CARLA Scenarios

Results for autonomous driving scenarios simulated with CARLA under different environment conditions.This work was supported by the Bavarian Ministry of Economic Affairs, Regional Development and Energy through the Center for Analytics–Data–Applications (ADACenter) within the framework of ”BAYERN DIGITAL II”.The models have been trained on the datasets Berkely Deep Drive, COCO and KITTI with ensemble distribution distillation and standard softmax training. As base model architecture a slightly advanced version of Yolov3 has been used

Verlässliche Adaptive Software-Architekturen im Auto: Von Fail-Silent zu Fail-Operational

Durch die zunehmende Automatisierung bis hin zum autonomen Fahren verändern sich auch die elektrisch-elektronischen (E/E) Architekturen sowie die Anforderungen an die Funktionalität von Fahrzeugen. Das hat zur Folge, dass Software-Architekturen eine zunehmende Flexibilität aufweisen und gleichzeitig eine erhöhte Zuverlässigkeit garantieren müssen

DANA - Description and Analysis of Networked Applications

We introduce the DANA platform for specifying and analyzing networked applications. DANA was originally created targeting the automotive domain for the verification and validation of software interface behavior in new infotainment and advanced driver assistant systems that are integrated on a single hardware platform. The messages in these interfaces can contain complex data, e.g., playlists with images. Therefore, valid behavior is described as a layered reference model. The platform can use the model to generate test cases, code for simulation, and to verify a live or recorded trace. Exchangeable resumption algorithms enable DANA to resume runtime verification after a deviation using the original state machine without manual changes. A generic input model allows quick integration of new sources for messages. Therefore, DANA can easily be applied to other domains where interactive behavior can be observed. In this paper, we present the tool, its layered reference model, and show its application for runtime verification

Absicherung vernetzter IoT-Funktionen mit selbstlernenden Modellen: Paper präsentiert auf der Konferenz "Internet of Things - vom Sensor bis zur Cloud", 19. Oktober 2017, München

Mit dem Internet-of-Things (IoT) wird die Verknüpfung diverser Systeme von eingebetteten Steuerungen über Cloud-Services und diverse Frameworks sowie Plattformen möglich. Für den Nachrichtenaustausch der beteiligten Komponenten existiert bereits heute eine Vielzahl von grundlegenden Kommunikationsprotokollen. Die darauf aufbauenden Anwendungsprotokolle sind jedoch in der Regel spezifisch für einzelne Applikationen definiert und umgesetzt. Um auch das korrekte Funktionieren einer vernetzten Anwendung sicherzustellen, muss deren Interaktion mit anderen Komponenten abgesichert und verifiziert werden. Damit dies effizient möglich ist, sind neue Verfahren zur automatisierten Absicherung notwendig, so dass die korrekte Interaktion solcher verteilter Anwendungen sichergestellt werden kann. Hierfür wird ein modellgetriebenes Verfahren vorgestellt, welches es erlaubt Fehler automatisiert anhand des Kommunikationsverhaltens festzustellen. Um den Aufwand hierfür gering zu halten und auch neue, zunächst unbekannte Anwendungen absichern zu können, wird ein selbstlernendes Verfahren eingesetzt. Das kann teilautomatisiert Modelle aus Anwendungsprotokollen erzeugen, die dann wiederum überprüft und weiterverwendet werden können. So können diese Modelle auch wieder zur automatisierten Absicherung der Anwendungen genutzt werden. Das Verfahren wurde in verschiedenen Projekten und Anwendungsszenarios, wie am Beispiel einer Modell-Produktionsanlage, bereits erfolgreich erprobt

Rapid Innovation Toolkit for the development of dependable cooperative applications

Cooperative applications have an enormous potential to improve future mobility systems. Though, special challenges regarding safety and security arise out of the connectivity and the distribution of the application among heterogeneous systems. These include expensive and time-consuming development and test phases. Especially, the debugging of an application, whose sub-functions are located on heterogeneous and partially mobile systems, requires a new kind of testing environment. The test and validation of the overall application is complex, as the wireless link implies varying timing behaviour and less data confidence. For this purpose, the proposed testbed integrates the DANA (“Description and Analysis of Networked Applications”) Framework to achieve a central overview of the overall application and the behaviour of all systems involved. This software tool kit is able to find deviations from the specified behaviour and also it can instantly locate and identify erroneous functions. In this paper, we present a solution for the complete development cycle of cooperative automotive systems together with an exemplary development flow for safety and security testing

Software implementieren und absichern: Mit Modellierung zum schnelleren Prototyping

Ein plötzliches Verkehrshindernis kann zur Gefahr werden. Vor allem dann, wenn es Autofahrer zu spät wahrnehmen und es nicht schaffen, rechtzeitig darauf zu reagieren. Ein Gefahrenwarner kann dem vorbeugen. Jedoch sind solche fahrzeugübergreifenden Funktionen sehr komplex: Sie erfordern neue Entwicklungsansätze und Entwicklungswerkzeuge. Das heißt vor allem, dass vernetzte Fahrfunktionen schon früh im Entwurf simuliert und getestet werden sollten

Towards safety-awareness and dynamic safety management

Future safety-critical systems will be highly automated or even autonomous and they will dynamically cooperate with other systems as part of a comprehensive ecosystem. This together with increasing utilization of artificial intelligence introduces uncertainties on different levels, which detriment the application of established safety engineering methods and standards. These uncertainties might be tackled by making systems safety-aware and enabling them to manage themselves accordingly. This paper introduces a corresponding conceptual dynamic safety management framework incorporating monitoring facilities and runtime safety-models to create safety-awareness. Based on this, planning and execution of safe system optimizations can be carried out by means of self-adaptation. We illustrate our approach by applying it for the dynamic safety assurance of a single car

Verifying network performance of cyber-physical systems with multiple runtime configurations

Modern Cyber-Physical Systems (CPS) must increasingly adapt to changing contexts, like smart cars to changing driving conditions. Thus, design approaches are facing a rapidly growing number of network runtime configurations. With recent approaches this problem can be solved for design space exploration (DSE) by analyzing the network performance of single configurations which are intended to represent the entire runtime variability space. This technique can be applied for DSE since the latter only intends to find an optimized system setup. Yet it does not meet the requirements of network verification, since it does not necessarily find the worst-case for all applications. To solve this, we developed an integrated model, which allows describing runtime variability in the network performance model with a0-1 linear-fractional program. Thus, we can cover entire runtime variability spaces without analyzing every single network runtime configuration. Although the approach utilizes heuristics, it still guarantees worst-case results. We can show that in comparison to state-of-the-art methods our approach scales for large automotive systems with multiple network configurations. Moreover, our evaluation results highlight the superior capabilities of our method with respect to accuracy and computation time

Verification of network end-to-end latencies for adaptive ethernet-based cyber-physical systems

As Cyber-Physical Systems (CPS) are evolving towards flexible and smart systems, their dependable communication becomes a decisive factor. In order to still guarantee a predictive and real-time behavior, verifying the network performance of such adaptive systems is vital. Therefore, the performance-verification has to consider the runtime variability while scaling for larger number of applications and networks in CPS. We introduce a novel performance-verification approach with integrated variability enabling the analysis of adaptive Ethernet-based CPS. It incorporates a formal model capturing all relevant characteristics for deriving safe communication bounds. Its soundness has been evaluated in an extensive automotive case study and several changing test setups targeting scalability. The results show that this integrated variability approach is superior to a common static analysis and previously utilized heuristic. In direct comparison it outperforms static analysis by up to 95 percent within the evaluated automotive system. Moreover, the results show that it scales well and provides a profound basis for analyzing larger adaptive networked systems

Context modeling for dynamic configuration of automotive functions

Current vehicles are usually equipped with an abundance of advanced driver assistant systems. Only a limited number of them can really be active permanently. This motivates our goal of providing the car with the means necessary to dynamically adapt the set of active functions to its current requirements. In this paper, we present a generic context modeling approach suitable for dynamic configuration of automotive functions. The demonstration of the feasibility of the proposed solution and evaluation of its effectiveness was based on a simulated prototypical system configuration. The simulations yielded to a significant reduction in average function activity of an exemplary car system. Depending on the provided context parameters, a reduction of up to 24% was achieved