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Open AccessArticle

Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles

1
Laboratoire des Sciences et Technologies de l’Information, de la Communication et de la Connaissance (Lab-STICC), National Center for Scientific Research (CNRS), Université de Brest, 29200 Brest, France
2
Espace pour le Développement (Espace-Dev), Université de Guyane, 97300 Cayenne, France
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(7), 155; https://doi.org/10.3390/a13070155
Received: 13 May 2020 / Revised: 13 June 2020 / Accepted: 22 June 2020 / Published: 28 June 2020
Bayesian Networks (BN) are probabilistic models that are commonly used for the diagnosis in numerous domains (medicine, finance, transport, robotics, …). In the case of autonomous vehicles, they can contribute to elaborate intelligent monitors that can take the environmental context into account. We show in this paper some main abilities of BN that can help in the elaboration of fault detection isolation and recovery (FDIR) modules. One of the main difficulty with the BN model is generally to elaborate these ones according to the case of study. Then, we propose some automatic generation techniques from failure mode and effects analysis (FMEA)-like tables using the pattern design approach. Once defined, these modules have to operate online for autonomous vehicles. In a second part, we propose a design methodology to embed the real-time and non-intrusive implementations of the BN modules using FPGA-SoC support. We show that the FPGA implementation can offer an interesting speed-up with very limited energy cost. Lastly, we show how these BN modules can be incorporated into the decision-making model for the mission planning of unmanned aerial vehicles (UAVs). We illustrate the integration by means of two models: the Decision Network model that is a straightforward extension of the BN model, and the BFM model that is an extension of the Markov Decision Process (MDP) decision-making model incorporating a BN. We illustrate the different proposals with realistic examples and show that the hybrid implementation on FPGA-SoC can offer some benefits. View Full-Text
Keywords: Bayesian networks; fault detection isolation and recovery; failure mode and effects analysis; embedded diagnosis; error mitigation; unmanned aerial vehicles; mission planning; Markov Decision Process; high level synthesis design tool; field programmable gate array; System-On-Chip Bayesian networks; fault detection isolation and recovery; failure mode and effects analysis; embedded diagnosis; error mitigation; unmanned aerial vehicles; mission planning; Markov Decision Process; high level synthesis design tool; field programmable gate array; System-On-Chip
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Dezan, C.; Zermani, S.; Hireche, C. Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles. Algorithms 2020, 13, 155.

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