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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.
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.
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.
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.
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.