On the first glance, autonomous vehicles seem to be just a simple continuation of the development of assistance systems which help the driver keeping the lane, holding the distance to other vehicles and avoiding accidents, with the vision of avoiding 80% of all accidents, because they are mainly caused by human errors. However, there is huge challenge with respect to the requirements on system performance and reliability for this step. As Herrtwich mentioned in [1], human drivers do quite well in driving a vehicle without accident, with statistically 7.5 million km between accidents on the German Autobahn network; if an assistance system helps a driver to avoid such accidents in (just for example) 9 out of 10 times, it does a good job by reducing the number of accidents by a factor of ten. However, autonomous vehicles with SAE level 3 or higher face the challenge to avoid or control any critical situation within a statistical distance of 75 million km between accidents, in order to achieve a similar performance compared to a level 2 (driver assisted) system. That includes many situations, which have traditionally been handled by human drivers easily, but might be difficult for automation. As Winner points out in [2], it would need test driving without accidents for hundreds of millions of kilometers to prove statistically, that the risk of autonomous vehicles is low enough to argue the safe operation; this kind of straight forward system verification would not lead to a practical implementation (because of time and cost issues) and furthermore would still leave gaps in an exhaustive safety argument. As in other industries with low risk requirements (aerospace, power supply systems, automated production systems, etc.), other methods have to be used to prove the safety performance of automated vehicles [3]. System design for such systems relies on redundant subsystems with well understood, logic-based safety arguments. But functional safety (based on robust and uncorrelated subsystems) is only one part of the design; functional completeness, i.e. the proof that any conceivable situation can be handled by the system, needs a very systematical system design and thorough verification and validation procedures. For this part of the development and testing task, simulation plays an important role [6]. This paper shall focus on the goals, the required tools and components, and the approaches of simulation for development and testing of autonomous vehicles.
This paper describes a driving simulation experiment, executed on the Daimler Driving Simulator (DDS), in which a filter-based and an optimization-based motion cueing algorithm (MCA) were compared using a newly developed motion cueing quality rating method. The goal of the comparison was to investigate whether optimization-based MCAs have, compared to filter-based approaches, the potential to improve the quality of motion simulations. The paper describes the two algorithms, discusses their strengths and weaknesses and describes the experimental methods and results. The MCAs were compared in an experiment where 18 participants rated the perceived motion mismatch, i.e., the perceived mismatch between the motion felt in the simulator and the motion one would expect from a drive in a real car. The results show that the quality of the motion cueing was rated better for the optimization-based MCA than for the filter-based MCA, indicating that there exists a potential to improve the quality of the motion simulation with optimization-based methods. Furthermore, it was shown that the rating method provides reliable and repeatable results within and between participants, which further establishes the utility of the method.
Das Verbundprojekt PEGASUS (Projekt zur Etablierung von generell akzeptierten Gutekriterien, Werkzeugen und Methoden sowie Szenarien und Situationen zur Freigabe
hochautomatisierter Fahrfunktionen) schliest Lucken in den Bereichen Testen und Freigabe von automatisierten Fahrzeugen, damit bereits vorliegende Funktionen und Prototypen zeitnah in Serienprodukte uberfuhrt werden konnen. Zentraler Use-Case des Projekts ist eine zukunftsnahe hochautomatisierte beispielhafte Fahrzeugfunktion, der Autobahn-Chauffeur. In dem vom Bundesministerium fur Wirtschaft und Energie (BMWi) geforderten Projekt erarbeiten insgesamt 17 Partner eine durchgangige Werkzeugkette, unter anderem mit Kriterien und Masen zur Funktionsbewertung, Guteniveaus, Testkataloge sowie zentrale Methoden und Prozessen zur Absicherung und Freigabe hochautomatisierter Fahrfunktionen. Die Werkzeugkette wird dabei innerhalb des Projektes prototypisch aufgebaut und praktisch demonstriert. Es resultiert ein neuer herstellerubergreifender Stand der Technik zur Absicherung von Assistenz- bzw. Automatisierungsfunktionen, der die spatere Freigabe und Zulassung vorbereitet.
To lay the basis of studying autonomous driving comfort using driving simulators, we assessed the behavioral validity of two moving-base simulator configurations by contrasting them with a test-track setting.With increasing level of automation, driving comfort becomes increasingly important. Simulators provide a safe environment to study perceived comfort in autonomous driving. To date, however, no studies were conducted in relation to comfort in autonomous driving to determine the extent to which results from simulator studies can be transferred to on-road driving conditions.Participants ( N = 72) experienced six differently parameterized lane-change and deceleration maneuvers and subsequently rated the comfort of each scenario. One group of participants experienced the maneuvers on a test-track setting, whereas two other groups experienced them in one of two moving-base simulator configurations.We could demonstrate relative and absolute validity for one of the two simulator configurations. Subsequent analyses revealed that the validity of the simulator highly depends on the parameterization of the motion system.Moving-base simulation can be a useful research tool to study driving comfort in autonomous vehicles. However, our results point at a preference for subunity scaling factors for both lateral and longitudinal motion cues, which might be explained by an underestimation of speed in virtual environments.In line with previous studies, we recommend lateral- and longitudinal-motion scaling factors of approximately 50% to 60% in order to obtain valid results for both active and passive driving tasks.
Abstract General properties of piezo-electric motors are explained. A short review of their historical development is given. Limiting factors for the power of piezo-electric motors are discussed in comparison to standard electromagnetic motors. Aspects for the choice of piezo-electric materials for motor applications result from these limits.
