efficient implementations, we also propose specific and original optimizations for the BN structures that can be applied in a systematic way. The last point concerned the BN integration into a decision-making engine. In [9], the authors use Decision Networks to express the mission planning but no systematic method to elaborate them is proposed. In this paper, a methodology to design Decision Networks for the mission planning is presented and it can be extended to other models based on MDP. This methodology offers an easy way to specify the mission that can incorporate BN monitors, no equivalent methodology is proposed in the literature. The disadvantage of this methodology is that it is limited to some patterns that we have selected and it does not take into account for now the historical variations of the evidence to make some predictions or to reinforce the value of the diagnosis. To validate the monitors and the mission planning elaborated by our methodology, we present different study cases that show also the scalability of our proposal. In Section 2, we focus on some BN models for the diagnosis and the error mitigation. These ones can help to elaborate monitors for some physical or functional elements of the system such as sensors and applications. In Section 3, we focus on the methodology to automatically build these BN monitors from FMEA-like tables and on the methodology to generate an embedded real-time version onto FPGA. Section 4 shows how to incorporate them into a global view of decision-making for the case of the mission management of an autonomous vehicle. This integration is proposed using two possible decision-making models: DN model and MDP model. In Section 5, we show through examples that the BN implementations onto FPGA are efficient and low-power and present some co-design experiment for decision-making modules incorporating BN monitors at the mission management level. Section 6 opens a discussion on the BN parameters and on the cooperation between the diagnosis and decision-making modules at the mission management level.