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Keywords
For more than a decade, ADAC accident researchers have analysed road accidents with severe injuries, recording some 20,000 accidents. An important task in accident research is to determine the causative factors of road accidents. Apart from vehicle engineering and human factors, accident research also focuses on infrastructural and
environmental aspects.
To find out what accident scenarios are the most common in ADAC accident research and what driver assistance systems can prevent them, our first task was to conduct a detailed accident analysis.
Using CarMaker, we performed a realistic simulation of accident scenarios, including crashes, with varying parameters. To begin with, we made an initial selection of driver assistance systems in order to determine those with the greatest accident prevention potential.
One important finding of this study is that the safety potential of the individual driver assistance systems can actually be examined. It also turned out that active safety offers even much more potential for development and innovation than passive safety. At the same time, testing becomes more demanding, too, as new systems keep entering the market, many of them differing in functional details.
ADAC will continue to test all driver assistance systems as realistically as possible so as to be able to provide advice
to car buyers. Therefore, it will be essential to develop and improve test conditions and criteria.
Advanced driver assistance systems (ADAS), Highly Automated Driving Functions (HAD) and Autonomous Driving (AD) provide among comfort to the driver also a great potential for future mobility and tends to increase traffic and car safety. All ADAS, HAD and AD functions and especially those associated with high safety levels, require paradigm-changing approaches for the homologation: some ADAS and AD functions require up to about 200 million km of real drive testing for the qualification. This amount of real drive testing is not feasible for any OEM and therefore there is a strong need for a mixed test strategy where the performed real drive tests take credit from a virtual campaign evaluation. Such a combined real and virtual test strategy could reduce the necessary efforts for the qualification of a given function development and its validation.
To support virtual testing, the triangle of the driver, the vehicle and the environment has to be modeled for simulation. The interface between the vehicle and the environment, i.e. a sensor that is a device to transform physical information into electrical signals, is of crucial importance for the ADAS, HAD and AD function, because it replaces step by step the perception of the driver in the car.
This presentation gives a survey about the challenges of the modeling of sensors and their interaction with the fusion strategy and the automated driving functions and the crucial role of high fidelity environment models in order to get confidence in the simulations as a part for the homologation.
The presentations lists also first insights in results of the environment modeling of the city of Kempten/Allgäu.
The functional mock-up interface (FMI) for co-simulation (CS) aims to provide a generic representation of dynamic system models, which can be coupled for co-simulations. A system model con-forming to FMI is called a functional mock-up unit (FMU). Being a standardised interface FMI for CS defines not only the required structure of FMUs but also provides an application programming interface (API) for coupled integration of FMUs in a support-ing simulation environment. FMI for CS is an exten-sion of FMI for Model Exchange (ME), which is mainly intended to provide standardised model descriptions. Since this work is focused on co-simulation the indicating suffix shall be omitted such that FMI stands for FMI for CS. FMI facilitates enhanced simulation workflows through better exchange of models between de-partments and vendors. While a growing number of vendors seeks to adopt FMI, the compliance of integration environments and FMUs to the FMI standard gains in significance. Only fully compliant simulation environments and FMUs reliably yield simulation results that aid actual design decisions. The intention of this work is to develop a proper means to detect ambiguities and errors in imple-mentations of FMI 1.0. The task is twofold in that both FMUs and integration environments using these FMUs may violate constraints given by the FMI standard. This work will focus on the role of integration environments.
In the early phase of the product development, it is crucial to quickly and accurately evaluate a systems overall performance in order to fully define and optimize viable system and functional architectures. The presentation explains the development steps for an embedded controller. Typically, the behavior of a dynamic system (plant and controller) is in general to complex to treat by theory or formulas. Several simulation methods has established for analyzing such systems.
The presented virtual integration method allows to model and simulate the entire system, and thus the validation of the design decisions in an early phase of the development. This approach is conducted on a model in equation based languages to gain knowledge about the (intended) real system behabior. Such an abstraction typically allows to focus on the main properties and their effects of the studied multi-domain system.
The new approach of virtual integration is demonstrated for the development of a control algorithm for an embedded controller. The entire system - both the plant and the control components – is designed with the modeling language Modelica. All necessary activities are presented for the role of the function developer and explained for the example traffic light controller for a simple intersection.
The virtual integration method usualy combines components that require specific domain solvers for mechanical, electrical, etc. components, and, consequently, is based on the co-simulations.
Entscheidende Elemente und Verfahren, die zum heutigen autonomen Fahren führten, wurden von Prof. Dickmanns entwickelt. Die Tragweite seiner Arbeiten kann gar nicht überschätzt werden. Autonomes Fahren wird die Zahl der Unfälle reduzieren und die Mobilität der Menschen in unserer alternden Gesellschaft erhalten. Sie hat zudem das Potential, die Innenstädte von parkenden Autos zu befreien und diese Flächen den Menschen zurückzugeben. Für seine herausragende Rolle in der Entwicklung des autonomen Fahrens wird Prof. Dickmanns am 14. Oktober 2017 mit dem Eduard-Rhein-Technologiepreis im Ehrensaal des Deutschen Museums geehrt. Prof. Dr. Christoph Günther
Kempten hat sich zu einer Drehscheibe für angehende Automobilingenieure entwickelt. Der ADAS-Master der Universität ist der weltweit einzige seiner Art. Sechs Studenten erzählen uns, warum sie vom automatisierten Fahren fasziniert sind, was sie von zukünftigen Arbeitgebern erwarten und wie sich der „Fahrspaß“ mit autonomen Autos verändern wird.
The performance of an automated driving system is crucially affected by its environmental perception. The vehicle's perception of its environment provides the foundation for the automated responses computed by the system's logic algorithms. As perception relies on the vehicle's sensors, simulating sensor behavior in a virtual world constitutes virtual environmental perception. This is the task performed by sensor models. In this work, we introduce a real-time capable model of the measurement process for an automotive lidar sensor employing a ray tracing approach. The output of the model is point cloud data based on the geometry and material properties of the virtual scene. With this low level sensor data as input, a vehicle internal representation of the environment is constructed by means of an occupancy grid mapping algorithm. By using a virtual environment that has been constructed from high-fidelity measurements of a real world scenario, we are able to establish a direct link between real and virtual world sensor data. Directly comparing the resulting sensor output and environment representations from both cases, we are able to quantitatively explore the validity and fidelity of the proposed sensor measurement model.
Fueled by the continuous, rapid progress within microelectronics, ever more intelligent and intricate functions are realized in mechatronic systems. To control the complexity associated with such designs, modelbased control design methods are increasingly adapted in industry. Despite Modelica’s obvious suitability to
efficiently create appropriate high fidelity system models, the utilization of Modelica for developing discrete control functions is not yet wide spread. Adoption of Modelica for this task offers the potential for a seamless development methodology from the logical virtual model down to the technical system architecture, with corresponding traceability and maintainability benefits.
This contribution will specifically address this potential and propose a Modelica sub- and superset adequate for use within the development of safety-relevant control applications.
we present a tool for the automatic generation of test stimuli for small numerical support functions, e.g., code for trigonometric functions, quaternions, filters, or table lookup. Our tool is based on KLEE to produce a set of test stimuli for full path coverage. We use a method of iterative deepening over abstractions to deal with floating-point values. During actual testing the stimuli exercise the code against a reference implementation. We illustrate our approach with results of experiments with low-level trigonometric functions, interpolation routines, and mathematical support functions from an open source UAS autopilot.