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At the current development stage, the lower airspace above urban areas is only used to a very limited extent. Recent developments in the drone industry are making this area more accessible with the possibility to set up so-called U-Spaces, where this area is controlled for manned and unmanned aerial participants. The leading use case for drone applications is currently seen in the medical sector. The relevance of the use of medical drones in urban areas and in which conditions the technology will bring specific advantages is still unclear.
Autonomous driving and traffic flow simulation requires a realistic and accurate representation of the environment. Therefore, this research focuses on the semantic segmentation of aerial images for simulation purposes. Initially, a dataset was created based on true orthophotos from 2019 and Kempten’s street cadaster, with true orthophotos being fully rectified aerial images. The chosen classes were oriented towards the subsequent conversion and usage in simulation. The proposed labeling workflow used cadaster data and demonstrated significant time efficiency compared to state-of-the-art datasets. Subsequently, a neural network was implemented that was trained and tested on the dataset. In addition, the network was also trained only on the lane markings to compare the network’s performance. Both cases demonstrated excellent segmentation results. The generalizability was then tested on true orthophotos from 2021. The results indicated a solid generalizability, but still needs to be improved. Finally, the aerial information was converted into a 3D environment, that can be used in simulations. Our results confirm the usage of aerial imagery and street cadaster data as a basis for the simulations.
The development of Automated Driving Systems (ADS) has the potential to revolutionize the transportation industry, but it also presents significant safety challenges. One of the key challenges is ensuring that the ADS is safe in the event of Foreseeable Misuse (FM) by the human driver. To address this challenge, a case study on simulation-based testing to mitigate FM by the driver using the driving simulator is presented. FM by the human driver refers to potential driving scenarios where the driver misinterprets the intended functionality of ADS, leading to hazardous behavior. Safety of the Intended Functionality (SOTIF) focuses on ensuring the absence of unreasonable risk resulting from hazardous behaviors related to functional insufficiencies caused by FM and performance limitations of sensors and machine learning-based algorithms for ADS. The simulation-based application of SOTIF to mitigate FM in ADS entails determining potential misuse scenarios, conducting simulation-based testing, and evaluating the effectiveness of measures dedicated to preventing or mitigating FM. The major contribution includes defining (i) test requirements for performing simulation-based testing of a potential misuse scenario, (ii) evaluation criteria in accordance with SOTIF requirements for implementing measures dedicated to preventing or mitigating FM, and (iii) approach to evaluate the effectiveness of the measures dedicated to preventing or mitigating FM. In conclusion, an exemplary case study incorporating driver-vehicle interface and driver interactions with ADS forming the basis for understanding the factors and causes contributing to FM is investigated. Furthermore, the test procedure for evaluating the effectiveness of the measures dedicated to preventing or mitigating FM by the driver is developed in this work.
Tourism is an important economic driver for numerous regions, at- tracting more than one billion visitors annually. While economically significant, excessive numbers of visitors lead to local overcrowding, which negatively im- pacts visitors’ experience and safety, and causes environmental harm. This paper proposes a practical approach to empowering destination management organiza- tions (DMOs) to manage tourist flows. We advocate for a rule-based approach that models visitor occupancy based on easily understandable influence factors like weather and date. As a central component, an ontology-guided knowledge graph ensures compatibility with diverse touristic data models and allows seam- less integration into existing infrastructures. By digitizing DMOs’ experiential knowledge, we facilitate the implementation of lean and cost-effective visitor guidance. We demonstrate our approach by implementing two applications for two different use cases. The results of our qualitative evaluation reveal the com- pelling potential for rule-based occupancy modeling approaches serving as a baseline for future visitor management systems.
Many cities in Europe and around the world are concerned with reducing their CO2-emissions. One step on this agenda is the introduction of electric buses to replace combustion engines. The electrification of urban buses requires an accurate prediction of the energy demand. In this pa per, an energy model and the underlying calibration process is presented. This approach leverages substantial tracking data from 10 electric buses operated in Göttingen, Germany. It was shown that, with the use of additional information from the directly measured tracking data, like auxiliary power, charging power and vehicle weight, it is possible to precisely calibrate models based on physical equations with regard to generally poorly identifiable parameters like rolling friction coefficient or efficiency of the electric machine. With a multilayered approach for simulating the energy demand, it is possible to validate the results on the mechanical layer and on the electrical layer separately. This enables a far better parametrization and elimination of uncertainties from the different parameters. Furthermore, we compare the results to other publications for sections with 1 km, 100 km and 230 km, respectively. The relative errors between the simulated and measured electrical power consumption are below 0.3%, 3% and 6.5%, respectively. Hence, the yielded model is appropriate for electric urban bus network planning applications. And the found parameters should be a good starting point for other energy prediction models. To further enable comparability with other approaches the dataset used for calibration is made publicly available.
In the early phase of new vehicle system developments, it is crucial to fully define and optimize working system and functional architectures. Architecture definition and validation in turn requires a quick and accurate evaluation of a system‟s overall performance. Modeling and simulating a complete vehicle system, however, is complex and in many cases was either technically not achievable or simply has been omitted within the development process. It is the utmost challenge in system modeling and simulation to realistically reflect interaction of various electrical, mechanical, thermal, and software elements as attributed to individual system modules and their relations. State-of-the-art tools meanwhile bear this capability. In this paper we present an approach how they may effectively and efficiently be incorporated into a car system development process. To accomplish this target, we „virtualize‟ all system entities while defining and reflecting all relevant system aspects. Our proposed development flow allows simulating, evaluating, and validating complete vehicle systems and their behavior. The proposed flow will sustainably change car system development processes.
Safety of the Intended Functionality (SOTIF) addresses sensor performance limitations and deep learning-based object detection insufficiencies to ensure the intended functionality of Automated Driving Systems (ADS). This paper presents a methodology examining the adaptability and performance evaluation of the 3D object detection methods on a LiDAR point cloud dataset generated by simulating a SOTIF-related Use Case. The major contributions of this paper include defining and modeling a SOTIF-related Use Case with 21 diverse weather conditions and generating a LiDAR point cloud dataset suitable for application of 3D object detection methods. The dataset consists of 547 frames, encompassing clear, cloudy, rainy weather conditions, corresponding to different times of the day, including noon, sunset, and night. Employing MMDetection3D and OpenPCDET toolkits, the performance of State-of-the-Art (SOTA) 3D object detection methods is evaluated and compared by testing the pre-trained Deep Lea rning (DL) models on the generated dataset using Average Precision (AP) and Recall metrics.
Driving simulators are used to test under reproducible conditions, however, they must be validated for each application. This guarantees that the gathered data on the simulator is representative of real vehicle data. This paper examines and compares objective data from 20 drivers that are recorded on a six degrees of freedom (DOF) high dynamic driving simulator and a passenger vehicle in the compact class on a proving ground. The purpose of this study is to investigate the comparability of the behavior of the subjects in their driving task on the driving simulator compared to the real driving test. The driving maneuvers include the 18 m slalom and an ISO double lane change (ISO 3888-2). The real car’s measurement setup is composed of an inertial measurement unit and access to the chassis CAN messages. The driving simulator is equipped with the same real electrical power steering as the test vehicle. Furthermore, a fully validated vehicle model is used in the simulation. Objective key performance indicators such as maximum steering wheel angle, steering wheel torque, lateral acceleration, yaw rate, and yaw gain deviate from around -18% to 10% in the slalom, with the majority of parameters not showing significant differences. Bigger differences are found for the double lane change. Overall, the results demonstrate a satisfactory degree of correlation between the driver behavior on the driving simulator and the real vehicle, even up to achieving absolute validity.
Comfort evaluation on a dynamic driving simulator with advanced tire, road and vehicle models
(2024)
The topics of automated driving and digitization are becoming increasingly im-portant and will shape the future of mobility. The potential of this technology is enormous. Concurrently, manufacturers want to continue to differentiate them-selves in driving characteristics typical of their brands. Rapid developments re-garding technological changes as well as legal regulations combined with short development times present new challenges for the entire automotive industry. In this context, virtualization and front-loading methods play a major role within the vehicle development. There has been a clear trend of pushing virtual devel-opment via simulation to reduce the number of necessary prototypes. Since however, both engineers and management still rely heavily on the crucial in-sights gained by real road tests, subjective closed-loop assessment must remain a part of this virtual process. Driving simulators have the potential to bridge these gaps, allowing engineers and test drivers to subjectively experience and assess new systems in an early virtual phase of development.
Kempten University of Applied Sciences is working with research and technol-ogy partners to research and further develop their dynamic driving simulator. With the goal to develop use-case specific methods for virtual vehicle develop-ment, the simulator’s novel motion platform is used specifically for research projects in areas requiring high dynamic performance such as vehicle dynamics and ride. This paper describes the methods and solutions developed in an R&D project investigating the simulator’s capabilities for ride comfort evaluation, such as primary & secondary ride. With the goal to enable experienced test drivers to perform a subjective ride evaluation in a very early development phase, the simulator’s real-time environment was extended with the highly so-phisticated tire model FTire. This paper provides an overview of the system’s performance regarding subjective ride assessment. It presents a brief insight into the detailed road modelling and describes the measures taken to ensure real-time capability of the individual model interfaces. Objective performance evaluation shows the benefit of this work for comfort evaluation in early phases of virtual development.
Many cities in Europe and around the world are concerned with reducing their CO2-emissions. One step on this agenda is the introduction of electric buses to replace combustion engines. The electrification of urban buses requires an accurate prediction of the energy demand. In this pa per, an energy model and the underlying calibration process is presented. This approach leverages substantial tracking data from 10 electric buses operated in Göttingen, Germany. It was shown that, with the use of additional information from the directly measured tracking data, like auxiliary power, charging power and vehicle weight, it is possible to precisely calibrate models based on physical equations with regard to generally poorly identifiable parameters like rolling friction coefficient or efficiency of the electric machine. With a multilayered approach for simulating the energy demand, it is possible to validate the results on the mechanical layer and on the electrical layer separately. This enables a far better parametrization and elimination of uncertainties from the different parameters. Furthermore, we compare the results to other publications for sections with 1 km, 100 km and 230 km, respectively. The relative errors between the simulated and measured electrical power consumption are below 0.3%, 3% and 6.5%, respectively. Hence, the yielded model is appropriate for electric urban bus network planning applications. And the found parameters should be a good starting point for other energy prediction models. To further enable comparability with other approaches the dataset used for calibration is made publicly available.
The following paper points out the key role of IT in the future of car development. At the moment a fundamental change in the structure of automotive IT organizations can be observed. The fact that software update cycle in automotive, about 1 year, in comparison with Apple, Google or Tesla is too much. The entertainment industry is constantly proceeding ahead much faster than the automotive industry. On top of this, new emerging platforms like Apple CarPlay and Android Auto are providing the look and the feel of a mobile phone regarding the control of the car. The vehicle itself is getting more and more as an “ultimate mobile application or app”. This shows the need of speeding up the Time-to-Market of new innovations in automotive industry.
The structure of IT departments has to support these process. No wonder that CIOs of car manufacturers are looking for new structures in their IT departments that enable faster cycle update for automotive applications taking in consideration safety and security requirements.
This only represents a particular interest, as for Apple and Google, we can see that Google has already a fleet of 23 self-driving cars in place which has already autonomously driven more than one million miles with only 12 accidents on public roads and Apple is said to work under the project name "Titan" on its own electric car.
Another important aspect is the software running in the car itself, e.g. the software that “fuses” data from sensors into a comprehensible form: objects have to be accurately located in the environment model of the socalled ego vehicle as a basis for decisions making either by the driver himself or even by the software that can determine within a fraction of a second what the car is going to do. High definition maps also play a very important role in enabling autonomous driving, being developed and maintained by companies such as Nokia HERE, with accuracy of only a few centimeters are thought to be of strategic importance for Advanced Driver Assistance Systems and Self Driving Cars.
“We’re the engine room of the system,” says Mr. Ristevski, vice president of reality capture and processing for former Nokia’s mapping unit named HERE. To be independent from Apple and Google maps and with that from possible competitors, it is said to be the main reason why the German premium car manufacturer Audi, BMW and Daimler bought the online map service for about € 2.5 bn. This is only the first step in the restructuring of the automotive industry.
Trajectory Modelling for Autonomous Driving: Investigating the Artificial Potential Field Method
(2024)
Although the focus of autonomous driving is on maximizing safety and efficiency, comfort and familiarity will play a key role in the adoption of autonomous driving. Therefore, it is important to develop algorithms that can mimic human driving skills and adapt to individual driving styles. The potential field method (PFM) is an obstacle avoidance algorithm for autonomous driving that uses a repulsive potential field, as a environment model, to navigate the vehicle to the lowest risk potential. In this paper, the PFM is used in a overtake scenario at high speed, to test the impact of using prediction when calculating the ideal yaw rate. Analysis is done on how the potential field can be used for lane keeping while following a car and then for overtaking it. A driving simulator is used to record human driving data and compare it with automated driving using a PFM as is proposed by [3], with modifications to enable future prediction.
The spectrometer-free chromatic confocal measurement technique enables 3D surface measurements with just one exposure and without scanning. To reduce the need for a spectrometer for the spectral analysis of the reflected light composition and thus the extraction of the local surface height, an optical spectral analysis unit is used. This unit determines the first momentum of the spectral composition reflected from the surface under probe for a large number of lateral measurement points simultaneously. This work investigates the impact of the spectral composition and light power of the light source on the sensitivity and accuracy of this method. A thorough optimization of the light source will be conducted, demonstrating the impact of various spectral compositions and light source power on the system performance, taking into account the system-related etendue. In addition, the optimization of the spectral transmission filter used in the optical spectral analysis unit and its influence on the accuracy and sensitivity of the system over the entire measurement range is shown.
The spectrometer free areal chromatic confocal metrology (ChromaCAM) is an optical 3D surface measurement technology, which allows a simultaneous measurement of a large array of measuring points within a single exposure. In this work, we investigate the accuracy of a first prototype sensor system utilizing this new singleshot 3D measurement technique. It is found that surface height measurement errors smaller 1μm within a total measurement range of about 1000 μm are achievable. Furthermore, several influential factors are investigated showing the advantages and limits of the presented system. Investigating different surface materials it is found that frame rates up to approximately 800 fps for highly reflecting surfaces and up to 30 fps for ceramics, aluminum, and plastics are achievable.
Portable Emission Measurement Technology and RDE on Motorcycles as Instruments for Future Challenges
(2020)
Impact of Inductive Charging Infrastructure at Intersections on Battery Electric Bus Operations
(2023)
Battery electric buses are the go-to solution for clean public transport at the moment. But they come with operational challenges. One technology that could potentially help in some of those is inductive in-motion charging, as it reduces additional dwell times and can help minimize battery size. In addition, the infrastructure can also be used by other road users. This paper presents a framework for evaluating the impact of intersection-based inductive charging infrastructure on battery electric bus operations using a traffic simulation and a physics-based energy demand model. The results are split into two categories: first, investigations of the charging lane lengths on a single intersection with increasing traffic volume, and second, implementation of charging infrastructure along a real bus line to better understand the implications of the charged energy in relation to the energy demand. The findings from the analysis reveal that the higher the traffic volume, the longer the charging lanes need to be to make use of the resulting delay times. The analysis indicates that, in our scenario, the bus can charge around 100% of the required energy with a charging lane length of around 80 meters and a charging power of 250kW. This work can inform decision-making for the deployment of charging infrastructure in urban environments and contribute to the development of sustainable urban transportation systems.
Battery electric buses (BEBs) are gaining prominence in public transportation systems. In this paper, we investi-gate the impact of road grade, passenger load, and recuperation power limits on the energy consumption of BEBs using a physics-based model with tuned parameters. The model was employed to conduct a sensitivity analysis taking into account different altitude data sources, passenger load assumptions, and maximum recuperation power limits. The results highlight the importance of considering the route topology and its interaction with dynamic passenger loading for energy consumption predictions. Further-more, the results indicate that various altitude data sources are feasible to estimate the road grade for this purpose. Apart from that, the sensitivity for recuperation power limitations is shown and put into context. Within a broader framework, the findings suggest that physics-based energy consumption models with optimised parameters can serve as a powerful tool for enhanced operations and planning of BEBs.
Safe Scenario Boundaries Determination by Parameter Variation for an Automated Driving System
(2023)
An expanding area of research interest is the scenario-based testing and development of Automated Driving Systems (ADS). In scenario-based testing, a system is examined in a set of pre-defined scenarios to inspect its behavior. Scenarios are described by a set of parameters, such as velocities and distances. For a safety-related system, identifying the parameter limits for safe operation is essential to reduce harm. Hence, there is a need to determine safe boundaries considering the assumed parameter set in a scenario to support the Verification and Validation (V&V) of an ADS. This paper presents a systematic approach to determining safe boundaries of parameters by scenario-based testing. The contributions of this work are: (i) performing scenariobased parameter variation to detect collisions, (ii) identifying safe boundaries of each single parameter from a specific Operation Design Domain (ODD) and (iii) providing safety-related evidence to identify safe boundaries from defined ODD. The results of this work can assist scenario reduction techniques to derive nothazardous scenarios and hazardous scenarios and support the V&V Processes of ADS.
Safeguarding and type approval of automated vehicles is a key enabler for their market launch in our complex traffic environment. Scenario-based testing by means of computer simulation is becoming increasingly important to cope with the enormous complexity and effort. However, there is a huge gap when assessing the safety of the virtual vehicle while the real vehicle will drive on the road. Simulation must be accompanied by model validation to ensure its credibility since errors and uncertainties are inherent in every model. Unfortunately, this is rarely addressed in the current literature. In this paper, a modular process is presented covering both model validation and safeguarding. It is characterized by the fact that it quantifies a large number of errors and uncertainties, represents them in the form of an error model, and ultimately integrates them into the safeguarding results. It is applied to a type-approval regulation for the lane-keeping behavior of a vehicle under various scenario conditions. The paper contains a thorough validation of the methodology itself by comparing its results with actual ground truth values. For this comparison, a binary classifier and confusion matrices are used that relate the binary type-approval decisions. The classifier demonstrates that the methodology of this paper identifies a systematic error of the simulation model across several safeguarding scenarios. Finally, the paper provides recommendations for alternative configurations of the modular methodology depending on different requirements.
As the development of advanced driver assistance systems (ADAS) continues, more and more software functions and sensors are being introduced to the market. This is accompanied by an increase in the amount of data that has to be transmitted to multiple receivers in the vehicle under hard real-time requirements. The use of deterministic and non-deterministic Fieldbus protocols enables communication between sensor and actuator or ECUs. For the purpose of verifying and validating the developed software modules, but also for type approval, an objective and thus data-driven toolchain is mandatory. By using suitable middleware such as Robotic Operating System (ROS), the complexity of integrating multiple (reference) sensors as well as prototypical software functions can be broken down into subtasks and thus distributed to the hardware in a computationally efficient manner. Recording and manipulating sensor ECU communication while driving is also possible under certain circumstances. However, at least to our knowledge, there is no public ROS driver available to integrate automotive-specific fieldbus protocols except for CAN. In the following paper, we introduce a generic and open-source framework for integrating on-board communication of various Fieldbus protocols and demonstrate the integration in ROS as a real-world use case. To validate the presented methodology, we perform a time analysis of the presented ROS node and compare it to a ROS-independent reference measurement system while performing a standardized vehicle dynamic driving test. In addition, we objectively compare two different on-board sensors from a series vehicle with two distinct reference sensors in a real-world scenario.
The increasing share of distributed energy resources gives rise to new opportunities for deploying innovative business models and coordination schemes within sustainable energy systems. Different concepts entail different implications at socioeconomic, technical and institutional level. Hence, their thorough assessment is key to understanding their actual potential as enablers of the energy transition. Considering this background, we focus on local energy markets as an increasingly discussed approach for coordinating distributed energy systems and introduce a simulative framework for enabling a multi-regional assessment of this concept. Local energy markets bear the potential for increasing the active participation of end consumers, which could increase their acceptance for energy projects in general and their returns on investment, as well as for reducing the peak load on increasingly congested electrical grids by enhancing local energy balancing. We evaluate these hypotheses for twelve representative German regions, for which we formulate assumptions regarding the energy demand as well as the shares of distributed energy resources that are consistently aligned with an overall European energy scenario envisaging a rapid growth of electric vehicles in Germany. For this purpose, we enhance an existing framework for the assessment of local energy markets in order to be able to include the flexibility of the electric mobility sector in local trade activities. The simulation results show that local energy markets have a significant impact on energy systems: First, local trading increases the economic benefits over all participants, who would otherwise only be able to use their generation for self-consumption or direct marketing in central energy markets. Second, local energy balancing increases on average by 60%over all regions. Third, infrastructural relief of the overlaying transmission grids can be accomplished by reducing the yearly peak load at the point of common coupling by 39%on average and at the most by 97%. Furthermore, we find that including electric vehicles in local market activities does not alter but rather reinforces these effects.
Die Kundenzufriedenheit entscheidet über den Erfolg von Produktinnovationen. An der Hochschule für angewandte Wissenschaften Kempten wurde daher eine methodische Studie über die Zufriedenheit mit aktuellen Fahrerassistenzsystemen durchgeführt, deren Erkenntnisse der Ausrichtung zukünftiger Entwicklungen dienen.
Carsharing is an integral part of the transformation toward flexible and sustainable mobility. New carsharing programs are entering the market to challenge large operators by offering innovative services. This study investigates the use of generative machine learning models for creating synthetic data to support carsharing decision–making when data access is limited. To this end, it explores the evaluation, selection, and implementation of leading-edge methods, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), to generate synthetic tabular transaction data of carsharing trips. The study analyzes usage data of an emerging carsharing program that is expanding its services to include free-floating electric vehicles (EVs). The results show that augmenting real training data with synthetic samples improves predictive modeling of upcoming trips by up to 4.63%. These results support carsharing researchers and practitioners in generating and leveraging synthetic mobility data to develop solutions to real-world decision support problems in carsharing.
Many modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor. Often, this effect is filtered, by using sensor data fusion, to use an undistorted point cloud for object detection. In this study, we developed a method using an artificial neural network to estimate an object’s velocity and direction of motion in the sensor’s field of view (FoV) based on the motion distortion effect without any sensor data fusion. This network was trained and evaluated with a synthetic dataset featuring the motion distortion effect. With the method presented in this paper, one can estimate the velocity and direction of an OoI that moves independently from the sensor from a single point cloud using only one single sensor. The method achieves a root mean squared error (RMSE) of 0.1187 m s−1 and a two-sigma confidence interval of [−0.0008 m s−1, 0.0017 m s−1] for the axis-wise estimation of an object’s relative velocity, and an RMSE of 0.0815 m s−1 and a two-sigma confidence interval of [0.0138 m s−1, 0.0170 m s−1] for the estimation of the resultant velocity. The extracted velocity information (4D-LiDAR) is available for motion prediction and object tracking and can lead to more reliable velocity data due to more redundancy for sensor data fusion.
Automated vehicles use light detection and ranging (LiDAR) sensors for environmental scanning. However, the relative motion between the scanning LiDAR sensor and objects leads to a distortion of the point cloud. This phenomenon is known as the motion distortion effect, significantly degrading the sensor’s object detection capabilities and generating false negative or false positive errors. In this work, we have introduced ray tracing-based deterministic and analytical approaches to model the motion distortion effect on the scanning LiDAR sensor’s performance for simulation-based testing. In addition, we have performed dynamic test drives at a proving ground to compare real LiDAR data with the motion distortion effect simulation data. The real-world scenarios, the environmental conditions, the digital twin of the scenery, and the object of interest (OOI) are replicated in the virtual environment of commercial software to obtain the synthetic LiDAR data. The real and the virtual test drives are compared frame by frame to validate the motion distortion effect modeling. The mean absolute percentage error (MAPE), the occupied cell ratio (OCR), and the Barons cross-correlation coefficient (BCC) are used to quantify the correlation between the virtual and the real LiDAR point cloud data. The results show that the deterministic approach matches the real measurements better than the analytical approach for the scenarios in which the yaw rate of the ego vehicle changes rapidly.
The effect of ionomer to carbon (I/C) weight ratio and relative humidity (RH) on cathode catalyst degradation was investigated by comprehensive in situ characterization. Membrane electrode assemblies (MEA) with I/C ratios of 0.5, 0.8 and 1.2 were subjected to an accelerated stress test performed at 40, 70 and 100% RH. The results show an increasing loss in electrochemical active surface area (ECSA) for both higher I/C ratios and RH during voltage cycling. To differentiate between ionomer and water connected ECSA, carbon monoxide stripping measurements were performed at varying RH. Before degradation, all MEAs show comparable total ECSA values, while higher I/C ratios lead to a larger fraction of ionomer connected ECSA. After degradation, ECSA measurements of the lowest I/C ratio showed a relatively higher loss of Pt in contact with ionomer than Pt in contact with water, while an opposite trend was observed for higher I/C ratios. H2 /N2 impedance measurements showed drastically increasing protonic catalyst layer resistances for decreasing RH especially at low I/C ratios, which might hinder Pt 2+ ion diffusion towards the membrane, hence decreasing the ECSA loss. Limiting current measurements show increasing molecular O 2 diffusion resistances at end of test for samples with higher I/C ratios and higher ECSA loss.
The development of Automated Driving Systems (ADS) has the potential to revolutionize the transportation industry, but it also presents significant safety challenges. One of the key challenges is ensuring that the ADS is safe in the event of Foreseeable Misuse (FM) by the human driver. To address this challenge, a case study on simulation-based testing to mitigate FM by the driver using the driving simulator is presented. FM by the human driver refers to potential driving scenarios where the driver misinterprets the intended functionality of ADS, leading to hazardous behavior. Safety of the Intended Functionality (SOTIF) focuses on ensuring the absence of unreasonable risk resulting from hazardous behaviors related to functional insufficiencies caused by FM and performance limitations of sensors and machine learning-based algorithms for ADS. The simulation-based application of SOTIF to mitigate FM in ADS entails determining potential misuse scenarios, conducting simulation-based testing, and evaluating the effectiveness of measures dedicated to preventing or mitigating FM. The major contribution includes defining (i) test requirements for performing simulation-based testing of a potential misuse scenario, (ii) evaluation criteria in accordance with SOTIF requirements for implementing measures dedicated to preventing or mitigating FM, and (iii) approach to evaluate the effectiveness of the measures dedicated to preventing or mitigating FM. In conclusion, an exemplary case study incorporating driver-vehicle interface and driver interactions with ADS forming the basis for understanding the factors and causes contributing to FM is investigated. Furthermore, the test procedure for evaluating the effectiveness of the measures dedicated to preventing or mitigating FM by the driver is developed in this work.
As the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energy consumption, like altitude, temperature, passenger load, and driving patterns. In order to comprehensively understand energy demands under varying operational conditions, the methodology integrates data-driven models and physical simulations into a modular and highly accurate energy predictor. The results demonstrate the effectiveness of our approach in providing more accurate energy consumption forecasts, which is essential for efficient electric bus fleet management. This research contributes to the growing body of knowledge in electric vehicle energy prediction and offers practical insights for transit authorities and operators in optimizing electric bus operations.
In this article, a system for speed estimation of vehicles in road traffic is presented. Using a state-of-the-art Convolutional Neural Network (CNN) for object detection, vehicles are first recognized as objects in the image material captured by a monocular camera, e.g. a mobile phone. In order to prevent the fluctuations of the bounding boxes of the detected vehicles from affecting the calculated velocity, a subsequent computer vision step is performed where the license plate of each individual vehicle is recognized based on a canny edge detection algorithm and a rectangular bounding box is drawn around the license plate. Repeating this for each individual frame in the video image material and observing the change of size of the license plate, the velocity of the vehicle is estimated based on the intercept theorem.
Im vorliegenden Beitrag wird eine Methode zur subjektiven und objektiven Charakterisierung von aktiven Fahrstreifenwechselfunktionen sowie eine Korrelationsanalyse zur Ermittlung optimaler Funktionseigenschaften vorgestellt. Zur Quantifizierung maßgeblicher subjektiver Eigenschaften wurden Bewertungskategorien und -kriterien aus den Bereichen Fahrerkooperation, Funktionsperformance, Entlastungsgrad und Sicherheitsgefühl erarbeitet, deren Beurteilung im Rahmen einer umfassenden Fahrstudie erfolgte. Die beurteilten Fahrzeuge wurden hinsichtlich ihrer unterschiedlichen Funktionsausprägungen anschließend in einem neuartigen fahrmanöverbasierten Prüfverfahren vermessen. Das Verfahren umfasst hierbei drei Typen von Fahrstreifenwechselszenarien in welchen unter anderem die Eigen- und Relativbewegung von Ego- und Target-Fahrzeug sowie die Funktionsrückmeldung am Lenkrad und im Kombi-Instrument des Egofahrzeugs messtechnisch erfasst wurden. Die Auswertung des hiermit aufgezeichneten objektiven Funktionsverhaltens geschieht durch eine automatisierte KPI-basierte Softwareumgebung. Ausgehend von der korrelativen Gegenüberstellung aller Subjektivkriterien mit den ermittelten KPI-Kennwerten können wichtige Trends und Zusammenhänge geprüft, erkannt und nutzbringend in die Festlegung optimaler Wertbereiche eingearbeitet werden. Die vorgestellte Methodik ermöglicht somit eine zielgerichtete Auslegung und Abstimmung der Eigenschaften einer aktiven Fahrstreifenwechselfunktion.
In this article, the optimization of the control circuit and path planning of a delta kinematic with the help of machine learning is presented. The described delta kinematic is primarily used for pick-and-place applications in the field of packaging machines. The optimization of the path planning procedure aims to make the workflow more efficent and flexible for commissioning the delta kinematic. By optimizing the control circuit using machine learning, mechanical oscillations and the deviation of the specified path are to be minimized. The possible use of a simulator for training, the prediction quality and the implementation on the robot controller are discussed. Furthermore, the path planning procedure was optimized. For this purpose, an environment was implemented in which a reinforcement learning agent plans the path of the robot between a starting point and a target point in a time-optimized manner, considering interference contours e.g. from the machine. The obtained results show the optimization of the robot by machine learning with a root mean squared error of the predicted torques of 0.06025 Nm in a prediction time of around 0.125 ms and the possibility of path planning with different criteria.
Scenario-based testing is essential for Highly Automated Driving (HAD) vehicles to determine the safety-related input parameters and their boundaries. The increasing complexity, vehicle functions, and operational design pose new challenges for scenario-based testing, as the number of scenarios is enormous. Therefore, an efficient and systematic process is required in the various stages of scenario-based testing. The contribution of this study is to provide sensitivity information of safety related parameters and support logical scenario reduction. This paper presents an approach that supports to optimize the safety-related parameters boundary towards logical scenario reduction. Additionally, sensitivity analysis is applied by computing Variance- Based Sensitivity Analysis (VBSA) indices and prioritize the input parameters. Two datasets are investigated by VBSA based on the input parameters. One dataset is based on the samples from realworld scenarios and other dataset is derived from the samples considering statistic distributions with a specific parameter range. Moreover, the proposed approach is applied to an exemplary use case and the outcomes are demonstrated.
Sustainability is a critical challenge in modern tourism, exacerbated by climate change and globalization. Thanks to digitization, data-driven approaches constitute a key technology for addressing related issues, such as overtourism. However, the overarching complexity of the touristic data landscape, amplified by the interplay of diverse digital platform ecosystems, poses considerable challenges to both data owners and consumers. To mitigate such issues, knowledge graphs (KGs) have received significant attention. KGs focus on data quality by employing unified data models and continuous data refinements, making them well-suited for data-driven applications. Although promising, many challenges must be addressed to make KGs useful in practice. This paper overviews the state of the art of the field and identifies avenues for future research, explicitly focusing on touristic value and sustainability. Following our results, future research should focus on different areas, notably real-time knowledge graph population, distributed and parallelized processes, and ontologies for dynamic data types.
Motion sickness research has always been shaped by current events. With the advent of highly automated vehicles (HAVs), the topic is currently being revisited as 60% of users of HAV functions are expected to suffer from motion sickness. Failure to address this condition will jeopardize user acceptance of HAV functions. We investigated the vestibular mechanisms of motion misinterpretation and hypothesized that cross-coupled stimuli induce more sensory conflict and lead to higher motion sickness incidence compared to the non-coupled control condition. We conducted an experiment on a dynamic driving simulator with realistic motion profiles and analyzed the influence of cross-coupled motion on motion sickness incidence. Results show no significant difference in motion sickness incidence between cross-coupled and non-coupled motion profiles. Further research is needed to investigate the thresholds of the Coriolis effect and should include the measurement of compensatory or inertial head motion of participants.
This paper has the goal to propose an approach to enable reliable planning of BEBs in a traffic simulation which acts as the virtual environment. It was shown that the defaults for the vehicle trajectories from the traffic simulation SUMO, introduce significant errors. With some fine tuning and optimization of the vehicle behavior, reasonable results can be obtained from the traffic simulation. This can be used for planning of new bus networks considering many aspects, like energy demand, impact of traffic on delays and the resulting charging times at terminal stations.
Simulation-Based Testing of Foreseeable Misuse by the Driver Applicable for Highly Automated Driving
(2024)
With highly automated driving (HAD), the driver can engage in non-driving-related tasks. In the event of a system failure, the driver is expected to reasonably regain control of the automated vehicle (AV). Incorrect system understanding may provoke misuse by the driver and can lead to vehicle-level hazards. ISO 21448, referred to as the standard for safety of the intended functionality (SOTIF), defines misuse as usage of the system by the driver in a way not intended by the system’s manufacturer. Foreseeable misuse (FM) implies anticipated system misuse based on the best knowledge about the system’s design and the driver’s behavior. This is the underlying motivation to propose simulation-based testing of FM. The vital challenge is to perform a simulation-based testing for a SOTIF-related misuse scenario. Transverse guidance assist system (TGAS) is modeled for HAD. In the context of this publication, TGAS is referred to as the “system”, and the driver is the human operator of the system. This publication focuses on implementing the driver-vehicle interface (DVI) that permits the interactions between the driver and the system. The implementation and testing of a derived misuse scenario using the driving simulator ensure reasonable usage of the system by supporting the driver with unambiguous information on system functions and states so that the driver can conveniently perceive, comprehend, and act upon the information.
A solution to the electrical urban transit routing problem with heterogeneous characteristics
(2023)
The already highly complex Urban Transit Routing Problem (UTRP) that serves to find efficient travelling routes for Public Transport (PT) systems is extended into the Heterogeneous Electric - Urban Transit Routing Problem (HE-UTRP). This extension focuses on step-by-step transformation of public bus transportation systems to electric mobility. The heterogeneity characteristics refers to the fleet and charging infrastructure. This article presents a framework that allows the generation, analysis and optimisation of PT Route Networks (RNs) for the HE-UTRP. In addition to the analysis of different charging technologies and Charging Locations (CLs), the approach enables a transformation process towards electrification of PT systems by presenting substitution scenarios as well as the resulting cost structure. The framework, based on a Sequence-based Selection Hyper-heuristic - with Great Deluge (SS-GD), is tested against varying objective functions and UTRP, HE-UTRP and Electric Transit Route Network Design Problem (E-TRNDP) instances.
A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
(2023)
In this work, we introduce a novel approach to model the rain and fog effect on the light detection and ranging (LiDAR) sensor performance for the simulation-based testing of LiDAR systems. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. The time domain analysis permits us to benchmark the virtual LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. In addition, the detection rate (DR), false detection rate (FDR), and distance error derror of the virtual LiDAR sensor due to rain and fog droplets are evaluated on the point cloud level. The mean absolute percentage error (MAPE) is used to quantify the simulation and real measurement results on the time domain and point cloud levels for the rain and fog droplets. The results of the simulation and real measurements match well on the time domain and point cloud levels if the simulated and real rain distributions are the same. The real and virtual LiDAR sensor performance degrades more under the influence of fog droplets than in rain.
This paper is a summary of the creation and data usage of an autonomous model vehicle which was recreated in CarMaker[1]. The simulation data come to use when following students of this semester project will develop algorithms and simulations which are impractical to test or train in real life. This paper starts with a summary of the background information around the model vehicle and the software CarMaker[1], then reproduces the construction process, digs deeper in ROS[6] and finishes with the extraction of the data.
The catalyst coated membrane represents the heart of a polymer electrolyte fuel cell (PEMFC) and is produced by directly applying a catalyst suspension on the membrane or a decal transfer foil. Understanding the rheology of catalyst inks is crucial to optimize the printed microstructure of the catalyst layer and hence the performance of the fuel cell. The suspension consists of platinum on carbon powder, ionomer dispersion (perfluorosulfonic acid ionomer) and solvents. Its rheology is directly affected by various material properties of all ink components. Therefore, measuring the flow curves represents a suitable tool for quality control at the beginning of the production line.
An artifact appearing during the cathodic transient of cyclic voltammograms (CVs) of low-loaded platinum on carbon (Pt/C) electrodes in proton exchange membrane fuel cells (PEMFCs) was examined. The artifact appears as an oxidation peak overlapping the reduction peak associated to the reduction of platinum oxide (PtOx). By varying the nitrogen (N2) purge in the working electrode (WE), gas pressures in working and counter electrode, upper potential limits and scan rates of the CVs, the artifact magnitude and potential window could be manipulated. From the results, the artifact is assigned to crossover hydrogen (H2X) accumulating in the WE, once the electrode is passivated towards hydrogen oxidation reaction (HOR) due to PtOx coverage. During the cathodic CV transient, PtOx is reduced and HOR spontaneously occurs with the accumulated H2X, resulting in the overlap of the PtOx reduction with the oxidation peak. This feature is expected to occur predominantly in CV analysis of low-loaded electrodes made of catalyst material, whose oxide is inactive towards HOR. Further, it is only measurable while the N2 purge of the WE is switched off during the CV measurement. For higher loaded electrodes, the artifact is not observed as the electrocatalysts are not fully inactivated towards HOR due to incomplete oxide coverage, and/or the currents associated with the oxide reduction are much larger than the spontaneous HOR of accumulated H2X. However, owing to the forecasted reduction in noble metal loadings of catalyst in PEMFCs, this artifact is expected to be observed more often in the future.
Nano-porous materials can be imaged spatially by focused ion beam scanning electron microscopy (FIB-SEM). This method generates a stack of SEM images that has to be segmented (or reconstructed) to serve as basis for structural characterization. To this end, we apply two state-of-the-art algorithms. We study the influence of the original image’s voxel size on estimates of morphological characteristics and effective permeabilities. Special attention is paid to analyzing anisotropies due to the FIB-SEM typical anisotropic sampling. Quantitative comparison of morphological descriptors and flow properties of reconstructed data is enabled by the use of synthetic FIB-SEM sets for which a ground truth is available. Moreover, in that case, reconstruction parameters can be chosen optimally, too.
We present a model of the cathode catalyst layer morphology before and after loading a porous catalyst support with Pt and ionomer. Support nanopores and catalyst particles within pores and on the support surface are described by size distributions, allowing for qualitative processes during the addition of a material phase to be dependent on the observed pore and particle size. A particular focus is put on the interplay of pore impregnation and blockage due to ionomer loading and the consequences for the Pt/ionomer interface, ionomer film thickness and protonic binding of particles within pores. We used the model to emulate six catalyst/support combinations from literature with different porosity, surface area and pore size distributions of the support as well as varying particle size distributions and ionomer/carbon ratios. Besides providing qualitatively and quantitatively accurate predictions, the model is able to explain why the protonically active catalyst surface area has been reported to not increase monotonically with ionomer addition for some supports, but rather decrease again when the optimum ionomer content is exceeded. The proposed model constitutes a fast translation from manufacturing parameters to catalyst layer morphology which can be incorporated into existing performance and degradation models in a straightforward way.
In this work, flatbed screen printing is evaluated regarding its capability to produce catalyst layers of PEM fuel cells. In the field of printed electronics, screen printing is regarded as robust and high-throughput coating technology. The possibility of in-plane structuring could be an additional degree of freedom, enabling more complex designs of catalyst layers in the future. In this study, process parameters are varied to investigate their effect on resulting layer thickness, homogeneity, and Pt-loading. With the usage of different screens, the Pt-loading can be adjusted. Additionally, two different pastes with and without water content are investigated. The catalyst paste without water showed a better process stability during printing and performed best under dry conditions (RH = 40%) and worst under wet conditions (RH = 100%) during electrochemical in-situ testing. Overall, the reproducibility of the CCM production process was verified. The viscosity of the catalyst paste with 19.55 wt% water in solvent was higher compared to the paste without water. Furthermore, a carbon paste (Pt-free) is developed in a similar viscosity range as the catalyst pastes. The main challenge of screen printing process development lies in the paste optimization to prevent evaporation effects over time, ensuring sufficient wetting of the paste on the substrate and sufficient fuel cell performance.
Green information systems are often praised for their potential to foster sustainability in citizens’ daily lives and meet their needs. With this focus on citizens, districts that use smart technologies provide a litmus test, the results of which will indicate how to design smart green information systems that better meet the needs and desires of citizens. To date, however, guidelines on how to design such green information systems in urban areas or actively involve citizens in this process are few and far between. In recognition and remedy of this shortage, the study draws on the design science research paradigm to develop seven design principles for citizen-centric green information systems that can be used in sustainable smart districts. These principles are evaluated in 15 semi-structured interviews and a prototype of a mobile district app for a citizen-centric green information system is instantiated. By taking this citizen-centric perspective, the paper fosters the active involvement of humans in the design of sustainable urban environments.
Punching is a process that is sensitive to a multitude of parameters. The estimation of part and punch quality is often based on expert knowledge and trial-and-error methods, mostly carried out as a separate offline process analysis. In a previous study, we developed an optical inline monitoring system with subsequent image processing which showed promising results in terms of capturing every manufactured part, but was limited by slow image processing. Here, we present a more efficient image processing technique based on neural networks. For our approach, we manually identify the burnish parts in images based on criteria established via an expert survey in order to generate a training dataset. We then employ a combination of region-based and boundary-based losses to optimize the neural network towards a segmentation of the burnish surface which allows for an accurate measurement of the burnish height. The hyperparameter optimization is based on custom evaluation metrics that reflect the requirements of the burnish surface identification problem as well. After comparing different neural network architectures, we focus on optimizing the backbone of the UNet++ structure for our task. The promising results demonstrate that neural networks are indeed capable of an inline segmentation that can be used for measuring the burnish surface of punching parts.
In recent years, new challenges have emerged in the automotive sector, particularly in the ADAS/AD domain. The development and testing of such functionality require not only efficient processing and analysis of a rapidly growing amount of recorded vehicle data, but also the ability to deal with a diverse set of new sensor and data types. With its ADAS/AD Big Data & Analytics Platform, AVL addresses these challenges and of-fers a solution that enables highly efficient and scalable search, visualization and anal-ysis of large data sets in an integrated way. A key aspect here is the abstraction of the involved big data mechanisms, since typical users are domain but not big data experts. Additionally, by following standard formats such as Open Simulation Interface (OSI), the ADAS/AD Big Data & Analytics Platform is also broadly applicable. In order to demonstrate the benefits of our platform, we show how it can be applied to logically describe, identify and analyze complex and custom driving scenarios and how tech-nical and legal requirements, such as on an automatically commanded steering function, can be efficiently verified on a large number of test drives.
To ensure the safety and security of Automated Vehicles (Avs), the interaction between the Functional Safety (FuSa) and the Cybersecurity (CS) domains needs to be managed systematically. There is a demand to develop effective and structured management systems to support the homologation process. From this motivation, identifying the interaction between the Safety Management System (SMS) and the Cybersecurity Management System (CSMS) is a fundamental aspect and needs to be improved for HAD systems. Hence, the classical Decision Making Trial and Evaluation Laboratory (DEMATEL) method and fuzzy DEMATEL are applied to evaluate the influential factors that can impact the safety and security of the HAD systems. This paper proposes a list of influencing factors focusing on the interaction between SMS and CSMS for HAD systems. Additionally, the results of an anonymously conducted survey among experts from industry and research are presented and used as inputs for the methods. This work helps to understand the relationship between influencing factors and provides a simplified, easy-to-visualized, and valuable guide for developing HAD systems. The result of this study shows that the most important influential factor is F13. Moreover, the cause and effect of the factors are illustrated numerically and graphically. The influential factors F1 to F7 are identified as the cause and F8 to F13 are reasoned to effect. Finally, a circular representation of the influential factors and their interaction is presented in this paper.
In Richtlinien zu Qualität und Bewertung von Glockenklängen werden seit den 1950er und 1960er Jahren akustische Merkmale wie klangliche Helligkeit, Teiltonstruktur, Grundtönigkeit sowie das Ein- und Ausschwingen des Glockenklangs als relevante Faktoren beschrieben. Der Wohlklang einer Glocke soll dabei vor allem durch eine weiche, volle, helle und klare Tongebung mit einem grundtönigen, geräuscharmen Klangspektrum erzielt werden, in dem ein leuchtend-klar ausgeprägter Schlagton mit möglichst wenig Nebenschlagtongeräuschen hörbar ist. In einem Hörversuch von Wolfgang Aures beschrieben 16 Versuchspersonen insgesamt 17 verschiedene Glockenklänge als wohlklingender, wenn die klangliche Schärfe abnahm, während die Rauigkeit bei Aures keinen Einfluss auf die Bewertung des Wohlklangs hatte. Als besonders ausschlaggebend für die Klangqualität erwies sich in einem 2021 von Andreas Rupp, Denis Spieß und Michael Plitzner vorgestellten Verfahren die Gestaltung des Klöppels. Sie zeigten, dass die Anteile der hohen Frequenzen im Spektrum durch die Klöppelmasse bestimmt werden (je geringer die Klöppelmasse, desto heller der Klang), wohingegen das Gewicht und die Anfluggeschwindigkeit des Klöppels vor allem die tiefen Anteile im Spektrum beeinflussen (je schwerer der Klöppel und je langsamer der Anflug, also je länger die Verweildauer des Klöppels an der Glocke, desto grundtöniger der Klang). Basierend auf diesen Erkenntnissen führten Christoph Reuter et al. 2022 einen Hörversuch durch, bei welchem sich eine geringe Ausprägung der großen Terz im Spektrum, eine geringe Inharmonizität und eine geringe Highest Peak Frequency als Prädiktoren für die empfundene Glockenqualität beobachten ließen. Glocken, bei denen gezielt durch die Änderung des Klöppels eine Verbesserung der empfundenen Klangqualität angestrebt wurde, wiesen weniger Geräuschanteile, stärker ausgeprägte harmonische Spektralanteile sowie eine geringere Helligkeit auf und wurden signifikant besser bewertet. Allerdings war die Varianzaufklärung in den Urteilen der Versuchspersonen nur akzeptabel (besonders unter den Expert:innen herrschte Uneinigkeit), was nahelegt, dass ein objektives Qualitätsurteil wenig Validität sowie Reliabilität aufweisen würde und vielmehr die Einflussfaktoren auf individuelle Präferenz als Ursache der hohen Streuung in den Fokus gerückt werden sollten.
Geläute heute – Automatisch extrahierte Audiomerkmale zur Klangbeurteilung von läutenden Glocken
(2022)
Nach allgemeiner Auffassung zeichnet sich der Klang einer wohlklingenden Glocke durch eine weiche, volle, helle und klare Tongebung mit einem grundtönigen, geräuscharmen Klangspektrum aus, in dem ein leuchtend-klar ausgeprägter Schlagton mit möglichst wenig Nebenschlagtongeräuschen hörbar ist [1]. Mit Hilfe von signalanalytischen Methoden lassen sich Beschreibungen wie "weiche, volle, helle und klare Tongebung" in Form bestimmter Audiosignaleigenschaften erfassen, so dass anhand dieser Messwerte eine objektive Bewertung der Angenehmheit und Qualität von läutenden Glocken in eine greifbare Nähe rückt. Darüber hinaus können berechenbare Klangmerkmale Aufschluss über die bei einer klanglichen Optimierung von Glocken (bzw. deren Klöppel) verbundene Qualitätssteigerung geben. In einem Pilotexperiment bewerteten 11 Glockenexpert*innen und 26 Laien das Läuten von 40 lautheitsangeglichenen Glockenaufnahmen unter dem Aspekt von klanglicher Qualität, Angenehmheit, Grundtönigkeit, Klarheit und Weichheit des Anschlags. Via Signalanalyse wurden die Glockenklänge auf 127 berechenbare Audiomerkmale hin untersucht. Über Korrelations- und Regressionsanalysen wurden Zusammenhänge zwischen den Glockenklang-Bewertungen und den errechneten Audiomerkmalen ermittelt. Erste Auswertungen lassen darauf schließen, dass die empfundene Qualität vor allem mit der Ausgeprägtheit der kleinen Terz im Klang zusammenhängt sowie mit dem Anschlagstempo (je langsamer desto besser) und der Weichheit des Anschlags (je weicher desto besser). Ähnliches gilt für die empfundene Angenehmheit. Zu Bedenken ist dabei, dass sowohl innerhalb der Laien- als auch Sachverständigengruppe lediglich geringe Übereinstimmungen bei der Beurteilung der Klänge nach Qualität und Angenehmheit vorzufinden sind. Die mit einer Glockenoptimierung durch Veränderung des Klöppels einhergehenden Klangeigenschaften zeigen sich besonders in der klanglichen Helligkeit (Timbral Brightness), der Ausgeprägtheit tonaler Anteile im Spektrum (Harmonic Energy) und der Geräuschanteile (Spectral Flux). Durch die Veränderung des Klöppels lassen sich Glocken gezielt in ihrer Klangfarbe angenehmer gestalten. Mit zunehmendem Klöppelgewicht und kleinerem Läutewinkel erweisen sie sich klanglich als grundtöniger, mit stärkeren harmonischen Anteilen und ärmer an Nebengeräuschen. Je größer der Glockendurchmesser, die Schlagringdicke und die Glockenmasse, desto angenehmer wird der Klang empfunden. Ähnliches gilt für die Klöppeleigenschaften: Je größer die Klöppelmasse und der Klöppelballendurchmesser und je kleiner der Klöppel-Läutewinkel, desto angenehmer das empfundene Klangerlebnis.
Der musikalische Fingerabdruck hat sich in den vergangenen Jahren als Mittel zur Zustandsanalyse von Glocken in Bezug auf mögliche vorhandene Risse im Klangkörper von Glocken etabliert, über 500 Glocken wurden bereits untersucht. Auf Basis der umfangreichen Datenlage konnte das Verfahren weiter verfeinert werden, so dass unter anderem bisher unentdeckte Risse in der Glockenflanke identifiziert werden konnten. Dieser bisher weitgehend unberücksichtigte Schadenstyp erfordert eine Anpassung der Klangaufnahme, um die Zuverlässigkeit der Analysemethode zu erhöhen. Um Ermüdungsrisse jedoch in einem derart frühen Stadium zu erkennen, dass sie noch keinen hörbaren Einfluss auf das Klangverhalten der Glocke haben, ist eine dauerhafte Überwachung erforderlich. Mithilfe eines allgemein zugänglichen Glockenatlasses bzw. durch eine Vernetzung aller Initiativen, die bereits auf dem Gebiet der Glockenarchivierung tätig sind, können Glockenschäden zuverlässig und frühzeitig erkannt werden.
Glocken sind an der Glockenkrone an einem Holz- oder Stahljoch befestigt und werden in unserem Kulturraum schwingend geläutet. Der Glockenkörper wird mittels Spannelementen an den Kronenbügeln und bei historischen Glocken auch am Kronenstock gehalten. Bei Glocken mit Mittelbohrung wird im Allgemeinen eine Mittelschraube zur Befestigung hinzugefügt, die gleichzeitig als Klöppelhalterung in der Glocke genutzt wird. Aufgrund des Klöppelanschlags ist das Läuten von Glocken mit hohen Belastungen im Klangkörper der Glocke aber kaum im Bereich des Glockendeckels und der Krone verbunden. Zusätzlich wird die Glocke durch das bloße Schwingen belastet, wodurch in der Regel geringe Beanspruchungen im Bereich der Krone entstehen. Gleichzeitig wird immer wieder über Schäden an der Krone von Glocken berichtet, die an gekröpften Jochen aufgehängt sind. Dieser Beitrag untersucht die physikalischen Hintergründe zu den beim Läuten an der Krone entstehenden Beanspruchungen in Abhängigkeit der Verkröpfung und des Läutewinkels.
Ein Läuteantrieb bestehend aus hochwertigen Steuer- und Regelkomponenten wurde entwickelt, um systematisch die Einflüsse des Antriebs auf das Läuten zu untersuchen. Die Steuerung erlaubt eine detaillierte Parametrisierung des Antriebsverlaufs, die Stellgröße ist das parametrisierte Motormoment für den für jede Seite einstellbaren Läutewinkel. Läuteversuche wurden an einer Glocke mit unterschiedlichen Klöppel unter Variation wesentlicher Parameter vorgenommen. Mit den Ergebnissen wird der Einfluss des Antriebs auf den erreichten Läutewinkel, die Symmetrie der Antriebsmomente, die erforderliche Antriebsenergie, die Rhythmik der Anschläge und die Anschlagsintensität sowie Schädigung für unterschiedliche Läutewinkel gezeigt.
Ein Glockensystem soll mit seinen Komponenten und seinem Aufbau unterschiedliche, zum Teil einander gegenläufige Anforderungen erfüllen. Dabei spielen die Dimensionierung und Form-gebung des Klöppels ebenso eine Rolle, wie sämtliche Eigenschaften, die das Läuten einer Glo-cke im Turm mitbestimmen. Die Forschungs- und Entwicklungsarbeiten an Glocken durch das ECC-ProBell der vergangenen Jahre hatten zum Ziel, die physikalischen Zusammenhänge im Hinblick auf Ermüdungsfestigkeit, Schlagverschleiß und Klang ingenieursmäßig zu beschreiben. Hieraus wurden Verfahren und Daten abgeleitet, die es erlauben, eine Glocke mit Klöppel ei-nerseits schonend bei gleichzeitiger hoher musikalischer Qualität zu läuten und andererseits die Anforderungen, die aus dem Bereich baulicher Randbedingungen, Denkmalschutz und Ästhetik kommen, zu erfüllen. Dies führt dazu, dass eine optimale Lösung für jedes Glockensystem speziell erarbeitet werden kann – woraus der „ProBell-Klöppel“ resultiert.
Die öffentlich geförderte Forschung an Glocken feiert ihr 25-jähriges Jubiläum. Ein Grund zu feiern und zu betrachten, welche Erkenntnisse und welches Verständnis sich über diese Zeit entwickelt und wie dieses Wissen Eingang bzw. Aufstieg in die Kirchtürme gefunden hat. Wir freuen uns daher sehr, das 3. Glockensymposium des Europäischen Kompetenzzentrums für Kirchenglocken ECCProBell ® veranstalten zu können, in dem wir zum aktuellen Stand von Wissenschaft und Technik berichten wollen. Viel Raum soll dabei die Diskussion mit allen Teilnehmenden haben, welche Fragestellungen denn heute von Bedeutung sind, um unsere wissenschaftlichen Untersuchungen für die nächsten Jahre danach auszurichten. Mit der Frage nach dem Einfluss des Drehens von Glocken auf die Lebensdauer begann im Dezember 1996 die systematische Forschung an Glocken im Hinblick auf ihre Schädigung. Ein erstes Projekt im Auftrag des Verbandes der Deutschen Gießereifachleute VDG am Fraunhofer Institut LBF beantwortete einige Fragen, zeigte aber zugleich eine ganze Anzahl ungeklärter Zusammenhänge und Phänomene auf. Die daraufhin im Jahre 2005 an der Hochschule Kempten mit Förderung der Europäischen Union aufgenommenen Forschungsarbeiten hatten zum Ziel, den Schutz und die Wartung von Glocken sowie Phänomene rund um das Thema der Schädigung mit modernen Ingenieurswerkzeugen zu untersuchen. Wegen des großen Interesses an den Ergebnissen wurde im Mai 2009 im Rahmen des 1. Glockensymposiums das Europäische Kompetenzzentrum für Glocken ECC-ProBell® gegründet, das als öffentlich geförderte Einrichtung der Hochschule einerseits Forschung betreibt und andererseits als Dienstleister Firmen, Kirchen, Gemeinden und die Denkmalpflege mit dem erarbeiteten Know-how unterstützt. Beim 2. Glockensymposium im März 2018 wurden unter anderem die Anwendung der Methoden und Verfahren zur Bestimmung des Schadensrisikos läutender Glocken und zur Einstellung eines schonenden Läutens bei hoher Klangqualität sowie der musikalische Fingerabdruck als Werkzeug zur Schadenserkennung und Überwachung zusammen mit dem ECC-Probell-Glockenatlas behandelt. Die Forschung an Glocken basiert neben Untersuchungen im Labor, Computer-Simulationen und Datenanalysen immer auch auf Messungen beim Läuten der Glocken in den Kirchtürmen. Dabei besteht die Herausforderung darin, die physikalischen Zusammenhänge und die Ingenieurswerkzeuge so zu entwickeln, dass sie auf jede einzelne Glocke mit ihren je eigenen Läutebedingungen und Eigenheiten angewandt werden können. Dafür wird ein grundlegendes Verständnis der wesentlichen Zusammenhänge im Hinblick auf Schwingungen, Pendel, Antrieb, Klang, Guss und Material, Materialermüdung, Verschleiß, Schadensentstehung und -fortpflanzung, Akustik, Klangempfinden, Läutekultur, Denkmalschutz und sicher noch einiges mehr benötigt. Mit mittlerweile über 400 untersuchten Glocken im In- und Ausland, zuletzt auch in Salvador da Bahia in Brasilien, liegen ein einzigartiger Fundus an Daten für umfassende Forschungen vor. Das Programm des 3. Glockensymposiums gibt einen Überblick über die Erkenntnisse und eingeführten Verfahren, stellt neueste Ergebnisse zu den aktuellen Forschungsthemen Antrieb, Kronen und Klang vor und betrachtet kulturelle Aspekte des Läutens. In einem neuen Format sind alle Teilnehmenden dann eingeladen, ihre eigenen Fragestellungen zu Glocken einzubringen und das Symposium zu nutzen, in großer Runde diese Themen zu diskutieren.
According to guidelines for the evaluation of church bell chimes, the timbre of a well-sounding bell is characterized by a soft, full, bright and clear tone with a salient fundamental pitch in a low-noise sound spectrum, where a luminous, clearly pronounced striking tone can be heard with as little percussive noise as possible. In a pilot experiment, 11 bell experts and 26 laypersons evaluated the ringing of 40 loudness-matched bell recordings in terms of tonal quality, pleasantness, salience of the fundamental pitch, clarity and softness of the attack. Via signal analysis, the bell sounds were analyzed in regards to 127 audio features. Correlation and regression analyses were used to determine relationships between the bells' timbre ratings and the calculated audio features. Timbral changes resulting from an optimization of the bell clapper turned out to be particularly noticeable in respect to timbral brightness, the pronouncedness of the tonal components in the spectrum (harmonic energy) and the noise components (spectral flux above 1000 Hz). Specifically, by modifying the clapper, subjective pleasantness of the bells' timbre can be improved. With increasing clapper weight and decreasing ringing angle, the fundamental pitch of bells becomes morepronounced, with stronger harmonic components and less background noise.
Seit den 1950er Jahren gibt es erste moderne Regelwerke zur Bewertung von Glockenklängen [1][2][3][4], in denen besonders ‒ meist nur verbal beschreibend ‒ auf die klangliche Helligkeit, die Teiltonzusammensetzung, die Grundtönigkeit und das Ein- und Ausschwingen des Glockenklangs eingegangen wird. Nach allgemeiner Auffassung zeichnet sich der Klang einer wohlklingenden Glocke durch eine weiche, volle, helle und klare Tongebung mit einem grundtönigen, geräuscharmen Klangspektrum aus, in dem ein leuchtend-klar ausgeprägter Schlagton mit möglichst wenig Nebenschlagtongeräuschen hörbar ist [5]. Zum Wohlklang von läutenden Glocke gibt es bislang noch so gut wie keine psychoakustischen Ansätze. Lediglich zum Klang einzelner Glockenklänge unternahm Wolfgang Aures [6] einen Hörversuch mit 16 Versuchspersonen, die 17 Glockenklänge auf ihren Wohlklang hin bewerten sollten und konnte zeigen, dass der empfundene Wohlklang mit abnehmender klanglicher Schärfe zunahm, während die ebenfalls erhobene klangliche Rauhigkeit so gut wie nichts zur Wohlklangsempfindung beitrug. 2021 stellten Andreas Rupp, Denis Spieß und Michael Plitzner ein Verfahren vor, mit dem der Einfluss der Klöppelgestaltung auf den Klang von läutenden Glocken simuliert werden konnte [7], wodurch gezielt ein ausgewogener Glockenklang erreicht werden kann: Während die Anteile der hohen Frequenzen im Spektrum durch die Klöppelmasse bestimmt werden (je geringer die Klöppelmasse desto heller der Klang), werden die tiefen Anteile im Spektrum vor allem durch das Gewicht und die Anfluggeschwindigkeit des Klöppels bestimmt (je schwerer der Klöppel und je langsamer der Anflug = je länger die Verweildauer des Klöppels an der Glocke, desto grundtöniger der Klang).
Der Klang einer läutenden Glocke wird neben ihrem eigenen Klangvermögen durch eine Vielzahl von Parametern beeinflusst. Äußere Parameter wie Läutewinkel, Glockenstube und Schalläden beeinflussen den Glockenklang außerhalb des Turmes. Die Abstimmung des dynamischen Systems Glocke mit Joch und Klöppelmaterial, -gewicht und -form sind bestimmend für die Lautstärke und die Anregung zum Schwingen. Die klangliche Wirkung des Klöppelanschlags selbst wird deutlich durch die Dauer des Anschlags beeinflusst, da sie einen direkten Einfluss auf die in der Glockenstruktur angeregten Schwingungsfrequenzen hat. Geräusche und Klänge, deren spektrale Anteile sich überwiegend im Bereich hoher Frequenzen befinden, werden vom menschlichen Gehör als „scharf“ oder „schrill“ empfunden. Schärfe korreliert häufig mit der Lästigkeit, d.h. je größer die Schärfe, desto schlechter die Geräusch- bzw. Klangqualität. Für eine ausgewogene Anregung der hohen und tiefen Teiltöne einer Glocke ist im Allgemeinen eine gute Anregung der niederfrequenten Töne und eine in Maßen abgeschwächte Anregung der höheren Töne vorteilhaft. Für eine Optimierung des Klangverhaltens einer läutenden Glocke rückt damit die Betrachtung die Anschlagdauer immer mehr in den Fokus. Der Zusammenhang zwischen Kontaktdauer von Klöppel und Glocke und der Klanganregung wurde mittels Computersimulationen untersucht. Da Glockenklang ein zutiefst subjektives Klangempfinden auslöst, wurden erste Beschreibungsversuche mit Psychoakustischen Methoden vorgenommen. Es konnte nachgewiesen werden, dass ein Klöppel durch einen gedrungenen Vorschwung als vorteilhaft hinsichtlich hoher Lebensdauer und grundtönigem Klang ausgelegt werden kann.
Das Europäische Kompetenzzentrum für Glocken ECC-ProBell der Hochschule Kempten zeigt anhand einer grundlegenden Machbarkeitsstudie die Möglichkeit zur Umsetzung von fliegenden Schallpegelmessungen. Als konkreter Anwendungsfall diente die Charakterisierung der Glockenschall-Ausbreitung um Kirchtürmen. Hierbei wurde das Messsystem mit einer Drohne zum Fliegen gebracht. Bei der Auswahl der Komponenten musste nach der funktionalen Eignung verstärkt auf das Gewicht geachtet werden, da das maximale Zusatzgewicht der Drohne limitiert ist. Die Drohne selbst sorgt für einen kontinuierlichen Störgeräuschpegel, welche genauer untersucht wurden. Um diese Störgeräusche vermindert aufzuzeichnen fand ein Mikrofon mit vorgeschaltetem Interferenzrohr und einer starken Richtwirkung Verwendung. Aus Kosten-, Gewichts, und funktionalen Gründen wurden die Ergebnisse mit einem mobilen Audio Recorder aufgezeichnet. Der gesamte Messaufbau wurde so konzipiert, dass hochgenaue Kalibriervorgänge, komplexe Analysen sowie verschiedene Filtermechanismen Verwendung finden können. Das gesamte Aufnahmesystem wurde tiefgehend analysiert und gegenüber hochwertiger Messtechnik kalibriert. Erste Ergebnisse wurden unter realen Bedingungen am Kirchturm und mit gängigen Methoden der Schallpegelberechnung erbracht. Mit dem erarbeiteten Vorgehen können Messfehler über die Berechnung der Fehlerfortpflanzung auf die Unsicherheit der Endergebnisse übertragen werden. Die Ergebnisse der Arbeit zeigen, dass mit dieser Art von Vorgehen Schallpegel externer Quellen ab etwa 80 dB gemessen werden können. Dieser Grenzwert kann durch variable Umgebungsbedingungen um wenige Dezibel abweichen.
Aufgrund der hohen Beanspruchungen, denen Kirchenglocken beim Läuten ausgesetzt sind, entstehen nicht selten Ermüdungsrisse im Klangkörper von Glocken, die durch Schweißen repariert werden müssen. Das Schweißen von Glocken ist jedoch mit zahlreichen Unsicherheiten behaftet, so dass sich bereits nach wenigen Jahren erneut Risse ausbilden können. Die bisher eingesetzten Methoden zur Qualitätssicherung reparierter Glocken beschränken sich im Wesentlichen auf eine Sichtprüfung bezüglich der Oberfläche und einer Klangprüfung mit besonderem Fokus auf das Abklingverhalten. Eine deutlich weitreichendere Analyse bietet die akustische Bewertung des Schwingverhaltens von Glocken mit der Methode des musikalischen Fingerabdrucks. Diese nutzt die Zwillingstonbildung rotationssymmetrischer Körper aus, um Fehlstellen im Klangkörper von Glocken anhand von Klangaufnahmen zu ermitteln. Abhängig von der Lage, Ausprägung und Art vorhandener Fehlstellen können so charakteristische Abweichungen vom typischen Schwingverhalten von Glocken festgestellt werden, die eine Identifikation der verursachenden Fehlstelle ermöglicht. Auf diese Weise können die für Schweißreparaturen üblichen Klangveränderungen aufgrund abweichender Materialeigenschaften oder fehlerhafter Materialbindungen an Modellen simuliert und mit dem Schwingverhalten reparierter Glocken verglichen werden. Die für die Bewertung erforderlichen Klangaufnahmen können mit geringem Aufwand erfasst werden, so dass dies Verfahren ein kostengünstiges Monitoring von Glocken ermöglicht. Nach Reparaturen kann so der Zustand der Glocken zuverlässig überwacht werden.
Possible interference sources for our aerosol lidar setup with transient recorders have been assessed. This was done by two methods: a spectrum analysis of the lidar signals in order to detect radio-frequency interference and measurements of the electromagnetic interference caused by the laser power supply. We found disturbances in the analog channels of the transient recorders, presumably caused by ageing effects of our older recorders. An easy method on how the signal-to-noise-ratio can be improved retrospectively is presented. We also show that the usage of two-way radio at our location leads to a visible radio-frequency interference in the lidar profiles. Further, we present measurements of the electromagnetic interference caused by the laser power sup- ply, which may lead to disturbances in the lidar profiles if the transient recorders are placed next to it.
Die Entwicklung, Erprobung und Validierung von Fahrerassistenzsystemen und automatisierten Fahrfunktionen ist im realen Fahrversuch aufgrund mangelnder Skalierbarkeit nur eingeschränkt möglich. IPG Automotive und die Hochschule Kempten beschreiben eine effziente Simulations-Toolchain, die eine nahtlose Integration und Austauschbarkeit verschiedener Sensormodelle und Systemkomponenten ermöglicht.
Developing, testing and validating advanced driver assistance systems and automated driving functions can only be realized to a limited extent in real-world test drives due to a lack of scalability. IPG Automotive and the Kempten University describe an efficient simulation toolchain that enables the seamless integration and exchange of different sensor models and system components.
During the development of state-of-the-art driver assistance systems and highly autonomous driving functions, there is a demand for reliable research vehicle platforms that can be used in a variety of applications. Especially for data-driven machine learning approaches, a large amount of measurement data obtained from multimodal sensors is needed. This paper presents a Robot Operating System (ROS) based prototype vehicle that is built on a Porsche Cayenne, which provides a dedicated test environment for autonomous research. To bridge the gap between pure research and actual production vehicles, the platform features near-series placement of sensors and the use of the built-in camera and actuators. Open-source packages and a containerized software architecture make the system reusable and easy to extend in terms of hardware and algorithms. Furthermore, we describe our approach for data recording and long-term persistence.
Automated and autonomous vehicles will fundamentally change the way we humans experience individual mobility. Customers would like to use the time gained for secondary tasks such as private or professional work, for consuming entertainment media or simply for relaxing. Vehicle motion and vibration imposed on the human body in this environment also lead to fatigue and discomfort. A threshold for comfortable task performance is currently not known, however this could be important knowledge for designing future active chassis systems. This exploratory study with 12 subjects examined motion comfort during two secondary tasks on a digital road with the help of a dynamic driving simulator. Subjective data from questionnaires as well as objective data from inertial sensors were analyzed. Evaluations indicate a threshold for comfortable environments that should be further examined in future studies.
The disruptive change of the future vehicle fleet, new technologies and short development times challenge the entire automotive industry. Every test drive and every test kilometer is cost-intensive and, on top of that, in some cases hardly feasible for safety reasons. The goal is crystal clear: to shift development more and more to simulation and reduce the number of prototypes. However, the real driving experience in a road test still offers essential insights for engineers and management. Driving simulators have the potential to bridge these gaps. They can create the possibility for engineers and test subjects to experience the subjective driving impressions of new functions, systems and driving attributes already in the virtual phase. In order to achieve a comparable driving experience on a driving simulator and thus a comparable evaluation result with the driving test, the methods as well the driving simulator environment must be aligned with the targeted applications and use cases. Kempten University of Applied Sciences has set up a novel dynamic driving simulator from AB Dynamics (ABD). Together with research and technology partners, technologies and methods are being further developed on this basis in order to achieve the above-mentioned goal. The paper presents potential, methods as well as use cases relevant for chassis development. The paper also gives a first-hand account of the experience.
Reducing time, costs and prototypes in vehicle development is a central objective. Previously, simulation and experimental testing are separate blocks in the development process chain that exchange information with each other. Theoretical preliminary considerations are realised by simulations, which are then tested by prototypes in real road trials. The use of driving simulators enables a synergetic solution to combine simulation and experimental testing. A process chain is presented in order to make it possible to experience the MBS model with damaged axle components on the driving simulator and thus also to evaluate them subjectively. Various deformation states of the components of the four-link rear axle are subjectively evaluated by several test drivers on the driving simulator in various driving manoeuvres. In the process, the components are ranked in terms of their damage criticality and the customer acceptance threshold is determined based on the component deformation. Furthermore, correlations between objective overall vehicle quantities and subjective driver evaluations are identified via linear regression models and artificial neural networks.
Unsupervised Domain Adaptation demonstrates great potential to mitigate domain shifts by transferring models from labeled source domains to unlabeled target domains. While Unsupervised Domain Adaptation has been applied to a wide variety of complex vision tasks, only few works focus on lane detection for autonomous driving. This can be attributed to the lack of publicly available datasets. To facilitate research in these directions, we propose CARLANE, a 3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE encompasses the single-target datasets MoLane and TuLane and the multi-target dataset MuLane. These datasets are built from three different domains, which cover diverse scenes and contain a total of 163K unique images, 118K of which are annotated. In addition we evaluate and report systematic baselines, including our own method, which builds upon Prototypical Cross-domain Self-supervised Learning. We find that false positive and false negative rates of the evaluated domain adaptation methods are high compared to those of fully supervised baselines. This affirms the need for benchmarks such as CARLANE to further strengthen research in Unsupervised Domain Adaptation for lane detection. CARLANE, all evaluated models and the corresponding implementations are publicly available at https://carlanebenchmark.github.io.
This dissertation presents a methodology for validation of simulation models to ensure virtual safeguarding of automated vehicles. The methodology quantifies model uncertainties compared to real experiments and predicts them by a data-driven approach to the virtual safeguarding scenarios. This provides statistical guarantees for reliable decision making. The methodology is first introduced generically and then configured, implemented, and evaluated specifically for safeguarding.
Measurement performance evaluation of real and virtual automotive light detection and ranging (LiDAR) sensors is an active area of research. However, no commonly accepted automotive standards, metrics, or criteria exist to evaluate their measurement performance. ASTM International released the ASTM E3125-17 standard for the operational performance evaluation of 3D imaging systems commonly referred to as terrestrial laser scanners (TLS). This standard defines the specifications and static test procedures to evaluate the 3D imaging and point-to-point distance measurement performance of TLS. In this work, we have assessed the 3D imaging and point-to-point distance estimation performance of a commercial micro-electro-mechanical system (MEMS)-based automotive LiDAR sensor and its simulation model according to the test procedures defined in this standard. The static tests were performed in a laboratory environment. In addition, a subset of static tests was also performed at the proving ground in natural environmental conditions to determine the 3D imaging and point-to-point distance measurement performance of the real LiDAR sensor. In addition, real scenarios and environmental conditions were replicated in the virtual environment of a commercial software to verify the LiDAR model’s working performance. The evaluation results show that the LiDAR sensor and its simulation model under analysis pass all the tests specified in the ASTM E3125-17 standard. This standard helps to understand whether sensor measurement errors are due to internal or external influences. We have also shown that the 3D imaging and point-to-point distance estimation performance of LiDAR sensors significantly impacts the working performance of the object recognition algorithm. That is why this standard can be beneficial in validating automotive real and virtual LiDAR sensors, at least in the early stage of development. Furthermore, the simulation and real measurements show good agreement on the point cloud and object recognition levels.
Functional safety and cybersecurity are essential parts of the development of automated vehicles to ensure vehicle safety. Highly automated driving (HAD) vehicles require safe and secure development and communication processes that have to be monitored, maintained and improved through management processes. Hence, interface management systems are required to confirm HAD vehicle safety. The acceptance level of the interface between functional safety and cybersecurity in management systems is crucial for the development of Highly Automated Driving (HAD) vehicles. The Safety Management System (SMS) needs to consider the aspect of cybersecurity to ensure the overall safety of the vehicles or vice-versa. However, the interface methods of SMS and Cybersecurity Management System (CSMS) is challenging given the complexity of the system development and constraints from the company culture. The objective of this study is to present an interface approach in between management systems with a set of interface specifications including communication adaption processes. The main contributions of the paper are, (i) Illustrating the interface areas of the SMS and CSMS by identifying the management factors, (ii) Presenting the degree of influence of the management factors based on the survey results, and (iii) Providing a support to deal with SMS and CSMS interface for HAD vehicle development. A list of interface-related management factors is presented in this paper based on the literature study and findings from other disciplines. Additionally, the degree of influence of the management factors is presented as a result of this research based on the survey results from functional safety and cybersecurity experts.
Modern electric power steering (EPS) systems allow a flexible adaptation of the same steering hardware to different vehicle types by calibration of the corresponding control unit (ECU). As the steering behavior is one of the major factors influencing the end customers` buying decision and driving experience, the steering ECU calibration has to fit very well to the customer requirements. A growing number of functions in the steering ECU offer increasing possibilities to influence comfort, safety and steering feel and must be validated in different driving maneuvers (figure 1). However, several trade-offs of these characteristics have to be solved such as the driver’s steering effort vs. the steering feedback while cornering.
Model-based Development Methods – What can Chassis and Powertrain Development Learn from Each Other?
(2015)
The biggest challenge for today’s vehicle development is the increasing number of vehicle variants for different markets, which have to be developed in ever shorter cycles. In addition, customers and governments have high demands when it comes to comfort, driving pleasure, consumption, security and CO2 emissions. The powertrain and the chassis domain have developed their own methods to solve these difficulties. Although there are some important differences, the question arises how one domain can profit from the methods of the other domain. For example, the strict emission legislation in the powertrain sector lead to advanced model-based calibration and testing methods, which could be useful also for other domains.
Executive Summary
Um die Ziele des Pariser Klimaabkommens zu erreichen, besteht rascher Handlungsbedarf in allen Sektoren. Dies gilt insbesondere für den Verkehrssektor, für den eine umfassende Transformation unumgänglich ist.
Aktuelle Forschungsarbeiten zeigen, dass neben der Elektrifizierung des motorisierten Individualverkehrs insbesondere der Ausbau und die Umsetzung alternativer Mobilitätsformen mit geringeren Treibhaus-gasemissionen (z.B. ÖPNV, Radfahren und Carsharing) aus Klimasicht zu bevorzugen und weiter voranzutreiben msind. Daher reicht es nicht aus, den derzeitigen Fahrzeugbestand mit Verbrennungsmotoren „einfach“ durch Fahrzeuge mit batterieelektrischem Antrieb zu ersetzen. Vielmehr müssen Wege gefunden werden, das Verhältnis von hergestellten Fahrzeugen und ihrer tatsächlichen Nutzung zu verbessern, um eine umfassende Reduktion der Treibhausgase im Bereich der individuellen Mobilität zu erreichen. Es ist daher notwendig, ein Bewusstsein für die Relevanz alternativer Mobilitätsformen (z.B. Mobilität-on-De-mand) zu schaffen und entsprechende Maßnahmen zum Ausbau dieser Mobilitätsformen zu ergreifen.
Due to the rapid growth of the tourism industry, associated effects like overcrowding, overtourism, and increasing greenhouse gas emissions lead to unsustainable development. A prerequisite for avoiding those adverse effects is the prediction of occupancy. The present study elaborates on the applicability and performance of various prediction models by taking a case study of beach occupancy data in Scharbeutz, Germany. The case study compares different machine learning models once as supervised machine learning models and once as time series models with a persistence model. XGBoost and Random Forest as time series demonstrate the most accurate prediction, followed by the supervised XGBoost model. However, the short prediction span of time series models is a disadvantage for longer-term visitor management to avoid the explained unsustainable effects through steering measures, so depending on the use case, the XGBoost model is to be favoured.
Durch die direkte Verbindung von Mobilität und Tourismus beeinflusst die fortschreitende Elektrifizierung des Verkehrs auch unmittelbar die Tourismusbranche. Öffentlich zugängliche Ladeinfrastruktur gewinnt immer mehr an Bedeutung und so werden auch an touristischen Zielorten Lademöglichkeiten nachgefragt. Doch gerade dort stellt der wirtschaftliche Betrieb eine große Herausforderung dar. Grund hierfür sind vor allem volatile Ladebedarfe, die hohe Lastspitzen und aufgrund der Bepreisung im deutschen Stromsystem hohe Kosten nach sich ziehen können. Nach einer kurzen Darstellung der aktuellen Kostenstruktur von Ladeinfrastruktur werden daher touristische Zielorte anhand verschiedener Ausprägungen, die das Lastprofil und damit die Kosten beeinflussen können, charakterisiert. Anschließend werden vielfältige, insbesondere durch die Digitalisierung realisierbare, Lösungsansätze aufgezeigt und mögliche zukünftige Entwicklungen diskutiert.
Digitales Besuchermanagement im Tourismus – Konzeptioneller Rahmen und Gestaltungsmöglichkeiten
(2023)
Digitales Besuchermanagement beschreibt die Beeinflussung von Reise- und Ausflugsentscheidungen durch digitale Verfahren zur Vermeidung von Überlastungen in Destinationen. Es umfasst die Bereitstellung von Daten, die Datenhaltung, das Erarbeiten von Empfehlungen und das Ausspielen der Ergebnisse über digitale Touchpoints. Dieser Beitrag beschreibt die Ziele und Bestandteile eines digitalen Besuchermanagementsystems im Tourismus und bewertet Möglichkeiten und Grenzen. Abschließend werden offene Forschungsfelder definiert.
Optimal Wiener filtering is a popular method for the estimation of stationary processes which can be completely derived system-theoretically. Although there exist several optimal filtering concepts for non-stationary processes, there is still a lack of fundamental time-variant system theory that describes the problem statement for non-stationary process estimation. This paper provides an intuitively understandable theory for the fundamental optimal filtering concept. By interpreting cross- and autocorrelations as a time-variant impulse response of a linear system, the problem statement can be illustrated with a network of linear systems. This paper introduces a double periodic system model that approximates a time-variant transfer function in both time and frequency domain which leads to an analytic solution for the optimal time-variant filter. The presented results are generally applicable and degenerate for simplified process properties (e.g. stationarity) to the well known results. We also present how the problem statement can easily be extended due to the fundamental and uniform theoretical approach.
When charging electric vehicles inductively, living objects must be prevented from being exposed to the magnetic field. Therefore, additional sensors are used to detect endangered objects under the vehicle. This ensures that the charging process can be stopped immediately if endangered objects stay inside the hazardous zone. To prevent the system from unintended charging switch-offs, it is preferable to detect also life-signs for a reliable differentiation between living and non-living objects. In this paper, we propose a method for Doppler-based detection of respiration movements using a chirp sequence modulated radar sensor. We also provide system-theoretical background concerning the identification of linear time-variant systems. This delivers a clear problem statement and facilitates the understanding of the proposed method. Consequently, the theoretical results are applied to measurements for the detection of respiration movements. The results enhance an existing approach for living object protection using a radar sensor on the vehicle side.
As battery capacities become suitable for the mass market, there is an increasing demand on technologies to charge electric vehicles. Wireless charging is regarded as the most promising technique for automatic and convenient charging. Especially in publicly accessible parking spaces, foreign objects are able to enter the large air gap between the charging coils easily. Since the evoked magnetic field does not meet regulations, wireless charging systems are demanded to take further precautions related to the protection of endangered objects. Thus, additional sensors are required to protect primarily living objects by preventing them from being exposed to the magnetic field. In this paper, we propose a new approach for monitoring the air gap under the vehicle underbody using an automotive radar sensor on the vehicle side. The concept feasibility is evaluated with the help of a prototypical implementation. Further, two-dimensional signal processing techniques are applied to meet the requirements of inductive charging systems. Consequently, this paper provides measurement data for relevant use cases frequently discussed in the community of inductive charging.
Identifying linear time-variant (LTV) systems has been of great interest for many decades already and an important problem in many engineering applications. This paper proposes to use a double periodic system approximation for the characterization of LTV radar channels. Hereby, a two-dimensional Fourier series is used to approximate the time-variant transfer function in both time and frequency domain. By observing the system's response corresponding to an appropriate input signal, this paper presents a mathematical derivation of how to determine the Fourier coefficients. In practice, these Fourier coefficients are often approximated by assuming the LTV system to be piecewise constant in time domain. This paper establishes an analytical relationship between the original Fourier coefficients and the approximated ones and it investigates how the Fourier coefficients get distorted due to this assumption. Further, this paper provides practical measures to evaluate and control the degree of distortion in advance by adjusting the input signal modulation. This is illustrated by simulation results that are carried out for a double periodic system model with fictitious Fourier coefficients. Consequently, it is shown how the provided results are applied to chirp sequence modulated radar systems and how the radar signals can be interpreted from a system-theoretical perspective.
Automotive radar sensors have been used for measurements of time-varying scenarios for several years and the demands on their performance are increasing continuously. For the development of future signal processing algorithms, it is useful to investigate the problem of linear system identification as a generalized radar scenario. This paper provides a method for the approximation of a wireless transmission channel and derives how such a linear time-variant system can be identified using an FMCW radar. This universal approach enables the introduction of system functions, each characterizing the system's behavior in different time and frequency representation. These system functions of a wireless transmission channel are illustrated by radar measurements of a radially approaching cyclist, which deliver a profound interpretation of the wireless channel.
Was für die sogenannte weiße Ware passt, muss sich auch für andere
Anwendungen in der Industrie eignen. Überzeugt von diesem Ansatz, kooperieren die Hochschule Kempten und ein Unternehmen für drahtlose Sensorsysteme eng miteinander, um ein kabelloses Sensorsystem weiterzuentwickeln.
Ziel des gemeinsam von Pro-micron und der Hochschule Kempten durchgeführten Projekts ist die Weiterentwicklung eines kabellosen Sensorsystems, das seit Jahren im automatisierten Garprozess in der weissen Ware zum Einsatz kommt. Das System dient zur Messung der Rotortemperatur von Elektromotoren in Großserienanwendungen. Denkbar sind somit nicht nur Anwendungen in Konvektomaten, sondern in sämtlichen Bereichen, in denen eine ausgeklügelte Temperaturmessung zu besseren Antriebseigenschaften führt. Somit liegt der Mehrwert entsprechender Temperatursignale zur Steuerung elektrischer Maschinen auf der Hand und kann erhebliche Vorteile aufweisen. Auslastung, Effizienz und Drehmomentengenauigkeit sowie ein zuverlässiger Übertemperaturschutz sind nur einige Beispiele für die Vielzahl von Vorzügen, welche sich aus der Rotortemperaturmessung ergeben.
Chromatic confocal metrology suffers from a limitation in the number of measurement points that can be measured simultaneously in a single frame acquisition. We propose chromatic confocal areal metrology (ChromaCAM), in which the surface height for each point in a 2D grid of measurement spots, generated by a rectangular micro-lens array, is parallely analyzed through the utilization of a pinhole multiplexer unit, an analog optical analysis unit, and postprocessing algorithms. An experiment shows the viability of the simultaneous acquisition of multiple measurement points and the advantages over exisiting areal chromatic confocal approaches. Compared with conventional chromatic confocal metrology, the increase in the acquisition rate is significant and enables one-shot measurements.
This paper proposes a framework for optimizing an electric urban bus network, based on data of an existing diesel bus fleet. Therefore, the required input data is described as well as the required steps to analyze and preprocess the data from the busses. With this aggregated data a mathematical model was formulated to optimize the cost of a future electric bus network. This is accomplished by choosing optimal batterie sizes and charging station locations as well as charging powers. • With extensive real-world data logging, we could validate assumptions about energy demand, waiting times and different traffic situations during the day • To use the data for the model implementation of a robust data pipeline including Interpreting, preprocessing and aggregating the data, was necessary • Based on the data of the diesel busses we simulated the electric buses energy demand • The Integer Linear Programming model finds cost optimal solutions for charging infrastructure and batterie sizes • The algorithm is very adjustable to specific needs of public transport operators and can generate optimal solutions in a short amount of time
The development of Highly Automated Driving (HAD) systems is necessary for automated vehicles in termsof various complex functionalities. HAD systems consist of complex structures containing different types ofsensors. The functionality of HAD systems needs be tested to ensure the overall safety of automated vehicles.Methods such as real-world testing require a large number of driving miles and are enormously expensive andtime-consuming. Therefore, simulation-based testing is widely accepted and applicable in the development ofHAD systems, including sensor performance improvement. In order to identify the functional insufficiencyof such sensors that affect the safety of HAD systems, it is critical to test these sensors extensively under avariety of conditions such as, road types, environment and traffic situations. Based on this motivation, the maincontributions of this paper are as follows: First, a simulation-based test concept of radar sensors with methodsfor the Safety Of the Intended Functionality (SOTIF) use case is presented. Second, a specific radar effect isevaluated through simulation-based testing of two different radar models to support and realize the sensor’sfunctional insufficiency. Finally, the development of a filter is proposed to improve the sensor performanceconsidering the radar specific multipath propagation effects.
This paper is a summary oft he traffic light detection for an autonomous model vehicle which was created for a student semester project. The traffic light detection comes to use when the model vehicle takes part in the VDI-Cup or other future Cups. The VDI-Cup will be discribed later on. The structure of this paper start with a summary, followed by the discribtion oft the VDI-Cup. After that we will explain how we implemented the traffic light detection and finishes with the conclusion of our implementation.
Collecting the training set required for building a robust neural network for autonomous driving requires large amount of data. It is nearly impossible to collect this data only by recording the driving of real world vehicles. There is no organization or company that is able to provide the resources needed to tackle this task.[1] Therefore the approach currently used is to generate the large amount of training data by simulating virtual cars in computer simulations that try to mirror real world road traffic as close as possible. Besides the huge amount it is also important that the data generated varies quiet a lot, otherwise the neural network can not learn to adapt to the many different situations that occur in real world road traffic every day.[2] Therefore it would be great to record the virtual car driving on as many different tracks as possible. To solve this issue this paper proposes a fast and simple iterative algorithm that can be used to procedural generate tracks that can be used for the recording and training of autonomous driving cars.
Lane detection is an essential part for an autonomous car to function. With a lane departure warning system many modern cars are already equipped with a lane detection system. But models and methods to predict lanes are numerous and often follow different approaches to solve the lane detection problem. This paper wants to give an overview on some of the state of the art models and methods for lane detection that lately achieved good results on public datasets. The paper will also look at training and testing a SCNN [10] using the free Colaboratory service from Google [29].
In the last decade autonomous driving has evolved from a science fictional dream to an everyday reality. With the advance of more and more companies bringing their versions of self-driving cars on the street it is just a matter of time before the majority of transportation will be in the hand of computers. But with the deadly car accident involving a self-driving Uber car back in 2018 there is also the question about how reliable autonomous driving really is and how we can validate and test the safety of this new road user 1. An uprising approach towards creating robust and adaptable neural networks is called domain randomization. This paper explores the possibility of using this method to create training data with driving simulations. It will propose a list of important criteria and factors affecting the selection of a fitting simulation. Furthermore it will present a track generator which is able to create useful tracks and export them to a format which can be used by several common simulations used in the field of autonomous driving research.
This paper is a summary of the time to collision (TTC) calculation for an autonomous model vehicle which was created for a student semester project. The TTC comes to use when the model vehicle takes part in the Carolo-and VDI-Cup which will be described later on. The structure of this paper starts with a summary of the background information, then digs deeper into what algorithms are discovered already and how they work and finishes with the explanation and conclusion of our implementation.
A real time object detection system is an essential core feature of any autonomous driving car. With the variety of models and data sets available, there is great opportunity to train a model that meets the high requirements of autonomous vehicle. In this paper we want to present an object detection model for traffic signs and lights implemented on a NVIDIA Xavier board [7] using the robot operating system (ROS) [9]. Furthermore we explain how to create and evaluate a data set as well as to test the accuracy and performance of the implemented detector. In regards to pattern recognition with neural networks, the performance and accuracy must also be weighed up. The goal is to achieve one without sacrificing the other. Considering all these aspects, we decided to train a YOLOv4 Standard and a YOLOv4 Tiny configuration [2]. While the accuracy of the YOLOv4 network exhibited a very high accuracy rate on high and mediocre resolutions but the performance is too poor to be used in a real time object detection system. On the other hand, the YOLOv4 tiny network reached the strived for performance even on the highest resolution scales tested, but at the cost of accuracy.
In manufacturing industry, product failure is costly, as it results in financial and time losses. Understanding the causes of product failure is critical for reducing the occurrence of failure and optimising the manufacturing process. As a result, a number of studies utilising data-driven approaches such as machine learning have been conducted to reduce the occurrence of this failure and to improve the manufacturing process. While these data-driven approaches enable pattern recognition, they lack the advantages associated with knowledge-driven approaches, such as knowledge representation and deductive reasoning. Similarly, knowledge-driven approaches lack the pattern-learning capabilities inherent in data-driven approaches such as machine learning. Therefore, in this paper, leveraging the advantages of both data-driven and knowledge-driven approaches, we present a strategy with a prototype implementation to reduce manufacturing product failure. The proposed strategy combines a data-driven technique, Bayesian structural learning, with a knowledge-based technique, knowledge graphs.