Dr. rer. nat.
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PublicationFuzzy Interpretation of Operational Design Domains in Autonomous Driving( 2022-07)
; ; ; ;Oboril, FabianBuerkle, CorneliusThe evolution towards autonomous driving involves operating safely in open-world environments. For this, autonomous vehicles and their Autonomous Driving System (ADS) are designed and tested for specific, so-called Operational Design Domains (ODDs). When moving from prototypes to real-world mobility solutions, autonomous vehicles, however, will face changing scenarios and operational conditions that they must handle safely. Within this work, we propose a fuzzy based approach to consider changing operational conditions of autonomous driving based on smaller ODD fragments, called μODDs. By this, an ADS is enabled to smoothly adapt its driving behavior for meeting safety during shifting operational conditions. We evaluate our solution in simulated vehicle following scenarios passing through different μODDs, modeled by weather changes. The results show that our approach is capable of considering operational domain changes without endangering safety and allowing improved utility optimization.
PublicationConcept for Safe Interaction of Driverless Industrial Trucks and Humans in Shared Areas( 2022-06-17)
; ; ; ;Ishigooka, Tasuku ;Otsuka, SatoshiMizuochi, MarikoHumans still need to access the same area as automated systems, like in warehouses, if full automation is not feasible or economical. In such shared areas, critical interactions are inevitable. The automation of vehicles is usually tied to an argument on improved safety. However, current standards still rely also on the awareness of humans to avoid collisions. Along with this, modern intelligent warehouses are equipped with additional sensors that can help to automate safety. Blind corners, where the view is obscured, are particularly critical and, moreover, their location can change when goods are moved. Therefor, we generalize a concept for safe interactions at known blind corners to movements in the entire warehouse. We propose an architecture that uses infrastructure sensors to prevent human-robot collisions with respect to automated forklifts as instances of driverless industrial trucks. This includes a safety critical function using wireless communication, which sporadically might be unavailable or disturbed. Therefore, the proposed architecture is able to mitigate these faults and gracefully degrades the system’s performance if required. Within our extensive evaluation, we simulate varying warehouse settings to verify our approach and to estimate the impact on an automated forklift’s performance.
PublicationSafety Implications of Runtime Adaptation to Changing Operating Conditions( 2022)
; ; ; ;Oboril, FabianBuerkle, CorneliusWith further advancements of autonomous driving, also larger application scenarios will be addressed, so-called Operational Design Domains (ODDs). Autonomous vehicles will likely experience varying operating conditions in such broader ODDs. The implications of changing operating conditions on safety and required adaptation is, however, an open challenge. In our work, we exemplary investigate a vehicle following scenario passing through altering operating conditions and Responsibility Sensitive Safety (RSS) as formal model to define appropriate longitudinal following distances. We provide a deeper analysis of the influence of switching the safety model parameter values to adapt to new operating conditions. As our findings show that hard switches of operating conditions can lead to critical situations, we propose an approach for continuously adapting safety model parameters allowing for a safe and more comfortable transition. In our evaluation, we utilize driving simulations to compare the hard switching of parameters with our proposal of gradual adaptation. Our results highlight the implications of changing operating conditions on the driving safety. Moreover, we provide a solution to adapt the safety model parameters of an autonomous vehicle in such a way that safety model violations during transition can be avoided.
PublicationFramework for Data and AI Lifecycle. Research Project REMORAThere is a lot of potential for Artificial Intelligence (AI) in the industrial domain to improve services and production. The Fraunhofer IKS is developing a framework for the data and AI life cycle to support every life cycle stage from AI development through data processing to data analysis. It aims at meeting the challenges of deploying AI in the industrial domain in order to enable continuous, automated, and dynamic AI applications.
PublicationFramework for Data and AI Life Cycle. Research Project REMORAIn the Industrie 4.0, an increasing amount of data is generated through intelligent interconnection of machines and processes. This data can be leveraged to generate knowledge - through Artificial Intelligence (AI) - to improve production and services. However, it is not sufficient to simply integrate AI. A continuous data and AI life cycle needs to be secured. The single life cycle stages (from data acquisition to AI development to data analysis) need to be executed flexibly and (semi-) automatically. The Fraunhofer IKS is developing a framework to enable and facilitate the flexible and continuous operation of AI in the Industrie 4.0. The aim is to support and automate AI development, integration, and operation, while reducing the effort for the user.
PublicationImplementing a Metadata Manager for Machine Learning with the Asset Administration Shell( 2022)
;Sawczuk da Silva, Alexandre ;With the rise of Industry 4.0, businesses are increasingly turning to Machine Learning to leverage data for improving quality and productivity. However, one open challenge when embracing Machine Learning in this context is the integration of cloud infrastructures, as well as the heterogeneity of data, interfaces, and protocols in the production environment. To address this, we are developing a framework that aims to simplify the adoption of Machine Learning techniques for heterogeneous industrial automation systems. One of the core features of this framework is the ability to handle data about production devices -- a scenario that is naturally suited to the use of Asset Administration Shells. However, the implementation of a system that uses Asset Administration Shells comes with its own set of challenges, such as the abstraction of details from users and the representation of device topologies. Thus, this paper introduces the concepts and implementation of a Metadata Manager component in the aforementioned framework that uses Asset Administration Shells as its basis. We further examine the Metadata Manager's current structure with unit testing, derive planned extensions, and discuss future directions from the Industry 4.0 perspective.
PublicationEnabling Cognitive Manufacturing in Heterogeneous Industrial Automation SystemsWith the increased digitization in the manufacturing sector, cognitive computing entails great potential to improve services and production. This is also referred to as cognitive manufacturing. The general idea is to simulate human cognitive processes - with the aim to improve decision making - by using machine learning (ML) to leverage the increased amount of data. However, the seamless adoption of cognitive computing and ML techniques to industrial automation systems on all abstraction levels is currently impeded by different challenges. Prominent blocking points are the heterogeneity of the systems, which impedes uniform data access and ML integration, and the lack of support for managing various ML life cycle phases. In this work, we propose a framework to manage data and ML life cycles in industrial automation systems. The framework comprises an architecture for the flexible integration of ML components (from component to cloud level) and their adaptive management (including retraining and updates). We address three phases of ML explicitly: pre-deployment, deployment, and post-deployment. We present first results and experiences of applying the framework to an industrial use case and discuss its future potential towards enabling cognitive manufacturing.
PublicationEnhanced System Awareness as Basis for Resilience of Autonomous Vehicles( 2021)
; ; ; ;The transition to autonomous driving and increasing automation of cars requires these systems to take correct decisions in very complex situations. For this, the understanding of a vehicle system's own capabilities and the environmental context is crucial. We introduce our approach of enhancing the system awareness of vehicles to handle changes gracefully, while optimizing the overall performance. Based on a system health management the available capabilities of the distributed vehicle system can be determined. By taking into account the environment in the form of so-called operational domains at run-time, self- and context-awareness can be established providing a situation picture to which the system can adapt. We developed a service contract based solution to trigger degradations or find optimal configurations, while not endangering safety goals. Our approach is evaluated in an intersection scenario, where we can highlight the advantages of enhanced system awareness to optimize an autonomous vehicles performance.
PublicationDevOps for Developing Cyber-Physical Systems(Fraunhofer IKS, 2021)
; ; ;Rothe, Johannes ;Tenorth, MoritzIn the age of digitalization, the success or failure of a product depends on bug-free and feature-rich software. Driven by consumer expectations and competition between vendors, software can no longer be delivered as-is but needs to be continuously supported and updated for a period of time. In large and complex projects, this can be a challenging task, which many IT companies are approaching with the state-of-the-art software development process DevOps. For companies manufacturing high-tech products, software is also becoming ever more critical, and companies are struggling with handling the complexity of long-term software support. The adoption of modern development processes such as DevOps is challenging, as the real-world environment in which the systems operate induces challenges and requirements that are unique to each product and company. Once they are addressed, however, DevOps has the potential to deliver more sophisticated products with minimal software errors, thus increasing the value provided to customers and giving the company a considerable competitive advantage.
PublicationDependable and Efficient Cloud-Based Safety-Critical Applications by Example of Automated Valet Parking( 2021)
; ;Shekhada, Dhavalkumar ; ; ;Ishigooka, Tasuku ;Otsuka, SatoshiMizuochi, MarikoFuture embedded systems and services will be seamlessly connected and will interact on all levels with the infrastructure and cloud. For safety-critical applications this means that it is not sufficient to ensure dependability in a single embedded system, but it is necessary to cover the complete service chain including all involved embedded systems as well as involved services running in the edge or the cloud. However, for the development of such Cyber-Physical Systems-of-Systems (CPSoS) engineers must consider all kinds of dependability requirements. For example, it is not an option to ensure safety by impeding reliability or availability requirements. In fact, it is the engineers' task to optimize the CPSoS' performance without violating any safety goals. In this paper, we identify the main challenges of developing CPSoS based on several industrial use cases and present our novel approach for designing cloud-based safety-critical applications with optimized performance by the example of an automated valet parking system. The evaluation shows that our monitoring and recovery solution ensures a superior performance in comparison to current methods, while meeting the system's safety demands in case of connectivity-related faults.