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The development of electric gerotor pumps is a complex multiphysical optimization problem. To develop optimal systems, accurate simulation models are required to increase digital reliability. An important challenge is the accurate prediction of the pump behavior for extreme temperatures in automotive applications from − 40 °C to 110 °C, where the viscosity of the fluid changes significantly. Therefore, simulation-based methods (numerical methods for calculating viscous friction) were developed and validated by measurements, including climatic chamber tests. The results show a strong correlation between simulated and measured performance characteristics, especially in terms of volumetric flow rate ( < 5 % ), pump torque and efficiency ( < 7 % ) at different temperature and viscosity conditions over a wide speed range (1000–5000 rpm) and different system pressures (0.5–5 bar). A novel method for simulating the cold start behavior of pumps (journal bearing approach for outer gear in pump housing) was introduced and validated by measurements. The methods presented significantly reduce the need for physical testing and accelerate the development process, as the pump behavior at each operating point can be accurately predicted before a hardware prototype is built. This improves the understanding of gerotor pump characteristics and provides insights to further improve the model-based development of electric oil pumps for the automotive industry. [ABSTRACT FROM AUTHOR]

In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistance and Automated Driving (ADAS/AD) systems play a pivotal role. As the complexity of these systems grows, comprehensive testing becomes imperative, with virtual test environments becoming crucial, especially for handling diverse and challenging scenarios. Radar sensors are integral to ADAS/AD units and are known for their robust performance even in adverse conditions. However, accurately modeling the radar's perception, particularly the radar cross-section (RCS), proves challenging. This paper adopts a data-driven approach, using Gaussian mixture models (GMMs) to model the radar's perception for various vehicles and aspect angles. A Bayesian variational approach automatically infers model complexity. The model is expanded into a comprehensive radar sensor model based on object lists, incorporating occlusion effects and RCS-based detectability decisions. The model's effectiveness is demonstrated through accurate reproduction of the RCS behavior and scatter point distribution. The full capabilities of the sensor model are demonstrated in different scenarios. The flexible and modular framework has proven apt for modeling specific aspects and allows for an easy model extension. Simultaneously, alongside model extension, more extensive validation is proposed to refine accuracy and broaden the model's applicability. [ABSTRACT FROM AUTHOR]

The lateral movement of vehicles within their lane under homogeneous traffic conditions is decisive for the range of vision of vehicle sensors. It significantly contributes to the maximum situational awareness an automated driving function can achieve. Given the integral role that simulations play in the validation of automated driving functions, the development of models that accurately describe the lateral movement of vehicles within their lane becomes essential. A few models have already been proposed in literature that address this task. Existing models, however, exhibit limitations when it comes to their usage for the virtual validation of automated driving functions such as the replication of general instead of driver-specific behavior and complex calibration methods. Furthermore, the metrics used for evaluation focus on measuring the accordance of the overall lateral offset and speed distribution and do not take into account the temporal course of the lateral offset profiles. Within this work, we introduce a two-level stochastic model addressing the identified limitations. We further present a strategy suitable for evaluating the low-level characteristics of the generated lateral offset profiles relevant for validating an automated driving function such as a cut-in detection function within simulations. The model’s capabilities are demonstrated based on five single driver datasets. It is shown that the model allows for efficient calibration, is able to replicate the behavior of these drivers, and is characterized by short runtimes. This makes it suitable for the virtual validation of automated driving functions.

The development of electric gerotor pumps is a complex multiphysical optimization problem. To develop optimal systems, accurate simulation models are required to increase digital reliability. An important challenge is the accurate prediction of the pump behavior for extreme temperatures in automotive applications from −40 °C to 110 °C, where the viscosity of the fluid changes significantly. Therefore, simulation-based methods (numerical methods for calculating viscous friction) were developed and validated by measurements, including climatic chamber tests. The results show a strong correlation between simulated and measured performance characteristics, especially in terms of volumetric flow rate (<5%), pump torque and efficiency (<7%) at different temperature and viscosity conditions over a wide speed range (1000–5000 rpm) and different system pressures (0.5–5 bar). A novel method for simulating the cold start behavior of pumps (journal bearing approach for outer gear in pump housing) was introduced and validated by measurements. The methods presented significantly reduce the need for physical testing and accelerate the development process, as the pump behavior at each operating point can be accurately predicted before a hardware prototype is built. This improves the understanding of gerotor pump characteristics and provides insights to further improve the model-based development of electric oil pumps for the automotive industry.

Virtual validation of radar sensor models is becoming increasingly important for the safety validation of Light Detection and Rangings (lidars). Therefore, methods for quantitative comparison of radar measurements in the context of model validation need to be developed. This paper presents a novel methodology for accessing and quantifying validation measurements of radar sensor models. This method uses Light Detection and Rangings (lidars) and the so-called Double Validation Metric (DVM) to effectively quantify deviations between distributions. By applying this metric, the study measures the reproducibility and repeatability of radar sensor measurements. Different interfaces and different levels of detail are investigated. By comparing the radar signals from real-world experiments where different objects are present, valuable insights are gained into the performance of the sensor. In particular, the research extends to assessing the impact of varying rain intensities on the measurement results, providing a comprehensive understanding of the sensor's behavior under these conditions. This holistic approach significantly advances the evaluation of radar sensor capabilities and enables the quantification of the maximum required quality of radar simulation models. [ABSTRACT FROM AUTHOR]

In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistance and Automated Driving (ADAS/AD) systems play a pivotal role. As the complexity of these systems grows, comprehensive testing becomes imperative, with virtual test environments becoming crucial, especially for handling diverse and challenging scenarios. Radar sensors are integral to ADAS/AD units and are known for their robust performance even in adverse conditions. However, accurately modeling the radar’s perception, particularly the radar cross-section (RCS), proves challenging. This paper adopts a data-driven approach, using Gaussian mixture models (GMMs) to model the radar’s perception for various vehicles and aspect angles. A Bayesian variational approach automatically infers model complexity. The model is expanded into a comprehensive radar sensor model based on object lists, incorporating occlusion effects and RCS-based detectability decisions. The model’s effectiveness is demonstrated through accurate reproduction of the RCS behavior and scatter point distribution. The full capabilities of the sensor model are demonstrated in different scenarios. The flexible and modular framework has proven apt for modeling specific aspects and allows for an easy model extension. Simultaneously, alongside model extension, more extensive validation is proposed to refine accuracy and broaden the model’s applicability.

For the last two decades, an extensive transition in automotive X-in-the-loop activities from isolated electronic control units to real-time related, geographically distributed validation tasks has occurred. Benefits are strengthening frontloading, enabling concurrent engineering and reducing prototypes and testing efforts. As a downside, comprehensive system understanding and adequate simulation models must be provided. New technological trends like software-over-the-air-updates denote a continuous validation process even after the start of production. The present review focuses on the virtual validation of vehicle longitudinal dynamics. This exemplary field of application receives more and more attention as electrification of the vehicle powertrain accelerates, and this property directly influences the vehicle DNA. A systematic review process based on the PRISMA workflow has been conducted, focusing on drivability-related powertrain applications. The investigation reveals the following trends: First, increasing complexity of virtualisation methods and models for validation activities influenced by vehicle-to-everything and geographically distributed development. Second, missing standards for virtual validation and proof of representativeness for combined real-virtual testing. In addition, many studies only contemplate the advantages of hardware-in-the-loop-driven development, disregarding crucial limitations and risks for such approaches. In conclusion, there is no longer the question of whether to validate virtually but how to comprehensible realise virtual validation.

Virtual validation of radar sensor models is becoming increasingly important for the safety validation of Light Detection and Rangings (lidars). Therefore, methods for quantitative comparison of radar measurements in the context of model validation need to be developed. This paper presents a novel methodology for accessing and quantifying validation measurements of radar sensor models. This method uses Light Detection and Rangings (lidars) and the so-called Double Validation Metric (DVM) to effectively quantify deviations between distributions. By applying this metric, the study measures the reproducibility and repeatability of radar sensor measurements. Different interfaces and different levels of detail are investigated. By comparing the radar signals from real-world experiments where different objects are present, valuable insights are gained into the performance of the sensor. In particular, the research extends to assessing the impact of varying rain intensities on the measurement results, providing a comprehensive understanding of the sensor’s behavior under these conditions. This holistic approach significantly advances the evaluation of radar sensor capabilities and enables the quantification of the maximum required quality of radar simulation models.

An immense test space is pushing the development and testing of automated driving functions from real to virtual environments. The virtual world is provided by interconnected simulation models representing sensors, vehicle dynamics, and both static and dynamic environment. For the virtual validation of automated driving, special attention must be paid to the simulation’s credibility, which can be impaired by inappropriate or inaccurate simulation models and tools. Therefore, in this work a method is proposed to assess the credibility of simulation-based testing for automated driving. The approach allows a qualitative and relatively quantitative comparisons between scenarios as well as between different simulation setups. Therefore, several uni- and multivariate metrics are applied towards a scoring of similarity of the behavior between simulation and real test drive. This is achieved by using ground truth data in form of simulation scenarios from real world measurement data. In this way, the virtual automated vehicle encounters the same conditions and surroundings than its counterpart in the real world for evaluating their similarity. The practical applicability of the proposed credibility assessment approach is demonstrated in a case study, in which the credibility of an exemplary simulation-based test bench is inferred.

Automotive radar sensors are vital in Advanced Driver Assistance Systems (ADAS). To be more precise, their ability to explicitly measure the relative velocity to its targets is essential in Adaptive Cruise Control (ACC) and Emergency Braking (EB) applications. Nevertheless, ADAS are getting more and more complex, due to constantly increasing demands regarding safety and performance. As a result, to speed up the development and validation time of ADAS, part of the testing is performed in simulations. Replacing some of the test drives by the runs in virtual environments not only reduces the cost of a product, but also helps in fully safe execution of dangerous corner cases. However, to enable reliable testing of radar-based ADAS in virtual environments, high-fidelity radar sensor models are required. In order to prove the reliability of a given model, a proper evaluation process has to be conducted. This paper presents an end-to-end, straightforward methodology for performance assessment and fine-tuning of radar sensor models. To show how the full pipeline of the framework can be executed, an exemplary radar sensor model has been incorporated. The successful fine-tuning of the model proves the usefulness of the introduced method.

This article presents the application of a generic sensor model (GSM) used in the virtual validation of an advanced driver-assistance system. The aim is to demonstrate the feasibility and usefulness of such a model in a case study. This model transforms an incoming object list from a virtual simulation to a sensor-specific object list and applies to all perception sensors acting at the object level. The procedure for parameterization of the sensor model was explained and illustrated with examples of two algorithms: vision- and lidar-based. The validity of the model was confirmed using the case of a pedestrian crossing where incidents involving pedestrians occur. Based on simulation studies and the evaluation of the actual configuration of the pedestrian crossing, conclusions were drawn which may contribute to the increase of pedestrians’ safety, both on the passive and active sides. The obtained results allow for the improvement of the operation of the transportation system in the field of advanced driver-assistance system (ADAS) development. [ABSTRACT FROM AUTHOR]

In the era of artificial intelligence perceptual algorithms used in state-of-the-art Advanced Driver Assistance Systems (ADAS), algorithm validation is not an easy task. To ensure the highest possible safety level of the solution, the performance of the algorithm must be evaluated under a variety of challenging conditions. To test the algorithms, simulators are used to emulate the virtual environment around the car taking into account road traffic, infrastructure and vehicles dynamics. Sensor models are necessary for virtual testing to provide required data to ADAS algorithms. This article introduces the issue of modeling the color filter spaces that are used in the automotive industry. The images generated by the simulator usually have RGB color. In contrast, the automotive industry uses filters such as RCCC and RYYCy. In this paper, the methods for transforming color space from RGB to RYYCy are discussed. Three novel approaches are introduced to solve this problem: analytical, polynomial, and based on a neural network. Moreover, comparative discussion of the presented solutions is shown and with the set of experiments the conversion accuracy and execution time of each algorithm are compared. In addition, introduced solution were compared with modified models that are presented in the literature. [ABSTRACT FROM AUTHOR]

Industrial vehicles have no vehicle-induced wind in operation mode, so they require more than one fan to secure enough air. Thus, a separate-type HVAC system with two fans is proposed in this study. Thanks to the two fans installed on both sides, this system can provide enough air, but it causes temperature imbalance. To deal with this issue, we performed a numerical study on the mixing performance of the several separate-type HVAC systems using CFD analysis. Experiments were also carried out to validate the numerical model. The results of the temperature and mass flow rate data matched well with the CFD results. Based on this validated model, a case study was done on three different HVAC models that have different configurations in the mixing zone. The results and discussion were done on the mixing performance and the characteristics of the velocity fields and pressure losses. [ABSTRACT FROM AUTHOR]

Shorter product life cycles require ever faster planning processes for the manufacturing of products. This also applies for measuring processes to ensure compliance with geometric workpiece specifications. In addition, these processes must be designed to be increasingly flexible since mass customization steadily increases product variety. Laser scanning systems mounted on robots offer the possibility of measuring a wide variety of geometries with low measurement uncertainty. In this paper, a method is presented with which measurement trajectories can be planned and virtually validated. We thereby combine and extend existing trajectory planning approaches and explicitly integrate robot kinematics into the planning approach to account for feasibility of the planned trajectories. These can then be directly transferred to the available measurement system. This is enabled by a real time interface directly connecting a virtual environment for measurement simulation and the real measurement system. [ABSTRACT FROM AUTHOR]

In the course of the development of automated driving, there has been increasing interest in obtaining ground truth information from sensor recordings and transferring road traffic scenarios to simulations. The quality of the “ground truth” annotation is dictated by its accuracy. This paper presents a method for calibrating the accuracy of ground truth in practical applications in the automotive context. With an exemplary measurement device, we show that the proclaimed accuracy of the device is not always reached. However, test repetitions show deviations, resulting in non-uniform reliability and limited trustworthiness of the reference measurement. A similar result can be observed when reproducing the trajectory in the simulation environment: the exact reproduction of the driven trajectory does not always succeed in the simulation environment shown as an example because deviations occur. This is particularly relevant for making sensor-specific features such as material reflectivities for lidar and radar quantifiable in dynamic cases.

Virtual Validation is one key aspect of planning production processes in the automotive industry. In practice the utilized deterministic simulation models only cover one possible process variant. This leads to a considerable deviation between simulated and real behavior. To overcome these drawbacks applying non-fixed simulation parameters is a promising approach enabling examination of process variance in early validation stages. Yet its application is hindered by lacking modularization of simulation components allowing for simulation with parameter variance. However, recent advances into this direction enable the realization of the aforementioned approach which is detailed and discussed in this paper. [ABSTRACT FROM AUTHOR]

The automotive innovations in the last decades are dominated by new software-based functions. A validation of these functions is always needed for ensuring the safety of the vehicles. XiL-methods are widely used for this purpose. Along with the development of automated driving vehicles, the demand for simulation based validation of such vehicles is rising, as shown by examples. For the simulations, the modeling of the perception sensors plays an essential role. A state-of-the-art survey clarifies, that a dilemma between simulation efficiency and fidelity of the current methods exists, especially when modeling active sensors. A novel concept of grey box modeling promises to solve the dilemma, or at least to defuse it. With a generalized sensor structure and a simplified propagation modeling, the grey box model considers the most important physically based performances of the sensors and their ranges. The basic structure of the modeling is sketched here, and an outlook for future requirements on it is presented. [ABSTRACT FROM AUTHOR]

In the course of the development of automated driving, there has been increasing interest in obtaining ground truth information from sensor recordings and transferring road traffic scenarios to simulations. The quality of the "ground truth" annotation is dictated by its accuracy. This paper presents a method for calibrating the accuracy of ground truth in practical applications in the automotive context. With an exemplary measurement device, we show that the proclaimed accuracy of the device is not always reached. However, test repetitions show deviations, resulting in non-uniform reliability and limited trustworthiness of the reference measurement. A similar result can be observed when reproducing the trajectory in the simulation environment: the exact reproduction of the driven trajectory does not always succeed in the simulation environment shown as an example because deviations occur. This is particularly relevant for making sensor-specific features such as material reflectivities for lidar and radar quantifiable in dynamic cases. [ABSTRACT FROM AUTHOR]

Virtual testing using simulation will play a significant role in future safety validation procedures for automated driving systems, as it provides the needed scalability for executing a scenario-based assessment approach. This article combines multiple essential aspects that are necessary for the virtual validation of such systems. First, a general framework that contains the vital subsystems needed for virtual validation is introduced. Secondly, the interfaces between the subsystems are explored. Additionally, the concept of model fidelities is presented and extended towards all relevant subsystems. For an automated lane-keeping system with two different definitions of an operational design domain, all relevant subsystems are defined and integrated into an overall simulation framework. The resulting difference between both operational design domains is the occurrence of lateral manoeuvres, leading to greater demands of the fidelity of the vehicle dynamics model. The simulation results support the initial assumption that by extending the operation domain, the requirements for all subsystems are subject to adaption. As an essential aspect of harmonising virtual validation frameworks, the article identifies four separate layers and their corresponding parameters. In particular, the tool-specific co-simulation capability layer is critical, as it enables model exchange through consistently defined interfaces and reduces the integration effort. The introduction of this layered architecture for virtual validation frameworks enables further cross-domain collaboration. [ABSTRACT FROM AUTHOR]