# Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
## 1 Introduction  
Bayesian Networks (BNs) are well known probabilistic models to elaborate diagnosis taking into account uncertainties. Numerous fields of applications like medicine, robotics, industrial process, exploit their inference mechanism to evaluate the causes of a disease or the source of the malfunctioning from the observed environment. With the autonomy of unmanned aerial vehicles (UAV), the safety becomes a major issue to achieve the mission correctly. Then the analysis and the evaluation of the dependability of the system [1] including the safety and the security constraints are crucial. In the case of aerospace systems, a set of faults and errors [2] related to sensors, actuators and embedded systems can be identified in advance. To detect and to mitigate the different kinds of hazards, number of model-based fault detection isolation recovery (FDIR) techniques have been introduced [3]. The BNs are used in safety and risk analysis/assessment. The superiority of the BN over conventional risk analysis methods such as the Bowtie method and Fault tree are well proven in many literatures [4; 5; 6]. In [7; 8; 9], the FDIR techniques use static or dynamic BNs in order to define the Health Management (HM) of the system from sensor observation. Other works [10; 11] in security show that BNs can be also used to model particular security threats. The BN model can be an interesting model for elaborating intelligent monitors for autonomous vehicles. The elaboration of the appropriate BN able to fit the dependable system is a complex task and is generally based on expert knowledge or on the learning algorithms [12; 13; 14]. Learning algorithms can help in defining the parameters and the structure of the networks. Nevertheless, they are really inefficient if not driven by some expertise. The first challenge is then to elaborate these ones without being a BN expert and without using extensive and inefficient learning methods. The second challenge in the context of autonomous vehicle is to embed these intelligent monitors in the real-time context. Actual UAV systems have the opportunity to embed parallel application by deploying them onto specific parallel architecture such as multi-core CPU, GPU or FPGA architecture. These hardware supports are currently proposed for the real-time vision application [15]. The BN monitors can be described in a parallel way and can be efficiently implemented on FPGA support. A hardware implementation on FPGA of the HM is proposed to achieve real-time objectives and non intrusive monitoring [16; 17]. The efficiency of their FPGA implementation relies on the Arithmetic Circuit (AC) compilation [18], proposed for the BN inference, which gives a linear time computational complexity for real-time execution. The main challenge considering this kind of implementation is the design tool/methodology for an easy and a scalable FPGA implementation. When embedded real-time BN estimators/monitors are available, the next step is to incorporate them into the decision-making module for an operational mission planning. For the mission planning, there are different decision-making models that can be compatible with the BN monitors such as Decision Networks (DN), Markov Decision Process (MDP), POMDP,...As Decision Networks is an extension of BN, the integration of BN monitors to this model is straightforward. For MDP model, the BNs can be used for the transition function between states. The interaction between the BN diagnosis and the decision-making model in the context of the mission management can be challenging in terms of functional distribution and operative organization onto the system.