Advantageous Usage of Textual Domain-Specific Languages for Scenario-Driven Development of Automated Driving Functions

Bock, Florian; Sippl, Christoph; Heinzz, Aaron; Lauerz, Christoph; German, Reinhard

doi:10.1109/syscon.2019.8836912

Cited by 25 publications

(15 citation statements)

References 13 publications

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“…This presents an interface for diverse service types in a specific domain, and facilitates converting, transforming and combining data from multiple services to meet the operations needs and satisfy both business and technical requirements. Different user interfaces validation techniques from various domains can be considered to evaluate this module, which can automatically validate knowledge from heterogeneous systems [104].…”

Section: Swe Handlermentioning

confidence: 99%

The Internet of Things and Architectures of Big Data Analytics: Challenges of Intersection at Different Domains

2022

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The current exponential advancements in the Internet of Things (IoT) technologies pave a vast intelligent computing platform by integrating smart objects with sensing, processing and communication capabilities. The core element of IoT is the complex big data generated from different interconnected sources at real-time, presenting divergent processing and analysis challenges. Best practices in software engineering have been continuously addressed in IoT technologies to handle such big data efficiently at different domains. Despite of the massive studies dedicated for IoT, no explicit processing architecture is proposed based on real investigation of software engineering concepts and big data analytics characteristics in IoT. This paper provides a systematic literature review for the current state-of-the-art of IoT systems in different domains. The study investigates the current techniques and technologies that serve IoT systems from the big data analytics and software engineering perspectives, revealing a matrix for the specific IoT data features and their encountered challenges and gaps for each domain. The review deduces a proposed domain-independent software architecture for big IoT data analytics, maintaining various IoT data processing challenges, including data scalability, timeliness, heterogeneity, inconsistency, confidentiality and correlations. Finally, the main research gaps are emphasized for future considerations.

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Section: Swe Handlermentioning

confidence: 99%

The Internet of Things and Architectures of Big Data Analytics: Challenges of Intersection at Different Domains

2022

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“…• divide the simulation quality into three main categories that may occur during scenario-based testing and simulation: the quality regarding the simulation model or tool, the quality of a vehicle or components of it, and the quality of scenarios for testing, and V-model with different X-in-the-loop phases [10] combined with abstraction levels of scenarios at different development stages, adapted from Bock et al [11].…”

Section: Novelty and Main Contribution To The State Of The Artmentioning

confidence: 99%

“…The main goal for this level is to create scenarios easily understandable and open for discussion. Bock et al [11] propose a supplementary abstraction level, called abstract scenarios, defined by a controlled natural language format that is machine-interpretable and an exemplary assignment of scenario abstraction levels to the V-model is shown in Fig. 1.…”

Section: A Simulation-and Scenario-based Testingmentioning

confidence: 99%

A Taxonomy for Quality in Simulation-Based Development and Testing of Automated Driving Systems

et al. 2022

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Ensuring the quality of automated driving systems is a major challenge the automotive industry is facing. In this context, quality defines the degree to which an object meets expectations and requirements. Especially, highly automated and automated vehicles at SAE level 4 and 5 will be expected to operate safely in various contexts and complex situations without misconduct. Thus, a systematic approach is needed to show their safe operation. A way to address this challenge is simulation-based testing as pure physical testing is not feasible for automated driving at level 4 and 5 since several billion kilometers of driving are necessary. During simulation-based testing, the data used to evaluate the actual quality of an automated driving system are generated using a simulation. However, to rely on these simulation data, the overall simulation, which also includes its simulation models, must provide a certain quality level. This quality level depends on the intended purpose for which the generated simulation data should be used. Therefore, three categories of quality can be considered: simulation quality (e.g., reliable simulation tool), quality of the automated driving system (e.g., handling of dangerous situations), and scenario quality (e.g., scenario criticality). Quality must be determined and evaluated in various process steps in developing and testing automated driving systems, the overall simulation, and the simulation models used for the simulation. In this paper, we propose a conceptual taxonomy to serve a better understanding of quality in the development and testing process to have a clear separation and insight where further testing is neededboth in terms of automated driving systems and simulation, including their simulation models and scenarios used for testing.

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“…In the field of ADSs, textual scenario descriptions or a combination of textual and graphical descriptions are used across different abstraction levels. When the two formats are used in combination, the textual description tends to be incomplete [8]. In addition, The quality, expressions, and semantics in a textual scenario description are highly dependent on the individual creator, ambiguities and misunderstandings often exist [25].…”

Section: Related Workmentioning

confidence: 99%

“…ADASs and ADSs are typically developed based on a process derived from the V-model, which represents a system development lifecycle that consists several development steps and their corresponding testing counterparts [8]. The development steps form one side of the 'V' shape, which starts from high level requirements to systems development and further to low level module development.…”

Section: Introductionmentioning

confidence: 99%

Scenario Description Language for Automated Driving Systems: A Two Level Abstraction Approach

Khastgir

Jennings

2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Advantageous Usage of Textual Domain-Specific Languages for Scenario-Driven Development of Automated Driving Functions

Cited by 25 publications

References 13 publications

The Internet of Things and Architectures of Big Data Analytics: Challenges of Intersection at Different Domains

The Internet of Things and Architectures of Big Data Analytics: Challenges of Intersection at Different Domains

A Taxonomy for Quality in Simulation-Based Development and Testing of Automated Driving Systems

Scenario Description Language for Automated Driving Systems: A Two Level Abstraction Approach

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