Issue
DARTS, Volume 11, Issue 1
Special Issue of the 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)
-
Part of:
Volume:
DARTS, Volume 11 (ECRTS 2025)
Conference: Euromicro Conference on Real-Time Systems (ECRTS)
Journal: Dagstuhl Artifacts Series (DARTS)
Editors
Publication Details
- published at: 2025-07-07
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- DBLP: db/journals/darts/darts11
Access Numbers
- Detailed Access Statistics available here
-
Total Document Accesses (updated on a weekly basis):
0PDF Downloads
Documents
No documents found matching your filter selection.
Document
Front Matter
Front Matter, Table of Contents, Artifact Evaluation Process, Artifact Evaluation Committee
Abstract
Front Matter, Table of Contents, Artifact Evaluation Process, Artifact Evaluation Committee
Cite as
Special Issue of the 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Dagstuhl Artifacts Series (DARTS), Volume 11, Issue 1, pp. 0:i-0:x, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{nemitz_et_al:DARTS.11.1.0,
author = {Nemitz, Catherine E. and Ward, Bryan C.},
title = {{Front Matter, Table of Contents, Artifact Evaluation Process, Artifact Evaluation Committee}},
pages = {0:i--0:x},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2025},
volume = {11},
number = {1},
editor = {Nemitz, Catherine E. and Ward, Bryan C.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.11.1.0},
URN = {urn:nbn:de:0030-drops-237736},
doi = {10.4230/DARTS.11.1.0},
annote = {Keywords: Front Matter, Table of Contents, Artifact Evaluation Process, Artifact Evaluation Committee}
}
Document
Artifact
Sensor Fusion Desynchronization Attacks (Artifact)
Abstract
Environmental perception and 3D object detection are key factors for advancing autonomous driving and require robust security measures to ensure optimal performance and safety. However, established methods often focus only on protecting the involved data and overlook synchronization and timing aspects, which are equally crucial for ensuring profound system security. For instance, multi-modal sensor fusion techniques for object detection can be affected by input desynchronization resulting from random communication delays or malicious cyber attacks, as these techniques combine various sensor inputs to extract shared features present in their data streams simultaneously. Current research acknowledges the importance of temporal alignment in this context. However, the presented studies typically assume genuine system behavior and neglect the potential threat of malicious attacks, as the suggested solutions lack strategies to prevent intentional data misalignment. Additionally, they do not adequately address how sensor input desynchronization affects fusion performance in depth. This paper investigates how desynchronization attacks impact sensor fusion algorithms for 3D object detection. We evaluate how varying sensor delays affect the detection performance and link our findings to the internal architecture of the sensor fusion algorithms and the influence of specific traffic scenarios and their dynamics. We compiled four datasets covering typical traffic scenarios for our empirical evaluation and tested them on four representative fusion algorithms. Our results show that all evaluated algorithms are vulnerable to input desynchronization, as the performance declines with increasing sensor delays, highlighting the existing lack of resilience to desynchronization attacks. Furthermore, we observe that the Light Detection and Ranging (LiDAR) sensor is significantly more susceptible to delays than the camera. Finally, our experiments indicate that the chosen fusion architecture correlates with the system’s resilience against desynchronization, as our results demonstrate that the early fusion approach provides greater robustness than others.
Cite as
Andreas Finkenzeller, Andrew Roberts, Mauro Bellone, Olaf Maennel, Mohammad Hamad, and Sebastian Steinhorst. Sensor Fusion Desynchronization Attacks (Artifact). In Special Issue of the 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Dagstuhl Artifacts Series (DARTS), Volume 11, Issue 1, pp. 1:1-1:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{finkenzeller_et_al:DARTS.11.1.1,
author = {Finkenzeller, Andreas and Roberts, Andrew and Bellone, Mauro and Maennel, Olaf and Hamad, Mohammad and Steinhorst, Sebastian},
title = {{Sensor Fusion Desynchronization Attacks (Artifact)}},
pages = {1:1--1:3},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2025},
volume = {11},
number = {1},
editor = {Finkenzeller, Andreas and Roberts, Andrew and Bellone, Mauro and Maennel, Olaf and Hamad, Mohammad and Steinhorst, Sebastian},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.11.1.1},
URN = {urn:nbn:de:0030-drops-236029},
doi = {10.4230/DARTS.11.1.1},
annote = {Keywords: Security, Time Synchronization, Sensor Fusion, Autonomous Driving, Delay Attack}
}
Document
Artifact
A First Look at ROS 2 Applications Written in Asynchronous Rust (Artifact)
Abstract
Deterministic real-time performance is critical for robotic applications, many of which are developed in C++ using the ROS 2 framework. Recently, Rust has emerged as a compelling alternative to C++ in various domains, including robotics. This study explores whether ROS 2 applications written in Rust can achieve real-time performance comparable to their C++ counterparts. We focus on the R2R library, which provides Rust bindings for ROS 2 with a modern asynchronous programming interface.
This artifact includes source code for several ROS 2 nodes and supporting Python scripts. The nodes implement simple publishers or subscribers and vary in programming language (Rust, C++), Rust asynchronous runtime (futures, Tokio), and callback-to-OS-thread mapping with different thread priorities. The nodes are run with tracing enabled and by post-processing the traces we obtain statistics about end-to-end latencies. Our results demonstrate that specific configurations enable Rust R2R applications to match the real-time performance of C++ applications.
Running the artifact as is does not require any ROS 2 or Rust knowledge. Following our README and running mentioned shell commands should be sufficient. However to verify that the commands do what is promised, some familiarity with ROS 2 workspace structure and Rust’s build tool cargo would be beneficial.
Cite as
Martin Škoudlil and Michal Sojka. A First Look at ROS 2 Applications Written in Asynchronous Rust (Artifact). In Special Issue of the 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Dagstuhl Artifacts Series (DARTS), Volume 11, Issue 1, pp. 2:1-2:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{skoudlil_et_al:DARTS.11.1.2,
author = {\v{S}koudlil, Martin and Sojka, Michal},
title = {{A First Look at ROS 2 Applications Written in Asynchronous Rust (Artifact)}},
pages = {2:1--2:2},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2025},
volume = {11},
number = {1},
editor = {\v{S}koudlil, Martin and Sojka, Michal},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.11.1.2},
URN = {urn:nbn:de:0030-drops-236038},
doi = {10.4230/DARTS.11.1.2},
annote = {Keywords: ROS, Rust, Real-time, Response time}
}
Document
Artifact
Bounding the WCET of a GPU Thread Block with a Multi-Phase Representation of Warps Execution (Artifact)
Abstract
This paper proposes to model the Worst-Case Execution Time (WCET) of a GPU thread block as the Worst-Case Response Time (WCRT) of the warps composing the block. Inspired by the WCRT analyzes for classical CPU tasks, the response time of a warp is modeled as its execution time in isolation added to an interference term that accounts for the execution of higher priority warps. We provide an algorithm to build a representation of the execution of each warp of a thread block that distinguishes phases of execution on the functional units and phases of idleness due to operations latency. A simple formula relying on this model is then proposed to safely upper bound the WCRT of warps scheduled under greedy policies such as Greedy-Then-Oldest (GTO) or Loose Round-Robin (LRR). We experimented our approach using simulations of kernels from a GPU benchmark suite on the Accel-Sim simulator. We also evaluated the model on a GPU program that is likely to be found in safety critical systems : SGEMM (Single-precision GEneral Matrix Multiplication). This work constitutes a promising first building block of an analysis pipeline for enabling static WCET computation on GPUs.
Cite as
Louison Jeanmougin, Thomas Carle, and Christine Rochange. Bounding the WCET of a GPU Thread Block with a Multi-Phase Representation of Warps Execution (Artifact). In Special Issue of the 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Dagstuhl Artifacts Series (DARTS), Volume 11, Issue 1, pp. 3:1-3:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{jeanmougin_et_al:DARTS.11.1.3,
author = {Jeanmougin, Louison and Carle, Thomas and Rochange, Christine},
title = {{Bounding the WCET of a GPU Thread Block with a Multi-Phase Representation of Warps Execution (Artifact)}},
pages = {3:1--3:5},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2025},
volume = {11},
number = {1},
editor = {Jeanmougin, Louison and Carle, Thomas and Rochange, Christine},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.11.1.3},
URN = {urn:nbn:de:0030-drops-236047},
doi = {10.4230/DARTS.11.1.3},
annote = {Keywords: GPU, WCET analysis}
}
Document
Artifact
Faster Classification of Time-Series Input Streams (Artifact)
Abstract
Deep learning–based classifiers are widely used for perception in autonomous Cyber-Physical Systems (CPS’s). However, such classifiers rarely offer guarantees of perfect accuracy while being optimized for efficiency. To support safety-critical perception, ensembles of multiple different classifiers working in concert are typically used. Since CPS’s interact with the physical world continuously, it is not unreasonable to expect dependencies among successive inputs in a stream of sensor data. Prior work introduced a classification technique that leverages these inter-input dependencies to reduce the average time to successful classification using classifier ensembles. In this paper, we propose generalizations to this classification technique, both in the improved generation of classifier cascades and the modeling of temporal dependencies. We demonstrate, through theoretical analysis and numerical evaluation, that our approach achieves further reductions in average classification latency compared to the prior methods.
Cite as
Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao. Faster Classification of Time-Series Input Streams (Artifact). In Special Issue of the 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Dagstuhl Artifacts Series (DARTS), Volume 11, Issue 1, pp. 4:1-4:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
Copy BibTex To Clipboard
@Article{agrawal_et_al:DARTS.11.1.4,
author = {Agrawal, Kunal and Baruah, Sanjoy and Guo, Zhishan and Li, Jing and Reghenzani, Federico and Yang, Kecheng and Zhao, Jinhao},
title = {{Faster Classification of Time-Series Input Streams (Artifact)}},
pages = {4:1--4:3},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2025},
volume = {11},
number = {1},
editor = {Agrawal, Kunal and Baruah, Sanjoy and Guo, Zhishan and Li, Jing and Reghenzani, Federico and Yang, Kecheng and Zhao, Jinhao},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.11.1.4},
URN = {urn:nbn:de:0030-drops-236057},
doi = {10.4230/DARTS.11.1.4},
annote = {Keywords: Classification, Deep Learning, Sensor data streams, IDK classifiers}
}