Abstract. This paper shows the utility to follow an architecture framework in order to design complex systems with a holistic approach. Multi-objective Optimization techniques extend and complete the architecture framework to support trade-off analysis and decision making in the Systems Engineering design process. The merging and combination of these two approaches, decision making and systems engineering, contribute to the efficient design of systems by helping to meet needs and constraints stemming mainly from the system analysis.To support this assertion, we present a case study for an Electric Vehicle Powertrain. The decision problem is modeled as a Pareto model, in order to find a solution for the Electric Vehicle Powertrain that maximizes its autonomy and minimizes its total cost of ownership.
Replacing the human driver to perform the Dynamic Driving Task (DDT)[1] will require perception, complex analysis and assessment of traffic situation. The path leading to success the deployment of fully Autonomous Vehicle (AV) depends on the resolution of a lot of challenges. Both the safety and the security aspects of AV constitute the core of regulatory compliance and technical research. The Autonomous Driving System (ADS) should be designed to ensure a safe manoeuvre and a stable behaviour despite the technological limitations, the uncertainties and hazards which characterize the real traffic conditions. In fully Autonomous Driving situation, detecting all relevant objects and agents should be sufficient to generate a warning, however the ADS requires further complex data analysis steps to quantify and improve the safety of decision making. This paper aims to improve the robustness of decision-making in order to mimic human-like decision ability. The approach is based on machine learning to identify the criticality of the dynamic situation and enabling ADS to make appropriate decision and fulfil safe manoeuvre.
ABSTRACT. We present in this paper an operational analysis of a complex system following a Model Based Systems Engineering approach, illustrated by a case study on electric vehicles. We explain some strategic issues and reasons that make electric vehicles important and complex systems, and how these vehicles can significantly contribute to the European policies for sustainable development. We explain why it is necessary to apply a Systems Engineering approach to deal with the complexity of such systems, and we give an overview of the architectural design framework we follow. We present a model of the system of interest and of its environment built from the analysis of public documents and literature reviews. This allowed us to identify the key stakeholders, external interfaces, needs, use cases and operational scenarios. Based on this operational analysis, we present ways to pursue functional and trade-off analyses.
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