Challenges for Future Automotive Mobility

Grebe, Uwe Dieter; Hick, Hannes; Rothbart, Martin; Helmolt, R. von; Armengaud, E.; Bajzek, Matthias; Kranabitl, Philipp

doi:10.1007/978-3-319-99629-5_1

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…The new generation of vehicles is currently able to supply advanced assistance to the driver in their driving tasks [ 1 , 2 , 3 ] by playing an active role in safety issues, such as electronic stability control (ESC) [ 4 ], antilock braking (ABS) [ 5 ], and the newer Advanced Driver Assistance Systems (ADAS) [ 6 , 7 , 8 ], as well as by managing in-car navigation and positioning technologies based on Inertial Measurement Units (IMUs), Global Positioning Systems (GPSs), and map matching [ 9 , 10 , 11 ]. Moreover, vehicles are already connected [ 12 , 13 , 14 ], since they can link with smartphones, providing emergency roadside assistance, real-time traffic alerts, and circulation [ 15 , 16 , 17 , 18 ], laying the basis for the effective and functional development of Intelligent Transport Systems (ITSs), or “smart mobility” [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Responsiveness and Precision of Digital IMUs under Linear and Curvilinear Motion Conditions for Local Navigation and Positioning in Advanced Smart Mobility

Chiominto,

Natale,

D’Emilia

et al. 2024

Micromachines

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Sensors based on MEMS technology, in particular Inertial Measurement Units (IMUs), when installed on vehicles, provide a real-time full estimation of vehicles’ state vector (e.g., position, velocity, yaw angle, angular rate, acceleration), which is required for the planning and control of cars’ trajectories, as well as managing the in-car local navigation and positioning tasks. Moreover, data provided by the IMUs, integrated with the data of multiple inputs from other sensing systems (such as Lidar, cameras, and GPS) within the vehicle, and with the surrounding information exchanged in real time (vehicle to vehicle, vehicle to infrastructure, or vehicle to other entities), can be exploited to actualize the full implementation of “smart mobility” on a large scale. On the other hand, “smart mobility” (which is expected to improve road safety, reduce traffic congestion and environmental burden, and enhance the sustainability of mobility as a whole), to be safe and functional on a large scale, should be supported by highly accurate and trustworthy technologies based on precise and reliable sensors and systems. It is known that the accuracy and precision of data supplied by appropriately in-lab-calibrated IMUs (with respect to the primary or secondary standard in order to provide traceability to the International System of Units) allow guaranteeing high quality, reliable information managed by processing systems, since they are reproducible, repeatable, and traceable. In this work, the effective responsiveness and the related precision of digital IMUs, under sinusoidal linear and curvilinear motion conditions at 5 Hz, 10 Hz, and 20 Hz, are investigated on the basis of metrological approaches in laboratory standard conditions only. As a first step, in-lab calibrations allow one to reduce the variables of uncontrolled boundary conditions (e.g., occurring in vehicles in on-site tests) in order to identify the IMUs’ sensitivity in a stable and reproducible environment. For this purpose, a new calibration system, based on an oscillating rotating table was developed to reproduce the dynamic conditions of use in the field, and the results are compared with calibration data obtained on linear calibration benches.

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Section: Introductionmentioning

confidence: 99%

Responsiveness and Precision of Digital IMUs under Linear and Curvilinear Motion Conditions for Local Navigation and Positioning in Advanced Smart Mobility

Chiominto,

Natale,

D’Emilia

et al. 2024

Micromachines

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“…As for the complexity of the entire transportation industry, automotive mobility has significant transformations, which are driven mainly by technical innovations, such as autonomous driving, electrification, internet of things, or new legislative standards [1]. Due to digital transformation of automobiles and the whole mobility system, the number of integrated software-based functions has been increasing rapidly [2].…”

Section: Introductionmentioning

confidence: 99%

Systems Engineering Based Sustainability Improvement in Automotive Product Development

Kolossváry¹,

Németh²

2023

JEMSE

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Affected by new trends, automobile companies have altered stakeholder requirements on their main product -the automobile. With enactment of new regulations concerning sustainability, new features appeared quickly, such as electrification, sharing services, autonomous mobility and so on. In this study, we present sustainability as a stakeholder and analyze the method of its realization in Systems Engineering (SE) based product development. Formula SAE provides a validated setting to conduct experiments on integrating sustainability into the classical product requirement architectures. By taking into consideration the use of SE or adding other methodological frameworks, findings can establish a new setting in sustainability research. The results of this study may be enlightening for scholars and practitioners and calls for further research on embedding sustainability requirements in automotive product development by using SE.

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“…In addition, stakeholder requirements regarding functionality, quality, and cost-efficiency are increasing [1]. These challenges are increasingly being met by four key trends [2]: electrification, autonomous driving, connected vehicles, and shared mobility. All these trends require the use of mechanical, electronic, and software systems onboard the automobile.…”

Section: Introduction 1motivationmentioning

confidence: 99%

Optimization Workflows for Linking Model-Based Systems Engineering (MBSE) and Multidisciplinary Analysis and Optimization (MDAO)

et al. 2022

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Developing modern products involves numerous domains (controlling, production, engineering, etc.) and disciplines (mechanics, electronics, software, etc.). The products have become increasingly complex while their time to market has decreased. These challenges can be overcome by Model-Based Systems Engineering (MBSE), where all development data (requirements, architecture, etc.) is stored and linked in a system model. In an MBSE system model, product requirements at the system level can lead to numerous technical variants with conflicting objectives at the parameter level. To determine the best technical variants or tradeoffs, Multidisciplinary Analysis and Optimization (MDAO) is already being used today. Linking MBSE and MDAO allows for mutually beneficial synergies to be expected that have not yet been fully exploited. In this paper, a new approach to link MBSE and MDAO is proposed. The novelty compared to existing approaches is the reuse of existing MBSE system model data. Models developed during upstream design and test activities already linked to the MBSE system model were integrated into an MDAO problem. Benefits are reduced initial and reconfiguration efforts and the resolution of the MDAO black-box behavior. For the first time, the MDAO problem was modeled as a workflow using activity diagrams in the MBSE system model. For a given system architecture, this workflow finds the design variable values that allow for the best tradeoff of objectives. The structure and behavior of the workflow were formally described in the MBSE system model with SysML. The presented approach for linking MBSE and MDAO is demonstrated using an example of an electric coolant pump.

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Challenges for Future Automotive Mobility

Cited by 6 publications

References 6 publications

Responsiveness and Precision of Digital IMUs under Linear and Curvilinear Motion Conditions for Local Navigation and Positioning in Advanced Smart Mobility

Responsiveness and Precision of Digital IMUs under Linear and Curvilinear Motion Conditions for Local Navigation and Positioning in Advanced Smart Mobility

Systems Engineering Based Sustainability Improvement in Automotive Product Development

Optimization Workflows for Linking Model-Based Systems Engineering (MBSE) and Multidisciplinary Analysis and Optimization (MDAO)

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