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of 367
pro vyhledávání: '"Lauer, Martin"'
This paper introduces an extension to the arbitration graph framework designed to enhance the safety and robustness of autonomous systems in complex, dynamic environments. Building on the flexibility and scalability of arbitration graphs, the propose
Externí odkaz:
http://arxiv.org/abs/2411.10170
Object detectors encounter challenges in handling domain shifts. Cutting-edge domain adaptive object detection methods use the teacher-student framework and domain adversarial learning to generate domain-invariant pseudo-labels for self-training. How
Externí odkaz:
http://arxiv.org/abs/2408.09431
Autor:
Eisemann, Leon, Fehling-Kaschek, Mirjam, Gommel, Henrik, Hermann, David, Klemp, Marvin, Lauer, Martin, Lickert, Benjamin, Luettner, Florian, Moss, Robin, Neis, Nicole, Pohle, Maria, Romanski, Simon, Stadler, Daniel, Stolz, Alexander, Ziehn, Jens, Zhou, Jingxing
With growing complexity and criticality of automated driving functions in road traffic and their operational design domains (ODD), there is increasing demand for covering significant proportions of development, validation, and verification in virtual
Externí odkaz:
http://arxiv.org/abs/2405.01776
Existing object detectors encounter challenges in handling domain shifts between training and real-world data, particularly under poor visibility conditions like fog and night. Cutting-edge cross-domain object detection methods use teacher-student fr
Externí odkaz:
http://arxiv.org/abs/2403.15786
Validating robotic systems in safety-critical appli-cations requires testing in many scenarios including rare edgecases that are unlikely to occur, requiring to complement real-world testing with testing in simulation. Generative models canbe used to
Externí odkaz:
http://arxiv.org/abs/2403.11728
In order to protect vulnerable road users (VRUs), such as pedestrians or cyclists, it is essential that intelligent transportation systems (ITS) accurately identify them. Therefore, datasets used to train perception models of ITS must contain a signi
Externí odkaz:
http://arxiv.org/abs/2302.08931
Autor:
Danek, Adrian, Diehl-Schmid, Janine, Jahn, Holger, Kassubek, Jan, Kornhuber, Johannes, Landwehrmeyer, Bernhard, Lauer, Martin, Prudlo, Johannes, Schneider, Anja, Ludolph, Albert C., Fliesbach, Klaus, Anderl-Straub, Sarah, Brüggen, Katharina, Fischer, Marie, Förstl, Hans, Hammer, Anke, Homola, György, Just, Walter, Levin, Johannes, Marroquin, Nicolai, Marschhauser, Anke, Pino, Danielé, Nagl, Magdalena, Oberstein, Timo, Hüper, Lea, Polyakova, Maryna, Pellkofer, Hannah, Richter-Schmidinger, Tanja, Rossmeier, Carola, Kulko, Marianna, Semler, Elisa, Spottke, Annika, Steinacker, Petra, Thöne-Otto, Angelika, Uttner, Ingo, Zech, Heike, Albrecht, Franziska, Mueller, Karsten, Ballarini, Tommaso, Fassbender, Klaus, Wiltfang, Jens, Otto, Markus, Jech, Robert, Schroeter, Mattias L.
Publikováno v:
In Heliyon 15 August 2024 10(15)
Reinforcement learning (RL) has recently been used for solving challenging decision-making problems in the context of automated driving. However, one of the main drawbacks of the presented RL-based policies is the lack of safety guarantees, since the
Externí odkaz:
http://arxiv.org/abs/2107.07413
Decision making under uncertainty can be framed as a partially observable Markov decision process (POMDP). Finding exact solutions of POMDPs is generally computationally intractable, but the solution can be approximated by sampling-based approaches.
Externí odkaz:
http://arxiv.org/abs/2106.04206
Estimating the current scene and understanding the potential maneuvers are essential capabilities of automated vehicles. Most approaches rely heavily on the correctness of maps, but neglect the possibility of outdated information. We present an appro
Externí odkaz:
http://arxiv.org/abs/2007.06904