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Die Welt der Automobilindustrie befindet sich in einem kontinuierlichen Wandel. Aktuell findet eine große Entwicklung in Richtung Connected, Autonomous, Shared, Electric (CASE) statt, welche völlig neue Auswirkungen auf den kompletten Lebenszyklus des Automobils hat. Das Fahrzeug entwickelt sich weiter in Richtung eines fahrenden Computers und die Anzahl der Programmzeilen von Fahrzeugfunktionen steigt. Dadurch und durch die Vernetzung von Fahrzeugen ergeben sich neue Gefahren durch Angriffe auf Fahrzeugsysteme, welchen durch Einsatz passender Cybersecurity-Maßnahmen entgegengewirkt werden muss. Diese Abschlussarbeit soll sich mit den aktuell geltenden und kommenden Normen und Verordnungen hinsichtlich Cybersecurity beschäftigen, besonders im Hinblick auf das autonome Fahren, und aufzeigen, wie sich Cybersecurity in der Automobilbranche auf Ebene der Sensorik umsetzen lässt. Die aufgezeigten Maßnahmen sollen am Beispiel eines Lidar-Systems verdeutlicht werden.
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.
Für viele Autofahrer ist das Einparken eine tägliche Herausforderung, für die einen mehr, für die anderen weniger. Ebenso gibt es in modernen Fahrzeugen Einparkassistenzsysteme, die diese Aufgabe sehr gut erledigen, und solche, die sie viel schlechter erledigen. Um sowohl manuelle Einparkmanöver als auch Parkmanöverassistenten objektiv bewerten zu können, hat die Hochschule Kempten in Zusammenarbeit mit MdynamiX eine Kundenstudie mit 21 Teilnehmern und verschiedenen Einparkassistenten durchgeführt.
For many drivers, parking is a daily challenge, for some more for others less. Likewise, there are parking assistance systems in modern vehicles that do this task very well and those that do it much worse. In order to be able to objectively evaluate both manual parking maneuvers and parking assistants, Kempten University of Applied Sciences, in cooperation with MdynamiX, conducted a customer study with 21 participants and various parking assistants.
Fahrerassistenzsystem (FAS) sind in PKWs seit zwei Jahrzehnten auf dem Vormarsch. Ihre Funktionen sind aber bisher weit davon entfernt, perfekt zu sein. Das Vertrauen und die Akzeptanz der Kunden in die Systeme sind daher immer noch niedrig. Automobilhersteller versuchen deshalb, Informationen zur Verbesserung der Systeme durch Befragung der Kund:innen oder Durchführung von Probandenstudien im Straßenverkehr zu gewinnen. Auch wenn auf diese Art bereits manche Erkenntnisse gewonnen werden können, ist der Straßenverkehr eine Versuchsumgebung, die schwer kontrollierbar ist, weshalb nur schwierig konsistentes Verhalten bei Proband:innen und Systemen hervorgerufen werden kann. Fahrsimulatoren bieten in der Theorie eine zuverlässige, sichere und kontrollierbare Umgebung zur Befragung der Proband:innen. Aber auch Simulatoren haben Beschränkungen und Eigenheiten, die berücksichtigt werden müssen. Aus diesem Grund können Fahrsimulatoren nur als komplementäres Werkzeug zu Studien im Straßenverkehr betrachtet werden. Das Ziel der Arbeit ist daher, Methoden zu finden, welche die Stärken bei der Versuchsumgebungen verbinden und ihre Schwächen eliminieren.
Um dieses Ziel zu erreichen, wurden fünf Methoden ausgewählt: die Bewertung der Realitätstreue, ein Konzept zur Validierung des Simulators, statistische Analyse der Daten aus Studien im Straßenverkehr, personabasiertes Design und das Engineering Judgement.
Die Methoden wurden nach Möglichkeit auf die Spurhalteassistenz-Studie angewandt, die im vergangenen Jahr an der Hochschule für angewandte Wissenschaften Kempten (HSK) durchgeführt wurde. Im Anschluss wurden sie bezüglich ihrer Effektivität bewertet. Zuletzt wurde ein Vorgehensmodell vorgestellt, das die oben genannten Methoden beinhaltet.
Es soll die Effizienz und Effektivität beim Prozess der Verbesserung von FAS mit Hilfe von Probandenstudien auf einem Simulator steigern.
Recently the trend of driving simulators with driver in the loop (DiL) integration in the development process has become more and more visible. BMW opened a completely new simulation center, Daimler and Toyota have already had theirs in operation for some years. The reasons are well known and are presented in conferences and written down in papers: Reducing development time, reducing prototypes, reducing costs and increasing overall performance and efficiency. The same benefits are promised in papers about pure simulation, but what is the actual benefit from a dynamic driving simulator? The investment and operating costs are very high and yet it is crucial to bring the driver in to the loop. Obviously, the maturity level or knowledge about pure simulation and understanding for human drivers are still insufficient for a major breakthrough of simulation in many fields of application. A driving simulator connects the real and virtual world, by which humans experience functions and characteristics subjectively. Crucial decisions are made on reliable subjective feedback and humans especially become part of the left arm of the V-model development process. The current approach is using the same models and simulation environments combined with a driving simulator transferring simple signals from visible into feelable. The disadvantage of closing open control loops with a driver is that the objective data and maneuver quality largely depends on the driver. Assuming the driver is well qualified, the interface must reflect the real driving experience. Otherwise, every system/component under test will be assessed with an offset, filter or error. Besides the visualization, the steering feel is the most important channel for the driver to properly control any lateral movement. The common approach is using a force feedback system. A look on a steering system illustrates the complexity to ensure good steering feel.
The proposed contribution addresses the growing need for systematic and efficient development methods for assisted and automated driving features. Active Lane Departure Warning (ALDW) systems with correcting steering interventions are the specific object of this research. Due to NCAP requirements ALDW systems are widespread in modern cars, but often lack drivers’ approval, as several related studies have shown. Causes are assumed to lie in unspecified attribute requirement metrics and missing target values. Based on typical ALDW usage scenarios on different road types, such as freeway, highway and country road, an advanced catalogue of subjective evaluation criteria was developed
and deployed in a comprehensive proband study with experts involving four distinct ALDW equipped passenger cars. Characteristic driving maneuvers with the intention to provoke controlled ALDW interventions complemented the subjective assessment with objective data. Statistically correlating derived objective key performance indicators with the respective subjective ratings established a link between drivers’ impressions and measurable system behavior. These dependencies allowed the conclusion of target values for a certain range of desired subjective ALDW characteristics fulfilling the initial requisites.
Today’s validation of driver assistance systems and automated driving functions in state-of-the-art vehicles, still takes place mostly on the proving ground or on the real road. But driving tests can only be performed with the finished vehicle and fully implemented function, which might be difficult in early stages of the development process. Those purely physical tests are also time consuming and lack reproducibility. Hence, the validation process is guided more and more by simulation tools to reduce the number of tests that have to be driven. The pure simulation offers benefits, such as the functions can be tested in early stages of the development without the need for a physical carrier vehicle. Also, the flexibility of the simulation is high, since all variations of driving scenarios, also safety critical, can be driven easily and much faster than real time. On the other side, compared to the physical test, the parameterization of supplemental models is sometimes difficult and the risk of errors due to model abstraction is high.
A major concern about advanced motion-based simulators is their level of fidelity i.e., how close the motion sensation in a simulator is to the one perceived in a real vehicle. In this study, we collect the assessment from an exceptional sample composed by n = 33 automotive industry experts who were asked to evaluate the fidelity in terms of steering, braking and speed. Given the subjective nature of our measure, we propose a censored-data Tobit regression model that accounts for this issue, thus providing more accurate estimations. Our results show that, on average, experts evaluated the steering actions close to the maximum level of fidelity. However, braking and speed were evaluated lower in realism, and in fact both diminished the overall fidelity judgement by up to 50%. Moreover, coefficients indicate that steering contributes more to the judgement of fidelity than braking and speed actions. Heterogeneity in the experts' responses and general implications are discussed.
This paper presents a detailed analysis and characterization of Subjective Assessment Indicators for evaluating manual as well as fully automatic parking maneuvers. Parking is a huge challenge for many drivers. With the introduction of autonomous driving, parking maneuver assistants are essential functional components. For the development of automatic parking assistants, a detailed characterization of a subjective evaluation is essential. The characterization analysis presented here is based on general Subjective Assessment Indicators, which cover the subjective overall performance of a parking maneuver on a customer-oriented level in as many facets as necessary. This paper shows meaningful characteristics of the individual Subjective Assessment Indicators validated in a driving study with 497 performed parking maneuvers. The study results reveal different degrees of intensity of the characterizations for the different driving maneuvers. Here, it is shown that the characterization of the Final Parking Position has different reference points for longitudinal and lateral parking maneuvers. Furthermore, it was shown that an additional characteristic ‘‘Driving-Off Behavior’’ is required for the evaluation of the Safety Feeling, but for Parking Comfort the ‘‘Lateral Acceleration’’ and for Dynamic Performance the ‘‘Distance Traveled’’ can be neglected. The characteristics described in this paper can be used for all parking maneuvers and vehicle types. It forms the basis for a complete evaluation and enables OEMs to apply their individual requirements in the development of parking assistants.