no code implementations • 23 Apr 2024 • Tobias Ladner, Michael Eichelbeck, Matthias Althoff
Graph neural networks are becoming increasingly popular in the field of machine learning due to their unique ability to process data structured in graphs.
no code implementations • 13 Feb 2024 • Hanna Krasowski, Matthias Althoff
We introduce an efficient verification approach that determines the compliance of actions with respect to the COLREGS formalized using temporal logic.
no code implementations • 26 Jan 2024 • Lukas Koller, Tobias Ladner, Matthias Althoff
Neural networks are vulnerable to adversarial attacks, i. e., small input perturbations can significantly affect the outputs of a neural network.
no code implementations • 13 Dec 2023 • Sebastian Mair, Matthias Althoff
Cooperative Adaptive Cruise Control (CACC) is a well-studied technology for forming string-stable vehicle platoons.
no code implementations • 21 Sep 2023 • Laura Lützow, Matthias Althoff
The first approach we propose can only be used with dependency-preserving set representations such as symbolic zonotopes, while the second one is valid for arbitrary set representations but relies on a reformulation of the ARMAX model.
no code implementations • 15 Sep 2023 • Jonathan Külz, Matthias Althoff
Industrial robots are designed as general-purpose hardware with limited ability to adapt to changing task requirements or environments.
no code implementations • 27 May 2023 • Di Liu, Sebastian Mair, Kang Yang, Simone Baldi, Paolo Frasca, Matthias Althoff
We show that self-organization promotes resilience to acceleration limits and communication failures, i. e., homogenizing to a common group behavior makes the platoon recover from these causes of impairments.
no code implementations • 3 May 2023 • Tobias Ladner, Matthias Althoff
Our evaluation shows that our approach can reduce the number of neurons to a fraction of the original number of neurons with minor outer-approximation and thus reduce the verification time to a similar degree.
no code implementations • 7 Mar 2023 • Eivind Meyer, Lars Frederik Peiss, Matthias Althoff
Manually specifying features that capture the diversity in traffic environments is impractical.
no code implementations • 2 Feb 2023 • Eivind Meyer, Maurice Brenner, BoWen Zhang, Max Schickert, Bilal Musani, Matthias Althoff
Heterogeneous graphs offer powerful data representations for traffic, given their ability to model the complex interaction effects among a varying number of traffic participants and the underlying road infrastructure.
no code implementations • 19 Oct 2022 • Niklas Kochdumper, Hanna Krasowski, Xiao Wang, Stanley Bak, Matthias Althoff
While reinforcement learning produces very promising results for many applications, its main disadvantage is the lack of safety guarantees, which prevents its use in safety-critical systems.
no code implementations • 16 Sep 2022 • Yuanfei Lin, Haoxuan Li, Matthias Althoff
We evaluate our approach for the use case of autonomous driving with predicates used in formalized traffic rules on a recorded dataset, which highlights the advantage of our approach compared to traditional approaches in terms of precision.
no code implementations • 6 Jul 2022 • Niklas Kochdumper, Christian Schilling, Matthias Althoff, Stanley Bak
We present a novel approach to efficiently compute tight non-convex enclosures of the image through neural networks with ReLU, sigmoid, or hyperbolic tangent activation functions.
no code implementations • 13 May 2022 • Hanna Krasowski, Jakob Thumm, Marlon Müller, Lukas Schäfer, Xiao Wang, Matthias Althoff
We categorize the methods based on how they adapt the action: action replacement, action projection, and action masking.
no code implementations • 12 May 2022 • Jakob Thumm, Matthias Althoff
Deep reinforcement learning (RL) has shown promising results in the motion planning of manipulators.
no code implementations • 12 May 2022 • Michael Eichelbeck, Hannah Markgraf, Matthias Althoff
The contingency constraint is computed using set-based backwards reachability analysis and actions of the RL agent are verified through a safety layer.
1 code implementation • 19 Oct 2020 • Amr Alanwar, Victor Gassmann, Xingkang He, Hazem Said, Henrik Sandberg, Karl Henrik Johansson, Matthias Althoff
The set-based estimation has gained a lot of attention due to its ability to guarantee state enclosures for safety-critical systems.
Cryptography and Security Robotics
no code implementations • 1 Jul 2020 • Xiao Wang, Saasha Nair, Matthias Althoff
Robust adversarial RL (RARL) was previously proposed to train an adversarial network that applies disturbances to a system, which improves the robustness in test scenarios.
no code implementations • 7 Jun 2020 • Cees F. Verdier, Niklas Kochdumper, Matthias Althoff, Manuel Mazo Jr
Subsequently, the best candidate is verified using reachability analysis; if the candidate solution does not satisfy the specification, an initial condition violating the specification is extracted as a counterexample.
no code implementations • 26 Mar 2020 • Fanta Camara, Nicola Bellotto, Serhan Cosar, Florian Weber, Dimitris Nathanael, Matthias Althoff, Jingyuan Wu, Johannes Ruenz, André Dietrich, Gustav Markkula, Anna Schieben, Fabio Tango, Natasha Merat, Charles W. Fox
Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets.
2 code implementations • 23 Mar 2020 • Amr Alanwar, Jagat Jyoti Rath, Hazem Said, Karl Henrik Johansson, Matthias Althoff
Both algorithms utilize a set-based diffusion step, which decreases the estimation errors and the size of estimated sets, and can be seen as a lightweight approach to achieve partial consensus between the distributed estimated sets.
no code implementations • 26 Feb 2020 • Fanta Camara, Nicola Bellotto, Serhan Cosar, Dimitris Nathanael, Matthias Althoff, Jingyuan Wu, Johannes Ruenz, André Dietrich, Charles W. Fox
Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets.
1 code implementation • 1 Nov 2017 • Amr Alanwar, Hazem Said, Ankur Mehta, Matthias Althoff
Distributed state estimation strongly depends on collaborative signal processing, which often requires excessive communication and computation to be executed on resource-constrained sensor nodes.
Systems and Control Robotics Signal Processing