Research
Research for an innovative future
The principle of integrating current research results and future-oriented topics into our projects is already deeply ingrained in our company's name - Technisch Wissenschaftlicher Transfer [Technical Scientific Transfer]. With the aim of securing progress today for tomorrow, TWT is involved in numerous national and international research projects. Activities include initiation, management and coordination of consortia, as well as participation in research initiatives.
Our innate drive for improvement aims to stimulate research-based innovations and advance emerging, cutting-edge technologies. Innovation and scientific expertise, coupled with the competence to examine things holistically, have been the cornerstones and driving forces behind TWT's successful research activities since the company was founded.
M-CUBE/COLTOC
Traffic jams related to irregular traffic are a common problem that imposes immense societal costs on both citizens and public authorities. In this project, we develop a holistic cooperative optimization approach that explicitly targets delays caused by irregular traffic phenomena. The proposed approach aims to (i) assess the resilience of a transportation network, (ii) identify critical and vulnerable elements, (iii) reduce (ir)regular delays, (iv) identify dynamic bottlenecks, and (v) mitigate their negative impacts through new control approaches and dynamic speed recommendations. The collaboration between industry partners, government agencies, and academic partners will enable the project partners to develop implementation-ready tools, which will be tested at the 2024 State Garden Show.
Objective
To achieve this goal, COLTOC uses a new type of sensor developed in this project that is capable of measuring regular and irregular traffic phenomena. The new information will be used to develop multimodal traffic models capable of detecting and correcting traffic incidents due to irregular traffic patterns. This project is the first to explore the relationship between network topology, integrated mobility, connected and autonomous vehicles, and new sensor technologies. The expected outcome is a framework capable of delivering significant societal benefits (improved air quality, reduced travel times, sustainable modal shift) while boosting economic growth thanks to breakthroughs in sensor technology and automation.
Our contributions to the project
- Providing Tronis for project partners
- Scenario creation in Tronis with focus on the traffic flow which represents a real traffic situation.
- Implementation of an interface to the planning algorithm in Tronis. Integration of message exchange between road users within Tronis.
- Extension of the co-simulation interface between Tronis and Sumo.
Partners
- TWT
- HawaDawa
- Fujitsu
- Technical University of Munich
- BMW
- Munich County
- MVG
- SIXT
- Kirchheim
SmartDelta
Digitization and automation increasingly lead to software-intensive systems and services and place high demands on industrial software development. Today, such software-intensive systems are rarely developed from scratch, but are incrementally assembled, integrated, and tailored to the needs of a specific customer, market, or region in short iteration cycles. Far too often, however, certain quality aspects of the system begin to deteriorate over time. It is therefore extremely important to be able to accurately analyze and determine the impact of each software change and enhancement on the quality of the entire software-intensive system.
SmartDelta develops automated solutions for the quality assessment of software increments for a continuous software development process. SmartDelta designs intelligent analysis methods based on development artifacts (e.g. source code, log files, requirements specifications), provides insights into the quality improvements or degradations of different software versions and provides recommendations for the next software versions.
Objective
- The goal of SmartDelta is to develop automated solutions for the quality assessment of software products and versions in continuous software development processes. SmartDelta focuses in particular on the validation and verification of extra-functional requirements and the corresponding implementation, provision, and maintenance of test models as a basis for the realization of automated quality assurance activities. The concrete goals of SmartDelta are:
- Automate model building by using natural language processing techniques and pattern-based approaches.
- Establishment of an automated consistency check and validation of software increments
- Reducing of the development, deployment, and feedback loops in software development
- Reduction of quality assurance efforts for extra-functional properties
What is SmartDelta used for?
Software-intensive systems and services are not usually designed and implemented from scratch for each customer or order, but are delivered as a further development or as a modified version of an existing software product tailored to the needs of a specific customer, market or region. Over time, software companies manage increasing volumes of software products in various versions and levels of maturity that serve as the basis for subsequent reuse. Efficient creation and delivery of high-quality and secure software is only possible if the quality and maturity assessments as well as the quality assurance process are supported by are supported by a sufficient degree of automation.
SmartDelta develops automated solutions for software product and release quality assessment, enabling organizations to develop and deliver high-quality, trusted software systems in a fast-paced, agile environment.
Our contributions to the project
- Approach to transforming extra-functional requirements expressed in constrained natural language into formalized requirements models.
- Automated generation of test cases from model checkers
- Automated model-based testing of the system models against the formalized requirements based on automatically generated test cases
- Approach to identifying cause-and-effect chains for problematic behavior in new software artifacts and making recommendations for improving the software based on these cause-and-effect chains.
Partners
- Software AG*,
- AKKA Industry Consulting GmbH
- Fraunhofer FOKUS
- ifak – Institute for Automation and Communication
- Bombardier*
- Infotiv
- Mälardalen University
- Quviq AB
- RISE – Research institutes of Sweden**
- IZERTIS
- PRIVOLVA
- Sotec Consulting*
- University of Madrid Carlos III
- Dakik Yazilim Teknolojileri
- Ericsson
- ERSTE Software Limited
- Kuveyt Turk Bank*
- NetRD
- BEIA GmbH*
- c.c.com GmbH
- University of Innsbruck
- Cyberworks Robotics
- eCAMION INC.
- GlassHouse Systems
- SmartCone Technologies Inc.
- University of Ontario Institute of Technology*
* national coordinator; ** international coordinator;
KARLI
KARLI stands for Artificial Intelligence for Adaptive, Responsive and Level-Compliant Interaction in the Vehicle of the Future. The project investigates driver states and driving situations, evaluates them by means of tuned AI models and develops human-machine interactions adapted to the situation. The AI-based driver-vehicle state models are expected to provide the necessary quality and robustness for autonomous driving, even at higher levels.
The specifications for vehicle architecture and sensor technology are to be used as a guideline for the use and derivation of data in production vehicles.
In KARLI, the following applications are developed from concept to prototype:
Level-compliant driver behavior - detection and promotion
AI interaction for adaptive systems
Motion sickness - detection and prevention
Objective
The goal of the KARLI project is to develop an adaptive, responsive and level-conforming interaction in the vehicle of the future.
To this end, customer-relevant AI functions are being developed in KARLI that capture driver states and shape interactions for different stages on the way to an automated vehicle (automation level).
These AI functions are developed in KARLI from empirical and synthetically generated data. The data will be collected and used in KARLI in such a way that the project results are scalable to Big Data from production vehicles that will be available in the future.
Our contributions to the project
TWT participates in all applications with
- Concept development for human-machine interaction through voice user interface
- Develop machine learning and data analytics methods for context and driving situation recognition.
- Identifying emotional states and features of motion sickness through machine learning.
Partners
- Continental Automotive Gmbh
- Ford-Werke GmbH
- Audi AG
- INVENSITY GmbH
- paragon semvox GmbH
- TWT GmbH Science & Innovation
- studiokurbos GmbH
- Fraunhofer IAO
- Fraunhofer IOSB
- Allround Team GmbH
- Hochschule der Medien
- University of Stuttgart
- branmatt II legal (under subcontract)
AINET-Antillas
AINET-Antillas is a sub-project within the European AINET initiative. In various applications, such as Industry 4.0 and connected and autonomous driving, network availability and quality are of utmost importance to implement customer functions properly. AINET-Antillas develops and validates infrastructure elements for applications in highly networked scenarios. Tronis® is to be used for validating automated, connected and autonomous driving functions with regard to network quality; the latter also applies when comparing different network generations.
Objective
AI-NET is aimed at creating a platform for the dynamic configuration of communication networks while they are running; a platform not only easy to use from a network operator's point of view, but also from the user's perspective. Via open descriptive interfaces ("intent-based"), the full potential of the infrastructure can thus be utilised along with seamless multi-cloud integration. Three sub-projects within the AINET framework develop solutions based on concrete, complementary application scenarios and implement them in demonstrators. The Antillas sub-project focuses on developing infrastructure elements for automated telecommunications networks with the aim of making them suitable for applications in the fields of industry and autonomous driving.
What is AINET-Antillas used for?
Within AINET-Antillas, data measurements are carried out and sensor modules are evaluated during real measurement runs. In collaboration with network and infrastructure technologies, realistic scenarios can be virtually examined and validated. These scenarios focus on communication and entertainment systems, entertainment functions and autonomous and connected driving functions relying on the 5G mobile communications standard. AINET-Antillas will also contribute to the virtual validation of driver assistance systems, with a particular emphasis on the functions using cellular data to compute their behaviour.
Our contributions to the project
- Development of novel solutions for application scenarios
- Autonomous network operation through end-to-end automation
- Infrastructure optimised for latency and security for telecommunications networks
- Specification of simulation concept, model extensions and interfaces
- Establishment and integration of technical interfaces for mobile communications and data models
- Implementation of model extensions, simulation in prototypical scenarios
- Demonstrator implementation in Tronis®
- Development of close-to-reality use cases for connected driving
- Generation of simulation results, data exchange with partners
- Investigation of mobile communications and WLAN
- Demonstrator of cloud-based application and visualisation
Partners
- DCAITI (Karl Hübener)
- Ericsson, Nokia
- Fraunhofer Fokus (Robert Protzmann)
- Attesio (Netzplanung-/optimierung)
- Uni Stuttgart IKR
- Adva (equipment for optical transmission of information)
KoSi
The acronym KoSi stands for Cooperative Autonomous Driving with Safety Guarantees. This project aims to investigate how the challenge of autonomous driving in complex traffic situations can be mastered through cooperative manoeuvre planning. Here, a particular focus is on mixed traffic scenarios, i.e. with autonomous and non-autonomous road users.
Objective
In this project, methods and algorithms are being developed to cooperatively negotiate the areas that can be travelled by the autonomous road user in a group of road users. These areas serve then to plan the vehicle trajectories and to identify potential emergency manoeuvres. Communication models between autonomous and non-autonomous road users are being established for taking into account even mixed traffic scenarios. Prediction of driving behaviour and trajectory planning will be viewed holistically together with their formal verification, thus guaranteeing the safety of manoeuvres. Radar systems as an essential component of autonomous vehicles' sensor technology will be further developed in terms of safety, in particular for detecting attempts at manipulation.
What is KoSi used for?
Driver assistance systems have long been an integral part of modern cars and trucks. Despite the development of increasingly intelligent and innovative systems, it is still a rocky road to the safe operation of autonomous vehicles in large-scale use and in all environments (extra-urban and urban). For many years to come, mixed traffic of conventional and automated vehicles, as well as of road users not capable of being automated, will dominate. The development of inherently safe methods of manoeuvre planning, especially for complex traffic scenarios, is thus inevitable. On this issue, KoSi will make its contribution.
Our contributions to the project
- Generation of complex, realistic test scenarios (incl. road users not capable of being autonomised, such as cyclists and pedestrians)
- Method development: coupling of the development and simulation tools involved
- Verification: validation of the algorithms developed in the project
- Radar sensor technology: generation of training data, data manipulation
Partners
- TWT GmbH
newAide
In the newAide project, the partners are researching the use of Artificial Intelligence (AI) methods in highly complex, simulation-based design processes in vehicle development with the aim of accelerating, optimising and partially automating these processes. In addition to improving individual design disciplines through AI approaches, the newAIDE project will use the sub-projects to investigate a fundamental database structure, as well as data structuring that supports and simplifies the use of AI methods in vehicle design. This could provide the base for a widespread introduction of AI approaches in vehicle design and beyond.
Objective
An important technical goal is to optimise simulation processes using AI algorithms. With the help of AI, the simulations will also include boundary conditions and factors in the decision-making process that previously had to be disregarded due to the high complexity. The additional networking is intended to increase the simulations' predictive accuracy and robustness of the simulations and thus contribute to reducing the development time.
What is newAide used for?
The project focuses on design processes in which fundamental decisions are based on human knowledge and experience. The decisions are to be learned by AI algorithms and made largely autonomously on the basis of comprehensive test, engineering and simulation data. In this way, complex design tasks that are currently still dependent on the developer's skills and experience can be transferred by AI methods into automatable, data-based decision-making processes.
Our contributions to the project
- Specifying use cases in multi-body simulation and control systems for the pre-application of chassis parameters.
- Automating workflows for the chassis and control design process
- Exploring new methods in metamodelling for physical systems
- Validating and optimising the methods developed for AI-controlled chassis and control design processes
Partners
- BMW
- TUM – Data Analytics & ML (Günnemann)
- TUM – Vibroacoustics (Marburg)
- MSC Software
- Altair
- divis
ALFRIED
ALFRIED – Automated and Connected Driving in Logistics at the FRIEDrichshafen test field – is intended to serve inner-city goods traffic in the future. The overall concept of the hyper-efficient mobility system consists of several mobility users. The project aims to improve the overall traffic situation, especially of vehicles (both connected and non-connected), intelligent infrastructures and control centres. To this end, various technologies are being developed and prepared for use and deployment in real traffic. In the future, the findings from Friedrichshafen should be highly relevant for other cities and regions.
Objective
The aim of the project is to develop a "future-proof, sustainable mobility system through automated driving and networking". As a German medium-sized city, the Friedrichshafen test site (real traffic) on Lake Constance offers an excellent field of application for a mobility concept that is transferable to many cities and regions. With the focus on the infrastructure and the Smart City Control Centre, the complex mobility system of the city of Friedrichshafen is to be further developed. Automated and connected driving, data integration, route optimisation, disruption prediction and intelligent real-time information are to optimise inner-city transport of goods between factory locations. The savings in transport runs and/or the associated emission consumption and the relief of the inner-city traffic volume shall be the outcome of this optimisation.
What is ALFRIED used for?
The ALFRIED project addresses problems of road safety and efficiency as well as high road congestion. The mixed operation between motorised and non-motorised road users, as well as among vehicles capable and incapable of V2X, currently poses a challenge. For the benefit of all road users and to reduce traffic hold-ups, there is a need to improve traffic flow and optimise routes.
Much of the content in the digital platform is expanded by various data sources and evaluated, analysed and displayed via the Smart City Control Centre, including intelligent infrastructure with its sensor fusion concept for complex intersections, as well as automated and connected driving in difficult driving situations. It also includes data from Intelligent Vehicles (about the vehicle, the infrastructure and environment). The results are validated at the Friedrichshafen test field with a special focus on inner-city transport of goods.
Our contributions to the project
Virtual verification platform featuring Tronis
- Replicating test tracks
- V2X communication
- Sensor technology (ray tracing)
- Digital twin
- Validation
- System tests
- Test scenarios
- Software-in-the-loop/HiL/Vehicle-in-the-loop
Dynamic Map
- Providing and aggregating information from and for all vehicles capable of V2X
- Metainformation of vehicles
- Surrounding information (signs, traffic lights, no-drive zones)
- (Mobile) road works
- Road-related procurement
- Acquisition of vehicle environment
- Representation of vehicles not capable of V2X
- Representation of traffic flow for control and optimisation
- Warning of critical (dangerous) situations
Partners
- IWT
- DHBW Ravensburg
- DLR Braunschweig
- ETO Gruppe
- Hahn-Schickard-Gesellschaft
- IHSE
- IMST
- Netwake Vision
- Voltra Solutions
- ZF Friedrichshafen
Autoaccept
Autoaccept stands for the elimination of insecurity in understanding human-machine interaction ("Automation Without Insecurity to Increase Acceptance of Automated and Connected Driving"). To improve the user experience, the recommender system selects the most suitable adaptation for the driver with regard to the information provided on the traffic situation or driving style.
Objective
Lack of trust and negative expectations may diminish acceptance of new technologies. While the emergence of automated and autonomous driving brings many benefits such as more leisure time, users also face challenges. Among the latter are lack of trust, insecurity about driving decisions made autonomously, and motion sickness.
What is Autoaccept used for?
To improve the acceptance of automated and autonomous driving, AUTOAKZEPT aims to develop user-centred strategies. This will bring about a safe change in driving style and trust in Human-Machine Interfaces (HMI) in autonomous and automated vehicles. A user-centric, iterative approach is adopted for achieving this. The problems described above are to be overcome by means of AUTOAKZEPT.
Our contributions to the project
- User state recognition
- User studies in real vehicles and simulators
- HMI to reduce insecurity and increase user experience
- Developing and implementing the recommender system
- Situation model
Partners
- DLR Braunschweig (Coordinator)
- IAV GmbH
- TU Chemnitz
- BMW Group (associated partner)
LiBAT
LiBAT – Development of a High Voltage Lithium BATtery – aims to develop an ultra-light, highly integrated battery pack for aerospace applications. The LiBAT design meets demanding requirements in terms of weight, energy density and performance and can be used flexibly in various applications. Prior to the LiBAT design being used in electric and hybrid aircraft, prototypical implementations and tests on the ground (TRL4) are being carried out. LiBAT is shaping a new future for aviation.
Objective
The field of hybrid and electric propulsion systems for aviation represents an enormous potential for innovation, especially with regard to CO2 savings. In order to achieve reliable, efficient and safe operation of appropriate battery systems, the latter must be developed and optimised with a careful eye on energy capacity, power density, total weight and volume, thermal requirements and design aspects. The main goal of the project is the design of a particularly lightweight battery pack with state-of-the-art energy density and building a prototype. A clear interface definition will ensure the demonstrator's facile integration into current aircraft architectures. The prototype will be developed with the target of achieving TRL4 and will be validated under laboratory conditions.
What is LiBAT used for?
Lightweight and powerful battery packs can be used in a multitude of ways - from applications in electrified aviation (air taxis, e-gliders) or even in e-vehicles to mobile battery packs (e.g. power provision at construction sites).
Our contributions to the project
- System simulation and modelling of battery pack and on-board power system
- Electrical & thermal simulations
Project coordination
Partners
- LION Smart GmbH
- Dassault Aviation (Topic Lead)
RABE
Rabe - Intelligent Rollator for Inpatient Care to Preserve the Autonomy of Residents and to Relieve the Workload of Caregivers – comprises the development of an intelligent rollator designed to increase the user's autonomy and mobility while relieving the workload of caregivers. This is achieved through a range of functions, such as the "Autonomous Driving" mode, an indoor navigation and a pedelec function. The products developed during the project are being tested in a nursing home run by Stiftung Liebenau. ollator für die stationäre Pflege zum Liebenau. Â
Objective
The RABE project aims to develop a smart rollator specifically for the needs of long-term inpatient care. The RABE rollator is intended to improve the autonomy of nursing home residents and to relieve nursing staff. This is achieved, among other things, by means of an electric drive that supports the user in coping with longer distances and inclines, as well as in driving over thresholds and curbs. In addition, the rollator is capable of driving autonomously using ultrasonic transmitters and receivers. This allows it, for example, to pick up the user at the bedside, thus relieving him/her of a route on which accidents often occur.
What is RABE for?
For years, the number of people in need of care has been rising in Germany due to increasing life expectancy. As a result, there is a growing need for new care concepts for the elderly, which have a demonstrably positive influence on maintaining, restoring or even increasing the quality of life in old age. The resulting needs cannot be met by the nursing staff alone; instead, technological innovations will have to be increasingly integrated into everyday nursing care of the future. The functionalities developed during the RABE project are intended to relieve caregivers of individual tasks or to support them in coping with those.
Our contributions to the project
- Supporting the technical implementation of rollator localisation and motor control
- Implementing indoor navigation
- Implementing a voice control system
- Implementing a service-based backend
- Creating a digital rollator twin for simulative further development and validation (TRONIS)
Partners
- Telocate GmbH
- Reiser AG Maschinenbau
- Hochschule Ravensburg-Weingarten (IKI, IGVP)
OPsTIMAL
OPsTIMAL – Optimised Processes for Trajectory, Maintenance, Management of Resources and Airline Operations – aims to optimise aviation operations. Data analysis and predictive maintenance of engines help to track the research on these topics.
Objective
The OPsTIMAL research project aims at software-based optimisation of flight operations by combining various relevant data sources in a single database. In this way, flexible disruption management, based on current conditions and user preferences such as costs, punctuality and safety, is to be made possible. A key element of the approach is the holistic optimisation of all subsystems considered, i.e. trajectory planning, MRO (Maintenance, Repair and Operations), turnaround and fleet and crew rotation. In the final version, the database display will give users a detailed overview of the respective situation and provide the corresponding evaluated options for action.
Our contributions to the project
- Data analysis
- Development of algorithms for predictive maintenance of jet engines
- Website for project presentation
Partners
- JEPPESEN
- PACE
- Rolls Royce
- MTU
- Inform
- DIEHL
- SAP
- DLR
- Fraunhofer FKIE
- Friedrich-Alexander Universität Erlangen-Nürnberg
- Technische Universität Dresden
AIToC
In the project AIToC – Artificial Intelligence Supported Tool Chain in Manufacturing Engineering – an integrated tool chain for production planning and production systems engineering is being developed to support decision-making at very early stages. This includes the development and adaptation of tools for defining and managing requirements, as well as for creating process plans, equipment models and layouts. For this purpose, a model-based approach is used to define product and production requirements. Tool chain integration focuses on solutions for tool interoperability and plug & play functionality to be flexible in designing the simulation environment. This will have a significant impact on efficiency (cost), quality of models and cycle time for simulations in the industrial context.
Objective
The aim is to develop an integrated and AI-supported tool chain for production planning and the production systems engineering. The planned tool chain will support the formalisation and automated requirements analysis, the computer-aided simulation model generation and the software-supported generation of layouts. In all these dimensions, AI approaches will be used to process the large amounts of data needed to learn from existing solutions. Concrete methods include knowledge management and expert systems, natural language processing and machine learning.
What is AIToC used for?
Expansion and improvement of virtual validation and, in particular, virtual commissioning in production planning and production systems engineering by incorporating manual processing steps and automated model creation for co-simulation.
Automating and improving the requirements specification process by a) providing a graphical requirements representation for visual analysis, by b) automating the formalisation of natural language text requirements, and by c) providing a formalised and unambiguous basis for analysis, testing, work plan generation and communication during planning and engineering.
Our contributions to the project
- Automated and AI-based formalisation of natural language text requirements
- Further development of the TWT requirements engineering tool in terms of modelling language, requirements modelling editor, visualisation of requirements, transformation of specification models, AI-based analysis and testing of formalised requirements, as well as support for the ReqIF standard.
- Ontology-based end-to-end approach to data and tools
- AI-based creation of behavioural models based on real measurement data
- Automated online update of a production system's digital twin
- Coupling of FMI-based MMUs for manual process steps to the TWT co-simulation master for production systems to enable simulation of manual process steps in human-machine interactions.
Partners
- Daimler Buses ? EvoBus GmbH (deutscher Koordinator)
- DFKI – Deutsches Forschungszentrum für Künstliche Intelligenz GmbH
- EKS InTec GmbH
- ifak – Institut für Automation und Kommunikation e.V.
- in2sight GmbH
- isb innovative software businesses GmbH
- Raumtänzer GmbH, SAG – Software AG
iVeSPA
iVeSPA – Integrated Verification, Sensors and Positioning for Aircraft Production – is investigating ways to significantly increase the level of digitisation in today's aircraft production. The current level of process step digitisation is rather low. Insofar as the machines used in the process make their sensor data from the undergone process steps available for further use, isolated solutions will emerge at best. A networking of the machines or central storage for simplified access to all data of the manufacturing processes is not widely implemented.
Objective
During the iVeSPA project, the efficiency of aircraft production is to be improved by increasing the individual processes' level of digitisation. For this purpose, radio-based and optical localisation methods are used to track an aircraft component throughout the entire installation process from delivery to installation in the aircraft fuselage. The dataset created is read into a digital 1:1 factory model in real time for simulative process description and displayed. For value-enhancing integration of sensor data generated with the production, the latter is linked to the underlying processes, thus ensuring quality and temporal sequences of the processes.
Our contributions to the project
- Modelling of positioning sensors
- Creating a digital model of the manufacturing environment (Blender)
- Integrating data from a sensor network and linking it to the model (Python)
- Implementing a digital twin of the underlying process
Partners
- Airbus Operations GmbH
- Advanced Realtime Tracking GmbH & Co. KG
- Agilion GmbH (nun Siemens AG)
- Fraunhofer-Institut für Fabrikbetrieb und -automatisierung IFF
- Werkzeugmaschinenlabor WZL der RWTH Aachen
- Siemens AG
- ZAL Zentrum für Angewandte Luftfahrtforschung GmbH
SMART
“Simulation of mobile networks and automotive behavior in realtime“ - Autonomous driving applications…Autonomous driving applications place high demands on mobile communications networks, which naturally have varying qualities of service. SMART is investigating virtual Vehicle-to-X (V2X) applications and their resilience in LTE and 5G scenarios. The project developed a mechanism for negotiating the Quality of Service (QoS). Real-time simulations of V2X-based autonomous driving scenarios in the Tronis® 3D virtual environment were also used to evaluate the mechanism's capabilities to predict stochastic QoS guarantees.
Objective
The aim of SMART is the innovative coupling of existing simulators for sub-aspects of autonomous driving relevant to mobile communications. In this way, a real-time capable simulation platform for the integrated investigation of V2X communications in scenarios close to reality is to be created. Using the platform, the mechanisms developed in SMART for negotiating and predicting the communications' quality of service will be evaluated in terms of their usefulness and feasibility.
Our contributions to the project
- Integration of V2X simulation models into the Tronis® 3D simulation environment
- Creation of simulation modules for autonomous driving scenarios
- Traffic simulation
- Real-time simulations
- Co-simulations
- Static and dynamic scenario generation
- Creation of realistic virtual driving scenarios
Partners
- Institute of Communication Networks and Computer Engineering
- University of Stuttgart
RITUAL
While assistive robots are already well developed in terms of function, they are currently often not yet capable of interpreting human emotions and behaviour correctly and of interacting with the user in a way appropriate to the situation. If assistive robots are to enter our private everyday life, they must be enabled to correctly interpret the user's state and the context of use and to adapt their verbal and non-verbal interaction strategy accordingly.
Objective
In RITUAL, established interaction strategies from robotics and the vehicle cabin are to be integrated into existing assistive robot platforms, adapted to different user types and states and evaluated in a long-term study. The focus is on investigating interaction-relevant parameters, such as the level of proactivity shown by assistive robots when initiating a dialogue with the user, the language of the assistive robots and their approach dynamics. The optimal interaction strategy is determined on the basis of personal characteristics, theoretically substantiated and recorded in the user profile, emotions detected in real time as well as through the interpretation of the current context. By adapting the assistive robot behaviour to both the user and the context, a positive effect on the User Experience (UX), the acceptance and the trust of the user is expected.
RITUAL is grounded in social science through participatory research methods that iteratively involve potential users in the specification of human-robot interaction (MRI) scenarios and the exploration of ELSI aspects and contextual factors. The special safety requirements arising from the uncontrolled place of use and the proximity between the human and the robot are also taken into account in a safety concept appropriate to both the standards and the context.
Our contributions to the project
- Planning the main project
Partners
- LebensPhasenHaus Tübingen
- Mehrgenerationenhaus Ravensburg
- Zusammenleben 4.0 Halle
- Stiftung Liebenau
Mathematical Research
Scientific engineering and physical questions are often formulated by partial differential equations and solved using the Finite Element Method (FEM). In this method, the given calculation domain is first meshed, i.e. broken down into simple geometric elements, such as triangles or quadrilaterals in case of surface models, or tetrahedra or hexahedra in case of volume models. These elements are used as the basis for defining the solution function, the coefficients of which are to be determined by the FE procedure. Depending on the quality of the resulting mesh, this is followed by a mesh optimisation step, which may be integrated, if applicable, into the mesh generation procedure. Then, taking into account boundary conditions such as loads, fixations, etc., a linear system of equations is set up during the FE simulation process to determine the solution coefficients. Subsequently, the resulting FE system is solved using special numerical methods and the solution of the simulation problem is evaluated.
Starting point: Obtaining high-quality meshes at geometric complexity
In this process, the quality of the meshing has a decisive influence on the efficiency and accuracy of FE simulation. As a rule, meshes with elements that are as regular as possible are desirable in order to avoid element angles that are too small or too large, since these lead to an increase in the condition number of the stiffness matrix and thus to poorer solvability of the resulting FE system or to inaccuracies in the resulting solution. The generation of high-quality meshes becomes more and more problematic with increasing geometric complexity.
TWT approach to solving the problem: GETMe
TWT's Mathematical Research & Services department developed the Geometric Element Transformation Method (GETMe) for smoothing finite element meshes. In this method, the quality improvement is achieved exclusively by repositioning mesh nodes without changing the mesh topology, i.e. the connectivity structure of the mesh elements is maintained. This is exemplified in the figure below for an outer mesh consisting of hexahedrons of the Aletis open passenger car developed by TWT. In the figure, the elements are coloured according to their regularity. Regularity was measured using a regularity measure, taking the value 0 (red) for degenerated elements and 1 (blue) for regular elements. In particular, elements with small quality numbers should be avoided, as these can lead to instabilities and inaccuracies in the finite element calculation.
Mesh smoothing by GETMe is based on the use of geometric transformations for polygons and polyhedra, which, when applied iteratively, successively transform problematic elements into regular and thus higher-order elements. In principle, the procedure is suitable for the improvement of the most common FE mesh types.
Outcome
In the publications authored by TWT, it was possible to demonstrate through numerous numerical tests and mathematical proofs that GETMe achieves mesh qualities previously only attainable with global optimisation methods. However, GETMe yields a significant speed advantage, since global optimisation methods require considerably more computing power due to their mathematical optimisation approach.
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