Managed and Continuous Evolution of Dependable Automotive Software Systems / Andreas Rausch, Oliver Brox, Axel Grewe, Marcel Ibe, Stefanie Jauns-Seyfried, Christoph Knieke, Marco Körner, Steffen Küpper, Malte Mauritz, Henrik Peters, Arthur Strasser, Martin Vogel, Norbert Weiss

Automotive software systems are an essential and innovative part of nowadays connected and automated vehicles. Automotive industry is currently facing the challenge to re-invent the automobile. Consequently, automotive software systems, their software systems architecture, and the way we engineer those kinds of software systems are confronted with major challenges: managing complexity, providing flexibility, and guaranteeing dependability of the desired automotive software systems and the corresponding engineering process. In this paper we will present an improved and sophisticated engineering approach. Our approach is based on the managed and continuous evolution of dependable automotive software systems. It helps engineers to manage system complexity based on continous engineering processes to iteratively evolve automotive software systems and therby guarantee the required dependability issues. Based on a running sample, we will present and illustrate the main assets of the proposed engineering approach for managed and continuous evolution of dependable automotive software systems

Managed and Continuous Evolution of Dependable Automotive Software Systems / Andreas Rausch, Oliver Brox, Axel Grewe, Marcel Ibe, Stefanie Jauns-Seyfried, Christoph Knieke, Marco Körner, Steffen Küpper, Malte Mauritz, Henrik Peters, Arthur Strasser, Martin Vogel, Norbert Weiss

Automotive software systems are an essential and innovative part of nowadays connected and automated vehicles. Automotive industry is currently facing the challenge to re-invent the automobile. Consequently, automotive software systems, their software systems architecture, and the way we engineer those kinds of software systems are confronted with major challenges: managing complexity, providing flexibility, and guaranteeing dependability of the desired automotive software systems and the corresponding engineering process. In this paper we will present an improved and sophisticated engineering approach. Our approach is based on the managed and continuous evolution of dependable automotive software systems. It helps engineers to manage system complexity based on continous engineering processes to iteratively evolve automotive software systems and therby guarantee the required dependability issues. Based on a running sample, we will present and illustrate the main assets of the proposed engineering approach for managed and continuous evolution of dependable automotive software systems

Engineering of safe autonomous vehicles through seamless integration of system development and system operation

Autonomous vehicles will share the road with human drivers within the next couple of years. This will revolutionize road trac and provide a positive benet for road safety, trac density, emissions, and demographic changes. One of the signicant open challenges is the lack of established and cost-ecient veri- cation and validation approaches for assuring the safety of autonomous vehicles. The general public and product liability regulations impose high standards on manufacturers regarding the safe operation of their autonomous vehicles. The vast number of real- world trac situations have to be considered in the verication and validation. Todays conventional engineering methods are not adequate for providing such guarantees for au- tonomous vehicles in a cost-ecient way. One strategy for reducing the costs of quality assurance is transferring a signicant part of the verication and validation from road tests to (system-level) simulations. The vast number and high complexity of real-world situations complicate the exhaustive verication of autonomous vehicles in simulations. It is not clear, how simulations address the vast number of real-world situations with sucient realism and how their results transfer to the real road. Extensive coverage of real-world situations in simulations requires the integration of de- velopment and operation. This thesis presents an engineering approach that integrates the development and operation of autonomous vehicles seamlessly using runtime moni- toring. The runtime monitoring veries if autonomous vehicles satisfy their requirements and operate within safe limits which have been veried in the simulations. Safety of autonomous vehicles is subject to the scope of veried trac situations in simulations. Systematic and comprehensive simulations support the improvement of autonomous vehicles and coverage of trac situations. Results of the runtime monitoring during operation are transferred to the development for the verication of autonomous vehicles and their safe limits in simulations with additional trac situations. The incomplete verication of autonomous vehicles for the vast number of real-world trac situations in simulations requires the validation of simulation results and addi- tional monitoring in the real world. Results from simulations are transferred to the runtime monitoring during operation in the real world for validating the realism of the simulations and maintaining the vehicle safety in critical situations. Vehicle data and real-world situations possess high complexities and, therefore, impact the complexity and eciency of the verication in simulations. The runtime monitoring abstracts from internal data of autonomous vehicles and real-world situations in the evaluation by introducing an abstract semantic representation from natural language requirements. A case study evaluates the engineering approach for an industrial lane change assistant and real-world trac data recorded in road tests on German highways.Autonome Fahrzeuge werden in den nächsten Jahren am Straßenverkehr teilnehmen und die Straße mit menschlichen Fahrern teilen. Dies wird den Straßenverkehr grundlegend revolutionieren. Die Einführung des autonomen Fahrens wird einen positiven Einfluss auf die Verkehrssicherheit, Verkehrsdichte, Emissionswerte und demographische Veränderungen haben. Neben rechtlichen Unklarheiten fehlen etablierte und kosteneffiziente Ansätze zur Verifikation und Validierung, um den notwendigen Grad an Sicherheit für die Einführung autonomer Fahrzeuge in den Straßenverkehr nachzuweisen. Produkthaftungsregularien und gesellschaftliche Erwartungen zwingen Fahrzeughersteller einen hohen Grad an Sicherheit für ihre autonomen Fahrzeuge sicherzustellen. Eine wesentliche Herausforderung für die Verifikation und Validierung autonomer Fahrzeuge stellt die überabzählbare Menge an realen Verkehrssituationen dar. Heutige konventionelle Entwicklungs- und Absicherungsmethoden in der Automobilbranche können die benötigten Garantien nicht verlässlich für autonome Fahrzeuge unter akzeptablen Kosten liefern. Eine Strategie in der Automobilbranche zur Kostenreduktion in der Absicherung von autonomen Fahrzeugen ist die Verlagerung eines umfangreichen Teils der Absicherung von Fahrten im realen Straßenverkehr in Simulationen. Die umfassende Verifikation autonomer Fahrzeuge in Simulationen wird durch die überabzählbare Anzahl und hohe Komplexität von realen Verkehrssituationen erschwert. Es ist derzeit nicht klar, wie Simulationen die überabzählbare Menge an realen Verkehrssituationen mit ausreichenden Realismus adressieren können und wie sich die Ergebnisse der Simulation auf den realen Verkehr übertragen lassen. Eine umfassende Abdeckung realer Verkehrssituationen in Simulationen erfordert die nahtlose Integration der Systementwicklung und des Betriebs autonomer Fahrzeuge. Diese Arbeit präsentiert eine Entwicklungsmethodik, die die Entwicklung und den Betrieb autonomer Fahrzeuge durch Runtime Monitoring integriert. Das Runtime Monitoring Framework überprüft, ob das autonome Fahrzeug die an es gestellten Anforderungen in realen Situationen erfüllt und in seinen Systemgrenzen operiert, die zuvor in Simulationen abgesichert wurde. Die Sicherheit autonomer Fahrzeuge hängt von dem Umfang der verifizierten Verkehrssituationen in den Simulationen ab. Systematische und umfassende Simulationen bilden die notwendige Grundlage für eine kontinuierliche Verbesserung autonomer Fahrzeuge und der Abdeckung von Verkehrssituationen. Ergebnisse des Runtime Monitorings aus dem Betrieb im realen Straßenverkehr werden in die Systementwicklung überführt, um die Verifikation der Funktionalität und der sicheren Systemgrenzen autonomer Fahrzeuge in neuen Verkehrssituationen zu ergänzen. Die unvollständige Verifikation autonomer Fahrzeuge für die überabzählbare Menge an realen Verkehrssituationen hat zur Folge, dass der Betrieb autonomer Fahrzeuge und die Ergebnisse der Situation zusätzlich im realen Straßenverkehr durch die Laufzeitüberwachung verifiziert und validiert werden müssen. Die Verwendung der Ergebnisse aus den Simulationen zur Laufzeitüberwachung während des Betriebs im realen Straßenverkehr erlaubt den Realismus der Simulation zu validieren und die Sicherheit der autonomen Fahrzeuge in kritischen Situationen sicherzustellen. Fahrzeugdaten und reale Verkehrssituationen haben aufgrund ihres hohen Grades an Komplexität einen signifikanten Einfluss auf die Komplexität und Effizienz der Verifikation autonomer Fahrzeuge in Simulationen. Das Runtime Monitoring Framework definiert eine abstrakte semantische Repräsentation, die von den internen Daten der autonomen Fahrzeuge und den realen Verkehrssituationen abstrahiert. Diese abstrakte semantische Repräsentation wird basierend auf den natürlich-sprachlichen Anforderungen der autonomen Fahrzeuge definiert. Die Evaluation der Entwicklungsmethodik erfolgt in einer Fallstudie anhand eines industriellen Fahrstreifenwechselassistenten und Aufnahmen von Testfahrten im realen Straßenverkehr

Engineering of Safe Autonomous Vehicles through Seamless Integration of System Development and System Operation

One of the significant open challenges is the lack of verification and validation approaches for assuring the safety of autonomous vehicles. The vast number of realworld traffic situations have to be considered in the verification and validation. Today’s conventional engineering methods are not adequate for providing such guarantees for autonomous vehicles in a cost-efficient way. One strategy for reducing the costs of quality assurance is transferring a significant part of the verification and validation from road tests to (system-level) simulations. Extensive coverage of real-world situations in simulations requires the integration of development and operation. This thesis presents an engineering approach that integrates the development and operation of autonomous vehicles seamlessly using runtime monitoring. The runtime monitoring verifies if autonomous vehicles satisfy their requirements and operate within safe limits which have been verified in the simulations. Systematic and comprehensive simulations support the improvement of autonomous vehicles and coverage of traffic situations. Results of the runtime monitoring during operation are transferred to the development for the verification of autonomous vehicles and their safe limits in simulations with additional traffic situations. The incomplete verification of autonomous vehicles for the vast number of real-world traffic situations in simulations requires the validation of simulation results and additional monitoring in the real world. Results from simulations are transferred to the runtime monitoring during operation in the real world. Vehicle data and real-world situations possess high complexities and, therefore, impact the complexity and efficiency of the verification in simulations. The runtime monitoring abstracts from internal data of autonomous vehicles and real-world situations in the evaluation

Minnesteckningar över avlidna ledamöter 2013. Särtryck ur KVVS Årsbok 2014

Minnesteckningar. Kungl. Vetenskaps- och Vitterhets-Samhället (KVVS) - ledamöter avlidna 2013 (i tidsföljd): Jan Hult, Bertil Åkesson, Bengt Rundblad, Ingemar Fernlund, Jan Ling

Learning Symbolic Timed Models from Concrete Timed Data – Data and Replication Package

Artifacts for the publication “Learning Symbolic Timed Models from Concrete Timed Data”. This includes data, code, and a reproduction package