07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8

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Author: search.filters.author.Kempter, FabianSubject: search.filters.subject.620

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Now showing 1 - 7 of 7
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    Towards learning human-seat interactions for optimally controlled multibody models to generate realistic occupant motion
    (2023) Fahse, Niklas; Harant, Monika; Roller, Michael; Kempter, Fabian; Obentheuer, Marius; Linn, Joachim; Fehr, Jörg
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    On the validation of human body models with a driver-in-the-loop simulator
    (2018) Kempter, Fabian; Fehr, Jörg; Stutzig, Norman; Siebert, Tobias
    For the development of modern integrated safety systems, standard simulation models of anthropometric test devices, often called crash test dummies, are inappropriate for Pre-Crash investigations due to missing activation possibilities, tuned characteristics for one specific accident scenario and high passive stiffness properties. To validate safety concepts getting active prior to the crash new tools like suitable virtual models of human occupants are required. Human Body Models (HBM) provide a higher biofidelity and can be equipped with active muscle elements enabling different muscle activation strategies. To improve the muscle activation strategy and the stiffness properties of active HBMs, validation processes on the basis of low-acceleration experiments are inevitable. In contrast to Post Mortem Human Surrogates only low-severity tests can be performed with real human subjects. This paper presents the workflow of a validation process based on an academic scale Driver-in-the-Loop (DiL) simulator in combination with a synchronized measurement chain consisting of an Optitrack stereo vision and an electromyography detection system.
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    Positioning and simulation of human body models on a motorcycle with a novel restraint system
    (2022) Maier, Steffen; Kempter, Fabian; Kronwitter, Stefan; Fehr, Jörg
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    Towards learning human-seat interactions for optimally controlled multibody models to generate realistic occupant motion
    (2023) Fahse, Niklas; Harant, Monika; Roller, Michael; Kempter, Fabian; Obentheuer, Marius; Linn, Joachim; Fehr, Jörg
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    Towards the learning of human-seat interactions for runtime-efficient human models based on pressure distributions
    (2022) Fahse, Niklas; Roller, Michael; Kempter, Fabian; Fehr, Jörg
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    EMMA4Drive : a digital human model for occupant simulation in dynamic driving maneuvers
    (2023) Obentheuer, Marius; Fahse, Niklas; Harant, Monika; Kleer, Michael; Kempter, Fabian; Reinhard, René; Roller, Michael; Björkenstam, Staffan; Fehr, Jörg; Linn, Joachim
    About two-thirds of all German workers currently commute to their workplace by car without being able to engage in any meaningful activity. In the future, occupants of autonomous driving vehicles will be able to perform new activities, such as regeneration exercises, working, or consuming entertainment media [1]. This leads to many new challenges in assessing the layout of vehicle interiors, both in terms of comfort and vehicle safety. The goal of the EMMA4Drive project is to further develop the muscle-activated multi-body human model EMMA (Ergo-dynamic Moving Manikin) for use in next-generation partially or fully autonomous driving vehicles. The resulting software prototype EMMA4Drive will be able to analyze and evaluate safety and ergonomics equally during driving maneuvers under dynamic loads as a digital image of the occupant. In the automotive industry, digital human models (DHM) are widely used to simulate the human driver in the early stages of product development. Furthermore, detailed finite element (FE) models of the human body are used to simulate the highly dynamic impact and resulting injuries in the human body in crash simulations [2]. Moreover, DHM based on multibody system (MBS) kinematics are widely applied in reachability investigations and (posture- based) ergonomic assessment of the driver [3]. However, to predict active movement in dynamic driving maneuvers such as cornering, sudden braking, or lane change and pre-crash scenarios, neither FE nor simple MBS kinematic models are applicable. For a more detailed overview on DHMs in this application case, we refer to [4]. In this work, we will present an approach for the enhancement of a multibody based DHM to generate human like motions for a highly dynamic driving simulation. For motion prediction, an optimal control problem (OCP) is set up and solved. A model order reduction (MOR) approach is used to transfer driver seat interaction from detailed FE simulations to the Optimal Control (OC) framework. We use our RODOS driving simulator [5] to validate simulation results (e.g., motions, seat pressure distribution) and to identify OC parameter configurations for motion prediction.