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

Abstract

<jats:p>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.</jats:p>

Development of a Test-Bench for Evaluating the Embedded Implementation of the Improved Elephant Herding Optimization Algorithm Applied to Energy-Based Acoustic Localization

“…This indicator shows that for all modules, iEHO computation time is around 10% of EHO for N = 6. When increasing the number of sensors reaching N = 15, still the iEHO computation time reduction falls in the interval [10,25]% of EHO, where higher values match greater values of the measurement noise.…”

“…Embedded systems are widely spread in our daily life. They are a vital component of larger structures such as wireless sensors networks (WSNs) [1], Internet of Things (IoT) [2], automotive electronics [3], home automation [4], energy management [5][6][7], noise monitoring [8,9], autonomous vehicles [10,11], among several others. Elecia White defines an embedded system as " a computerized system that is purpose-built for its application" [12].…”

“…The state-of-the-art comprises some platforms that have been proposed in the context of an acoustic acquisition. Nonetheless, localization processing is mostly done offline, on a central computing unit, or when considered locally, on complex and expensive devices such as field-programmable gate arrays (FPGAs) [9,10]. Some proposals of hardware platforms that have been presented in the context of acoustic acquisition can be found in [21] but with the need for complex mathematical processing.…”

PFDA-FMEA, an Integrated Method Improving FMEA Assessment in Product Design

“…Some authors mix methodologies to improve it and make the FMEA application robust for their processes under study. Dezan et al [14] used Bayesian Network and FMEA for the decision making logic, to improve safety in autonomous vehicles. Rastayesh et al [15] used the FMEA methodology as a basis to analyze the failures in hybrid energy systems; they include the Failure Tree Analysis (FTA) to identify risks between the different electronic components, identifying the critical components in the hybrid energy system.…”

Editorial for the Special Issue on “Bayesian Networks: Inference Algorithms, Applications, and Software Tools”

“…Simulated accident sequence data are used as evidence during DBN inference; the results can serve as a training tool for CES operators to better prepare for accident scenarios. Autonomous vehicles, taking the environmental context into account, are another application field documented in [3], where BNs help in Fault Detection Isolation and Recovery (FDIR) in real-time conditions. The models are automatically generated from Failure Mode and Effects Analysis (FMEA) and operate online for autonomous vehicles.…”