@article{SchulteTiggesFoersterNikolovskietal.2022, author = {Schulte-Tigges, Joschua and F{\"o}rster, Marco and Nikolovski, Gjorgji and Reke, Michael and Ferrein, Alexander and Kaszner, Daniel and Matheis, Dominik and Walter, Thomas}, title = {Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {19}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s22197146}, pages = {20 Seiten}, year = {2022}, abstract = {Abstract In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars.}, language = {en} } @article{CollPeralesSchulteTiggesRondinoneetal.2022, author = {Coll-Perales, Baldomero and Schulte-Tigges, Joschua and Rondinone, Michele and Gozalvez, Javier and Reke, Michael and Matheis, Dominik and Walter, Thomas}, title = {Prototyping and evaluation of infrastructure-assisted transition of control for cooperative automated vehicles}, series = {IEEE Transactions on Intelligent Transportation Systems}, volume = {23}, journal = {IEEE Transactions on Intelligent Transportation Systems}, number = {7}, publisher = {IEEE}, issn = {1524-9050 (Print)}, doi = {10.1109/TITS.2021.3061085}, pages = {6720 -- 6736}, year = {2022}, abstract = {Automated driving is now possible in diverse road and traffic conditions. However, there are still situations that automated vehicles cannot handle safely and efficiently. In this case, a Transition of Control (ToC) is necessary so that the driver takes control of the driving. Executing a ToC requires the driver to get full situation awareness of the driving environment. If the driver fails to get back the control in a limited time, a Minimum Risk Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g., decelerating to full stop). The execution of ToCs requires some time and can cause traffic disruption and safety risks that increase if several vehicles execute ToCs/MRMs at similar times and in the same area. This study proposes to use novel C-ITS traffic management measures where the infrastructure exploits V2X communications to assist Connected and Automated Vehicles (CAVs) in the execution of ToCs. The infrastructure can suggest a spatial distribution of ToCs, and inform vehicles of the locations where they could execute a safe stop in case of MRM. This paper reports the first field operational tests that validate the feasibility and quantify the benefits of the proposed infrastructure-assisted ToC and MRM management. The paper also presents the CAV and roadside infrastructure prototypes implemented and used in the trials. The conducted field trials demonstrate that infrastructure-assisted traffic management solutions can reduce safety risks and traffic disruptions.}, language = {en} } @inproceedings{NikolovskiLimpertNessauetal.2023, author = {Nikolovski, Gjorgji and Limpert, Nicolas and Nessau, Hendrik and Reke, Michael and Ferrein, Alexander}, title = {Model-predictive control with parallelised optimisation for the navigation of autonomous mining vehicles}, series = {2023 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2023 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, isbn = {979-8-3503-4691-6 (Online)}, doi = {10.1109/IV55152.2023.10186806}, pages = {6 Seiten}, year = {2023}, abstract = {The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle's drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.}, language = {en} } @article{ElsenKraiss1999, author = {Elsen, Ingo and Kraiss, Karl-Friedrich}, title = {System concept and realization of a scalable neurocomputing architecture}, series = {Systems Analysis Modelling Simulation}, volume = {35}, journal = {Systems Analysis Modelling Simulation}, number = {4}, publisher = {Gordon and Breach Science Publishers}, address = {Amsterdam}, issn = {0232-9298}, pages = {399 -- 419}, year = {1999}, abstract = {This paper describes the realization of a novel neurocomputer which is based on the concepts of a coprocessor. In contrast to existing neurocomputers the main interest was the realization of a scalable, flexible system, which is capable of computing neural networks of arbitrary topology and scale, with full independence of special hardware from the software's point of view. On the other hand, computational power should be added, whenever needed and flexibly adapted to the requirements of the application. Hardware independence is achieved by a run time system which is capable of using all available computing power, including multiple host CPUs and an arbitrary number of neural coprocessors autonomously. The realization of arbitrary neural topologies is provided through the implementation of the elementary operations which can be found in most neural topologies.}, language = {en} } @article{ElsenKraissKrumbiegeletal.1999, author = {Elsen, Ingo and Kraiss, Karl-Friedrich and Krumbiegel, Dirk and Walter, Peter and Wickel, Jochen}, title = {Visual information retrieval for 3D product identification: a midterm report}, series = {KI - K{\"u}nstliche Intelligenz}, volume = {13}, journal = {KI - K{\"u}nstliche Intelligenz}, number = {1}, publisher = {Springer}, address = {Berlin}, issn = {1610-1987}, pages = {64 -- 67}, year = {1999}, language = {en} } @inproceedings{Elsen1998, author = {Elsen, Ingo}, title = {A pixel based approach to view based object recognition with self-organizing neural networks}, series = {IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society}, booktitle = {IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society}, publisher = {IEEE}, address = {New York}, isbn = {0-7803-4503-7}, doi = {10.1109/IECON.1998.724032}, pages = {2040 -- 2044}, year = {1998}, abstract = {This paper addresses the pixel based classification of three dimensional objects from arbitrary views. To perform this task a coding strategy, inspired by the biological model of human vision, for pixel data is described. The coding strategy ensures that the input data is invariant against shift, scale and rotation of the object in the input domain. The image data is used as input to a class of self organizing neural networks, the Kohonen-maps or self-organizing feature maps (SOFM). To verify this approach two test sets have been generated: the first set, consisting of artificially generated images, is used to examine the classification properties of the SOFMs; the second test set examines the clustering capabilities of the SOFM when real world image data is applied to the network after it has been preprocessed to be invariant against shift, scale and rotation. It is shown that the clustering capability of the SOFM is strongly dependant on the invariance coding of the images.}, language = {en} } @inproceedings{ElsenKraissKrumbiegel1997, author = {Elsen, Ingo and Kraiss, Karl-Friedrich and Krumbiegel, Dirk}, title = {Pixel based 3D object recognition with bidirectional associative memories}, series = {International Conference on Neural Networks 1997}, booktitle = {International Conference on Neural Networks 1997}, publisher = {IEEE}, address = {New York}, isbn = {0-7803-4122-8}, pages = {1679 -- 1684}, year = {1997}, abstract = {This paper addresses the pixel based recognition of 3D objects with bidirectional associative memories. Computational power and memory requirements for this approach are identified and compared to the performance of current computer architectures by benchmarking different processors. It is shown, that the performance of special purpose hardware, like neurocomputers, is between one and two orders of magnitude higher than the performance of mainstream hardware. On the other hand, the calculation of small neural networks is performed more efficiently on mainstream processors. Based on these results a novel concept is developed, which is tailored for the efficient calculation of bidirectional associative memories. The computational efficiency is further enhanced by the application of algorithms and storage techniques which are matched to characteristics of the application at hand.}, language = {en} } @inproceedings{DeyElsenFerreinetal.2021, author = {Dey, Thomas and Elsen, Ingo and Ferrein, Alexander and Frauenrath, Tobias and Reke, Michael and Schiffer, Stefan}, title = {CO2 Meter: a do-it-yourself carbon dioxide measuring device for the classroom}, series = {PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference}, booktitle = {PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference}, editor = {Makedon, Fillia}, publisher = {Association for Computing Machinery}, address = {New York}, isbn = {9781450387927}, doi = {10.1145/3453892.3462697}, pages = {292 -- 299}, year = {2021}, abstract = {In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway.}, language = {en} } @article{ElsenHartungHornetal.2001, author = {Elsen, Ingo and Hartung, Frank and Horn, Uwe and Kampmann, Markus and Peters, Liliane}, title = {Streaming technology in 3G mobile communication systems}, series = {Computer : innovative technology for computer professionals}, volume = {34}, journal = {Computer : innovative technology for computer professionals}, number = {9 Seiten}, editor = {Voas, Jeffrey}, publisher = {IEEE}, address = {New York}, issn = {0018-9162}, pages = {46 -- 52}, year = {2001}, abstract = {Third-generation mobile communication systems will combine standardized streaming with a range of unique services to provide high-quality Internet content that meets the specific needs of the rapidly growing mobile market.}, language = {en} } @inproceedings{WalterElsenMuelleretal.1999, author = {Walter, Peter and Elsen, Ingo and M{\"u}ller, Holger and Kraiss, Karl-Friedrich}, title = {3D object recognition with a specialized mixtures of experts architecture}, series = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings}, booktitle = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings}, publisher = {IEEE}, address = {New York}, isbn = {0-7803-5529-6}, issn = {1098-7576}, doi = {10.1109/IJCNN.1999.836243}, pages = {3563 -- 3568}, year = {1999}, abstract = {Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.}, language = {en} }