RTLola on Board: Testing Real Driving Emissions on your Phone

Biewer, Sebastian; Finkbeiner, Bernd; Hermanns, Holger; Köhl, Maximilian A.; Schnitzer, Yannik; Schwenger, Maximilian

doi:10.1007/978-3-030-72013-1_20

“…Our practical experience with the approach of the paper has been very positive. We have used the approach to visualize stream-based monitoring data from recent case studies that use RTLola for UAS [2,3] and other cyber-physical systems [4,5,11]. The visual tools provided by cgv have proven very useful for the type of data produced by our monitors.…”

Section: Discussionmentioning

confidence: 99%

“…This paper shows that stream-based monitoring languages like RTLola are very well suited to carry out the needed data processing for useful visualizations. Our focus on RTLola is motivated by recent work on RTLola-based monitoring for UAS [2,3] and other cyber-physical systems [4,5,11]. However, the approach of the paper is clearly transferrable to other monitoring tools for CPS [22][23][24].…”

Section: Related Workmentioning

confidence: 99%

Real-time Visualization of Stream-based Monitoring Data

Baumeister¹,

Finkbeiner²,

Gumhold³

et al. 2022

Preprint

Self Cite

0

View full text Add to dashboard Cite

Stream-based runtime monitors are used in safety-critical applications such as Unmanned Aerial Systems (UAS) to compute comprehensive statistics and logical assessments of system health that provide the human operator with critical information in hand-over situations. In such applications, a visual display of the monitoring data can be much more helpful than the textual alerts provided by a more traditional user interface. This visualization requires extensive real-time data processing, which includes the synchronization of data from different streams, filtering and aggregation, and priorization and management of user attention. We present a visualization approach for the RTLola monitoring framework. Our approach is based on the principle that the necessary data processing is the responsibility of the monitor itself, rather than the responsibility of some external visualization tool. We show how the various aspects of the data transformation can be described as RTLola stream equations and linked to the visualization component through a bidirectional synchronous interface. In our experience, this approach leads to highly informative visualizations as well as to understandable and easily maintainable monitoring code.

show abstract

On the Detection of Doped Software by Falsification

Biewer

¹

,

Hermanns

²

2022

Fundamental Approaches to Software Engineering

3

0

View full text Add to dashboard Cite

Software doping is a phenomenon that refers to the presence of hidden software functionality, whose existence is only in the interest of the manufacturer. The most prominent example is the diesel emissions scandal. There is a need for methods that identify software doping, and such methods are bound to be applied to the final product with no or rare knowledge about its internals. Black-box analysis techniques have recently been developed for this purpose, harvesting the formal foundations of software doping. This paper integrates them with established falsification techniques for the purpose of real-world applicability. With a focus on the diesel scandal and emissions tests on chassis dynamometers we make the testing procedures significantly more effective in terms of time and cost. The theoretical results are implemented in a prototypical doping tester.

show abstract

Real-Time Visualization of Stream-Based Monitoring Data

Baumeister

¹

,

Finkbeiner

²

,

Gumhold

³

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Stream-based runtime monitors are used in safety-critical applications such as Unmanned Aerial Systems (UAS) to compute comprehensive statistics and logical assessments of system health that provide the human operator with critical information in hand-over situations. In such applications, a visual display of the monitoring data can be much more helpful than the textual alerts provided by a more traditional user interface. This visualization requires extensive real-time data processing, which includes the synchronization of data from different streams, filtering and aggregation, and priorization and management of user attention. We present a visualization approach for the RTLola monitoring framework. Our approach is based on the principle that the necessary data processing is the responsibility of the monitor itself, rather than the responsibility of some external visualization tool. We show how the various aspects of the data transformation can be described as RTLola stream equations and linked to the visualization component through a bidirectional synchronous interface. In our experience, this approach leads to highly informative visualizations as well as to understandable and easily maintainable monitoring code.

show abstract

RTLola on Board: Testing Real Driving Emissions on your Phone

Cited by 9 publications

References 11 publications

Real-time Visualization of Stream-based Monitoring Data

Real-time Visualization of Stream-based Monitoring Data

On the Detection of Doped Software by Falsification

Real-Time Visualization of Stream-Based Monitoring Data

Contact Info

Product

Resources

About