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The model driven software development (MDSD) paradigm is gaining momentum in developing extensive business software applications. With MDSD it can be carried out a significant contribution towards the key factors of success which are exibility and adherence to delivery dates as well as efficient maintenance and adaptability. With AtomsPro that is introduced here we consequently integrate aspects of domain modelling and software architecture as well as clear and understandable means to enable domain experts to take an active role in the software development process. The challenging aim is to increase the overall ratio of code generated in the development process of enterprise applications by at the same time preserving abilities for efficient maintenance. Hence, we have chosen a fruitful cooperation with several involved project partners.
Speeding up Discrete Event Simulations (DESs) is a broad research field. Promising Parallel Discrete Event Simulation (PDES) approaches with optimistic and conservative synchronisation schemes have emerged throughout the years. However, in the area of real-time simulation, PDESs are rarely considered. This is caused by the complex problem of fitting parallel executed DES models to a real-time clock. Hence, this paper gives an extensive review of existing conservative and optimistic synchronisation schemes for PDESs. It introduces a metric to compare their real-time capabilities to determine whether they can be used for soft or firm real-time simulation. Examples are given on how to apply this metric to evaluate PDESs using synthetic and real-world examples. The results of the investigation reveal that no final answer can be given if PDESs can be used for soft or firm real-time simulation as they are. However, boundary conditions were defined, which allow a use-case specific evaluation of the real-time capabilities of a certain parallel executed DES. Using this in-depth knowledge and can lead to predictability of the real-time behaviour of a simulation run.
The prediction of the motion of traffic participants is a crucial aspect for the research and development of Automated Driving Systems (ADSs). Recent approaches are based on multi-modal motion prediction, which requires the assignment of a probability score to each of the multiple predicted motion hypotheses. However, there is a lack of ground truth for this probability score in the existing datasets. This implies that current Machine Learning (ML) models evaluate the multiple predictions by comparing them with the single real trajectory labeled in the dataset. In this work, a novel data-based method named Probabilistic Traffic Motion Labeling (PROMOTING) is introduced in order to (a) generate probable future routes and (b) estimate their probabilities. PROMOTING is presented with the focus on urban intersections. The generation of probable future routes is (a) based on a real traffic dataset and consists of two steps: first, a clustering of intersections with similar road topology, and second, a clustering of similar routes that are driven in each cluster from the first step. The estimation of the route probabilities is (b) based on a frequentist approach that considers how traffic participants will move in the future given their motion history. PROMOTING is evaluated with the publicly available Lyft database. The results show that PROMOTING is an appropriate approach to estimate the probabilities of the future motion of traffic participants in urban intersections. In this regard, PROMOTING can be used as a labeling approach for the generation of a labeled dataset that provides a probability score for probable future routes. Such a labeled dataset currently does not exist and would be highly valuable for ML approaches with the task of multi-modal motion prediction. The code is made open source.
Cooperative Driving
(2022)
The effect of decentralized congestion control on collective perception in dense traffic scenarios
(2018)
Teleoperation: the holy grail to solve problems of automated driving? Sure, but latency matters
(2019)
A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments.
Dynamic Performance Stubs support performance improvement. They can be used to identify “hidden” bottlenecks and also to provide better estimations of the gain from performance improvement. The idea behind these stubbing mechanism will be described, the core concept will be explained and a closer look on the possibility of creating stubs will be given. Furthermore an introduction on “How to use dynamic performance stubs” is also included.
A lot of applications have been sketched in the area of Car2X-Communication. Safety aspects are often mentioned among others as a motivation for Car2X-Communication systems. We present a statistical based selection of feasible applications with the goal of increasing safety. Therefore the official statistical information of the year 2007 has been considered.
Additionally we will present some consequences for the introduction of Car2X-Communication systems including infrastructure.
Dynamic performance stubs provide a framework to simulate the performance behavior of software modules and functions. Hence, they can be used as an extension to software performance engineering methodologies. The methodology of dynamic performance stubs targets to gain oriented performance improvement. Other applications include the identification of "hidden" bottlenecks and the prioritization of optimization alternatives. Main memory stubs have been developed to extend the simulation possibilities of the dynamic performance stubs framework. They are able to simulate the heap and stack behavior of software modules or functions. This paper extends and improves the simulation algorithm to be able to simulate constant stack values. Moreover, it presents calibration possibilities to improve the simulation results by determining the various overhead in the algorithm. The results are furthermore used to compensate inaccuracies in the simulation. Additionally, a proof of concept is given as validation of the results. This paper shows that, main memory stubs can be used to simulate the heap, stack and timing behavior exactly when considering the parameters determined by the calibration functions.
Building CPU Stubs to Optimize CPU Bound Systems: An Application of Dynamic Performance Stubs
(2011)
RFID hat sich als Automatisierungstechnologie im Logistiksektor seit Jahren etabliert, um einerseits Qualität, Sicherheit und Transparenz von Prozessen zu verbessern und anderer-seits die Möglichkeit für Rationalisierung und Kostensenkung zu bieten.
- Eine Hürde für KMUs sind aber die hohen Implementierungskosten. Rund 50% der Kosten für Automatisierungssysteme, welche hier als Plattformtechnologie für RFID gesehen wird, entfallen auf die Software, Tendenz steigend. Außerdem entfallen wie-derum 60% bis sogar 80% im Bereich der hochverfügbaren Softwaresysteme auf das Testen der Software.
- Es existieren Methodiken zum Testen von Automatisierungs-, Mechatroniksystemen und zum Testen von reinen Softwareapplikationen. Eine spezielle Testmethodik allerdings, die die Hard- und Software-Spezifika von RFID-Systemen berücksichtigt, existiert nicht.
Um den Unsicherheiten beim Testen von RFID-Anwendungen zu begegnen zielte das Forschungsvorhaben ITERA darauf ab, eine neue Methodik für das gezielte Testen derartiger Anwendungen zu entwickeln. Dabei wurde im Projektverbund zunächst untersucht ob eine Integration von existierenden Techniken und Werkzeugen möglich ist und schließlich mit der Entwicklung einer Methodik zur Anwendung von generischen und reproduzierbaren Testszenarios für RFID-Anwendungen begonnen. Die Aufgabe der Technischen Hochschule Ingolstadt bestand im Besonderen darin, die wissenschaftliche Grundlage zu schaffen und anhand dieser eine Me-thodik und ein Konzept zum Testen von RFID Anwendungen zu entwickeln. Die aus dem Gesamtprojekt resultierende Methodik kann grob, wie folgt skizziert werden:
- Modultests für Softwarekomponenten, durchgeführt mit Hilfe von vorhandenen Frameworks wie JUnit oder NUnit.
- Integrationstest (Bottom-Up) für die verwendeten RFID-Hardwarekomponenten. Dient der Sicherstellung, dass die Funktion der Komponenten und Übertragungswege (Luftschnittstelle) gegeben ist.
- Hauptfokus sind Integrationstests (Top-Down), rein für Softwarekomponenten mithilfe der zuvor definierten, virtuellen RFID-Komponenten. Ziel der virtuellen Komponenten ist es, dem Entwickler der Anwendungssoftware ein Mittel an die Hand zu geben, mit dem er Testläufe ohne jegliche RFID-bezogene Hardware ausführen kann.
- Systemtest der vollständig aufgesetzten RFID-Systemumgebung.
Aus der entwickelnden Methodik entstehen eine Reihe von Vorteilen, wie der frühen Fehler-erkennung, ein effizienterer Testvorgang, die Möglichkeit zur Anwendung agiler Entwick-lungsmethoden, parallelisieren von Testphasen und der Anwendung von bewährten Methoden der Leistungsanalyse und Leistungsverbesserung. Diese Aspekte schlagen sich positiv auf die Softwarequalität, Entwicklungszeit und somit auch auf die Entwicklungskosten nieder.
A Survey on Decentralized Cooperative Maneuver Coordination for Connected and Automated Vehicles
(2021)
V2X communications can be applied for maneuver coordination of automated vehicles, where the vehicles exchange messages to inform each other of their driving intentions and to negotiate for joint maneuvers. For motion and maneuver planning of automated vehicles, the cooperative maneuver coordination extends the perception range of the sensors, enhances the planning horizon and allows complex interactions among the vehicles. For specific scenarios, various schemes for maneuver coordination of connected automated vehicles exist. Recently, several proposals for maneuver coordination have been made that address generic instead of specific scenarios and apply different schemes for the message exchange of driving intentions and maneuver negotiation. This paper presents use cases for maneuver coordination and classifies existing generic approaches for decentralized maneuver coordination considering implicit and explicit trajectory broadcast, cost values and space-time reservation. We systematically describe the approaches, compare them and derive future research topics.