@inproceedings{61492, abstract = {{This paper deals with the development and results of a prediction framework for traffic light control systems as well as the usage and benefits of such predictions in green light optimal speed advisory (GLOSA) scenarios. Various machine learning methods like support vector machines, neural networks or reinforcement learning were evaluated for their applicability in the prediction context and compared based on their efficiency and most importantly accuracy. The resulting prediction framework uses decision tree ensemble models combined with certain model knowledge to forecast different control strategies. This method was chosen due to its best performance in various test scenarios. Very high accuracy and fidelity were achieved for standard control methods like fixed-time, time-of-day-based and 'ordinary' traffic-based programs. Only for the more sophisticated model predictive control which was tested lower accuracies were achieved. For the upcoming GLOSA application the penetration of equipped vehicles was varied for different traffic scenarios and control strategies. Results showcase high potentials for enhancing urban mobility and reducing environmental impact by lower emissions and waiting times. However, it is also clear from the studies presented in this contribution that the coordination of the control strategy with the GLOSA vehicles is of enormous importance.}}, author = {{Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)}}, keywords = {{ML, Prediction, Tree Ensembles, GLOSA}}, location = {{Gold Coast (Australia)}}, publisher = {{IEEE}}, title = {{{ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application}}}, volume = {{28}}, year = {{2026}}, } @inproceedings{59088, abstract = {{This paper deals with the implementation and results of the application of a multi-stage traffic light control system which includes a simulation-based traffic estimation and model predictive control. The traffic light control system incorporates a fuzzy system for traffic light phase preselection, followed by a model predictive control to optimise phase combinations and switching times. Predefined phases are selected without restrictions in the order according to a multi-objective optimisation to adapt to the traffic as freely as possible. Initially, the system is tested in simulations and compared with existing methods and analysed afterwards for its effectiveness in a prototype commissioning in field tests. Results indicate high potentials for reducing emissions and waiting times, highlighting the system's value. However, further refinement is necessary for standard implementation. This comprehensive approach demonstrates advancements in traffic management technology, showcasing the potential for enhancing urban mobility and reducing environmental impact.}}, author = {{Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)}}, issn = {{2153-0017}}, keywords = {{MPC}}, location = {{Edmonton (Canada)}}, publisher = {{IEEE}}, title = {{{Implementation and Results of a Multi-Stage Model Predictive Traffic Light Control System}}}, doi = {{10.1109/itsc58415.2024.10919569}}, volume = {{27}}, year = {{2025}}, } @inproceedings{61427, abstract = {{The carbon footprint of modern vehicles and their mechatronic systems is more important than ever. Research by the publicly funded Nalyses project and the HELLA company shows that the headlamps use phase makes a significant contribution to the life cycle footprint taking into account the current electricity mix [1]. Today, functionalities such as adaptive curve light or glare-free high beam ensure comfort and safety by assessing the state of the vehicle and evaluating the driving scenario ahead. In future, this evaluation will be expanded and used to adapt the headlamp to the driving scenario in such a way that as little light as possible is emitted, but as much light as necessary. In order to achieve this goal, an overall evaluation of the regulatory compliant energy saving potential is crucial in a first step and leads to constraints for a dynamic adaption while driving. In this paper, the potential is illustrated by evaluating UNECE Regulation No. 149 and optimizing luminous intensity distributions. Depending on the different resolutions of matrix LED headlamps, this approach can result in a significantly lower luminous flux. On the other hand, the results are point-like distributions that raise the question of whether the regulation still provides for sensible minimum requirements for modern matrix LED headlamps. The results are further presented in a simulated virtual environment with regard to the resulting luminance in different driving scenarios. We then present an approach to integrate regulatory requirements into a control algorithm by setting optimization constraints and saturating the control. Finally, we classify the found luminous intensity distributions qualitatively according to common lighting criteria. In summary, although the investigated minimum distributions are by no means desirable for drivers themselves, they form the basis on which energy-saving distributions for illuminated areas and twilight scenarios could be adaptively controlled in the future.}}, author = {{Fittkau, Niklas and Bußemas, Leon and Malena, Kevin and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Proceedings of the 16th International Symposium on Automotive Lighting 2025}}, location = {{Darmstadt}}, title = {{{Regulatory-compliant energy-saving potential for the passing beam of matrix LED headlamps}}}, doi = {{10.26083/tuprints-00030840}}, year = {{2025}}, } @inproceedings{53106, author = {{Bußemas, Leon and Fittkau, Niklas and Gausemeier, Sandra and Trächtler, Ansgar and Rüddenklau, Nico}}, booktitle = {{VDI Mechatroniktagung Dresden 2024}}, location = {{Dresden}}, pages = {{29--34}}, publisher = {{Technische Universität Dresden}}, title = {{{LiDAR-Sensormodell basierend auf zeitabhängigem Photon Mapping}}}, year = {{2024}}, } @inproceedings{44390, abstract = {{The development of autonomous vehicles and their introduction in urban traffic offer many opportunities for traffic improvements. In this paper, an approach for a future traffic control system for mixed autonomy traffic environments is presented. Furthermore, a simulation framework based on the city of Paderborn is introduced to enable the development and examination of such a system. This encompasses multiple elements including the road network itself, traffic lights, sensors as well as methods to analyse the topology of the network. Furthermore, a procedure for traffic demand generation and routing is presented based on statistical data of the city and traffic data obtained by measurements. The resulting model can receive and apply the generated control inputs and in turn generates simulated sensor data for the control system based on the current system state.}}, author = {{Link, Christopher and Malena, Kevin and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems}}, isbn = {{978-989-758-652-1}}, keywords = {{Traffic Simulation, Traffic Control, Car2X, Mixed Autonomy, Autonomous Vehicles, SUMO, Sensor Simulation, Traffic Demand Generation, Routing, Traffic Lights, Graph Analysis, Traffic Observer}}, location = {{Prague, Czech Republic}}, publisher = {{SCITEPRESS - Science and Technology Publications}}, title = {{{Simulation Environment for Traffic Control Systems Targeting Mixed Autonomy Traffic Scenarios}}}, doi = {{10.5220/0011987600003479}}, year = {{2023}}, } @misc{56827, abstract = {{Zusammenfassung: Die Erfindung betrifft ein Verkehrsleitsystem für die Steuerung von Lichtsignalanlagen aufweisend: • Eingänge für Verkehrszustandsermittlungsverfahren abhängig von verschiedenen Sensoren, wobei die Sensoren einen aktuellen Verkehrszustand eines zugeordneten Verkehrsabschnitts ermitteln, • Statusregister, die den aktuellen Status der gesteuerten Lichtsignalanlagen speichern, • Phasenregister, die alle möglichen Verkehrsleitphasen, die durch die Lichtsignalanlagen angesteuert werden können, speichern, • eine Fuzzy-Logik (F) zur Vorselektion von möglichen Verkehrsleitphasen basierend auf einem aktuellen Verkehrszustand, wobei für jede mögliche Verkehrsleitphase basierend auf dem aktuellen Verkehrszustand ein Prioritätswert ermittelt wird, wobei für die weitere Verarbeitung nur eine vorbestimmte Anzahl von vorselektierten nachfolgenden Verkehrsleitphasen anhand der Priorität ausgewählt wird, • wobei die so vorausgewählten Verkehrsleitphasen einer modellprädiktiven Regelung mit einer Verkehrsprädiktionssimulation (MPC) zugeführt werden, wobei die Regelung basierend auf den Phasenfolgen (gebildet aus den vorselektierten Verkehrsleitphasen), dem aktuellen Verkehrszustand und dem aktuellen Status der gesteuerten Lichtsignalanlagen eine geeignete prädiktive zeitliche Phasensteuerung ermittelt, wobei die Schaltzeitpunkte der Phasenfolgen durch ein Optimierungsverfahren berechnet werden, • wobei die so ermittelte prädiktive zeitliche Phasensteuerung keine, eine oder mehrere zu schaltenden Folgephasen mit den ermittelten Schaltzeitpunkten aufweist, • wobei anhand der Bewertungen der einzelnen prädiktiven zeitlichen Phasensteuerungen ermittelt wird, welche der prädiktiven zeitlichen Phasensteuerungen zur weiteren Steuerung der Lichtsignalanlagen ausgewählt und verwendet wird.}}, author = {{Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, title = {{{Vorrichtung und Verfahren zur echtzeit-basierten dynamischen Verkehrszuordnung für zumindest zwei nachfolgende Fahrbahnen}}}, year = {{2023}}, } @inbook{33849, abstract = {{Modern traffic control systems are key to cope with current and future traffic challenges. In this paper information obtained from a microscopic traffic estimation using various data sources is used to feed a new developed traffic control approach. The presented method can control a traffic area with multiple traffic light systems (TLS) reacting to individual road users and pedestrians. In contrast to widespread green time extension techniques, this control selects the best phase sequence by analyzing the current traffic state reconstructed in SUMO and its predicted progress. To achieve this, the key aspect of the control strategy is to use Model Predictive Control (MPC). In order to maintain realism for real world applications, among other things, the traffic phase transitions are modelled in detail and integrated within the prediction. For the efficiency, the approach incorporates a fuzzy logic preselection of all phases reducing the computational effort. The evaluation itself is able to be easily adjusted to focus on various objectives like low occupancies, reducing waiting times and emissions, few number of phase transitions etc. determining the best switching times for the selected phases. Exemplary traffic simulations demonstrate the functionality of the MPC-based control and, in addition, some aspects under development like the real-world communication network are also discussed.}}, author = {{Malena, Kevin and Link, Christopher and Bußemas, Leon and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Communications in Computer and Information Science}}, editor = {{Klein, Cornel and Jarke, Mathias and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}}, isbn = {{9783031170973}}, issn = {{1865-0929}}, keywords = {{Traffic control, Traffic estimation, Real-time, MPC, Fuzzy, Isolated intersection, Networked intersection, Sensor fusion}}, pages = {{232–254}}, publisher = {{Springer International Publishing}}, title = {{{Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments}}}, doi = {{10.1007/978-3-031-17098-0_12}}, volume = {{1612}}, year = {{2022}}, } @inproceedings{23576, author = {{Biemelt, Patrick and Böhm, Sabrina and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC)}}, location = {{Melbourne, Australia}}, pages = {{1619 -- 1626}}, title = {{{Subjective Evaluation of Filter- and Optimization-Based Motion Cueing Algorithms for a Hybrid Kinematics Driving Simulator}}}, year = {{2021}}, } @inproceedings{24159, abstract = {{The online fitting of a microscopic traffic simulation model to reconstruct the current state of a real traffic area can be challenging depending on the provided data. This paper presents a novel method based on limited data from sensors positioned at specific locations and guarantees a general accordance of reality and simulation in terms of multimodal road traffic counts and vehicle speeds. In these considerations, the actual purpose of research is of particular importance. Here, the research aims at improving the traffic flow by controlling the Traffic Light Systems (TLS) of the examined area which is why the current traffic state and the route choices of individual road users are the matter of interest. An integer optimization problem is derived to fit the current simulation to the latest field measurements. The concept can be transferred to any road traffic network and results in an observation of the current multimodal traffic state matching at the given sensor position. First case studies show promosing results in terms of deviations between reality and simulation.}}, author = {{Malena, Kevin and Link, Christopher and Mertin, Sven and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems}}, isbn = {{978-989-758-513-5}}, keywords = {{Microscopic Traffic Simulation, Online State Estimation, Mixed Road Users, Sensor Fusion, Integer Programming, Route Choice, Vehicle2Infrastructure}}, location = {{Online Streaming}}, pages = {{386--395}}, publisher = {{SCITEPRESS}}, title = {{{Online State Estimation for Microscopic Traffic Simulations using Multiple Data Sources*}}}, volume = {{7}}, year = {{2021}}, } @inproceedings{24166, abstract = {{This paper deals with a novel method for the online fitting of a microscopic traffic simulation model to the current state of a real world traffic area. The traffic state estimation is based on limited data of different measurement sources and guarantees general accordance of reality and simulation in terms of multimodal road traffic counts and vehicle speeds. The research is embedded in the challenge of improving the traffic by controlling the traffic light systems (TLS) of the examined area. Therefore, the current traffic state and the predicted route choices of individual road users are the matter of interest. The concept is generally transferable to any road traffic system. To give an impression of the accuracy and potential of the approach, the validation and first application results are presented.}}, author = {{Malena, Kevin and Link, Christopher and Mertin, Sven and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{2021 IEEE Transportation Electrification Conference & Expo (ITEC)}}, isbn = {{978-1-7281-7584-3}}, publisher = {{IEEE}}, title = {{{Validation of an Online State Estimation Concept for Microscopic Traffic Simulations◆}}}, doi = {{10.1109/itec51675.2021.9490087}}, year = {{2021}}, } @article{22963, author = {{Biemelt, Patrick and Gausemeier, Sandra and Trächtler, Ansgar}}, journal = {{International Journal On Advances in Systems and Measurements}}, number = {{3 & 4}}, pages = {{203--219}}, title = {{{Design and Objective Evaluation of Filter- and Optimization-based Motion Cueing Strategies for a Hybrid Kinematics Driving Simulator with 5 Degrees of Freedom}}}, volume = {{13}}, year = {{2020}}, } @inproceedings{22964, author = {{Biemelt, Patrick and Mertin, Sven and Rüddenklau, Nico and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Proceedings of the Driving Simulation Conference Europe VR}}, location = {{Antibes, France}}, pages = {{85--92}}, publisher = {{Driving Simulation Association}}, title = {{{Design and Evaluation of a Novel Filter-Based Motion Cueing Strategy for a Hybrid Kinematics Driving Simulator with 5 Degrees of Freedom}}}, year = {{2020}}, } @inproceedings{22966, author = {{Mertin, Sven and Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{The 23rd IEEE International Conference on Intelligent Transportation Systems}}, publisher = {{International Conference on Intelligent Transportation Systems (ITSC)}}, title = {{{Macroscopic Traffic Flow Control using Consensus Algorithms}}}, volume = {{23}}, year = {{2020}}, } @inproceedings{22968, author = {{Biemelt, Patrick and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Proceedings of the 21st IFAC World Congress}}, location = {{Berlin, Germany}}, pages = {{6082 -- 6088}}, title = {{{A Model-Based Online Reference Prediction Strategy for Model Predictive Motion Cueing Algorithms}}}, year = {{2020}}, } @inproceedings{22970, author = {{Biemelt, Patrick and Mertin, Sven and Rüddenklau, Nico and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Proceedings of the International Conference on Advances in System Simulation (SIMUL)}}, location = {{Valencia, Spain}}, publisher = {{IARIA}}, title = {{{Objective Evaluation of a Novel Filter-Based Motion Cueing Algorithm in Comparison to Optimization-Based Control in Interactive Driving Simulation}}}, year = {{2019}}, } @inbook{22971, author = {{Rüddenklau, Nico and Biemelt, Patrick and Mertin, Sven and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{13th International Symposium on Automotive Lighting (ISAL)}}, pages = {{677--686}}, publisher = {{utzverlag GmbH}}, title = {{{Simulation-Based Lighting Function Development of High-Definition Headlamps}}}, volume = {{18}}, year = {{2019}}, } @inproceedings{22972, author = {{Mertin, Sven and Buse, Dominik and Franke, Mario and Trächtler, Ansgar and Gausemeier, Sandra and Dressler, Falko}}, booktitle = {{VDI/VDE AUTOREG 2019}}, pages = {{159--170}}, publisher = {{VDI Verlag Düsseldorf}}, title = {{{Proof-of-Concept einer komplexen Co-Simulationsumgebung für einen Fahrsimulator zur Untersuchung von Car2X-Kommunikations-Szenarien}}}, year = {{2019}}, } @inbook{22973, author = {{Rüddenklau, Nico and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{VDI/VDE AUTOREG 2019}}, publisher = {{VDI- Verlag, Düsseldorf}}, title = {{{Hardware-in-the-Loop Simulation of High-Definition Headlamp Systems}}}, year = {{2019}}, } @inbook{22982, author = {{Rüddenklau, Nico and Biemelt, Patrick and Mertin, Sven and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{IARIA SysMea}}, pages = {{72--88}}, publisher = {{IARIA}}, title = {{{Real-Time Lighting of High-Definition Headlamps for Night Driving Simulation}}}, volume = {{12}}, year = {{2019}}, } @inproceedings{22986, author = {{Rüddenklau, Nico and Biemelt, Patrick and Henning, Sven and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{SIMUL 2018, The Tenth International Conference on Advances in System Simulation}}, publisher = {{IARIA}}, title = {{{Shader-Based Realtime Simulation of High-Definition Automotive Headlamps}}}, year = {{2018}}, }