@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{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{11925,
  abstract     = {{In this paper we present a system for car navigation by fusing sensor data on an Android smartphone. The key idea is to use both the internal sensors of the smartphone (e.g., gyroscope) and sensor data from the car (e.g., speed information) to support navigation via GPS. To this end we employ a CAN-Bus-to-Bluetooth adapter to establish a wireless connection between the smartphone and the CAN-Bus of the car. On the smartphone a strapdown algorithm and an error-state Kalman filter are used to fuse the different sensor data streams. The experimental results show that the system is able to maintain higher positioning accuracy during GPS dropouts, thus improving the availability and reliability, compared to GPS-only solutions.}},
  author       = {{Walter, Oliver and Schmalenstroeer, Joerg and Engler, Andreas and Haeb-Umbach, Reinhold}},
  booktitle    = {{9th Workshop on Positioning Navigation and Communication (WPNC 2012)}},
  keywords     = {{Smartphone, navigation, sensor fusion}},
  title        = {{{Smartphone-Based Sensor Fusion for Improved Vehicular Navigation}}},
  year         = {{2012}},
}

@inproceedings{11726,
  abstract     = {{In this paper we present a robust location estimation algorithm especially focused on the accuracy in vertical position. A loosely-coupled error state space Kalman filter, which fuses sensor data of an Inertial Measurement Unit and the output of a Global Positioning System device, is augmented by height information from an altitude measurement unit. This unit consists of a barometric altimeter whose output is fused with topographic map information by a Kalman filter to provide robust information about the current vertical user position. These data replace the less reliable vertical position information provided the GPS device. It is shown that typical barometric errors like thermal divergences and fluctuations in the pressure due to changing weather conditions can be compensated by the topographic map information and the barometric error Kalman filter. The resulting height information is shown not only to be more reliable than height information provided by GPS. It also turns out that it leads to better attitude and thus better overall localization estimation accuracy due to the coupling of spatial orientations via the Direct Cosine Matrix. Results are presented both for artificially generated and field test data, where the user is moving by car.}},
  author       = {{Bevermeier, Maik and Walter, Oliver and Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{7th Workshop on Positioning Navigation and Communication (WPNC 2010)}},
  keywords     = {{altitude measurement unit, barometers, barometric altimeter, barometric error Kalman filter, barometric height estimation, direct cosine matrix, global positioning system, Global Positioning System, GPS device, height information, height measurement, inertial measurement unit, Kalman filters, loosely-coupled error state space Kalman filter, loosely-coupled Kalman-filter, map matching, robust information, robust location estimation, sensor fusion, topographic map information, vertical user position}},
  pages        = {{128--134}},
  title        = {{{Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter}}},
  doi          = {{10.1109/WPNC.2010.5650745}},
  year         = {{2010}},
}

@inproceedings{11723,
  abstract     = {{In this paper we present a novel vehicle tracking algorithm, which is based on multi-level sensor fusion of GPS (global positioning system) with Inertial Measurement Unit sensor data. It is shown that the robustness of the system to temporary dropouts of the GPS signal, which may occur due to limited visibility of satellites in narrow street canyons or tunnels, is greatly improved by sensor fusion. We further demonstrate how the observation and state noise covariances of the employed Kalman filters can be estimated alongside the filtering by an application of the Expectation-Maximization algorithm. The proposed time-variant multi-level Kalman filter is shown to outperform an Interacting Multiple Model approach while at the same time being computationally less demanding.}},
  author       = {{Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{6th Workshop on Positioning Navigation and Communication (WPNC 2009)}},
  keywords     = {{covariance matrices, expectation-maximisation algorithm, expectation-maximization algorithm, global positioning system, Global Positioning System, GPS, inertial measurement unit, interacting multiple model approach, Kalman filters, multilevel sensor fusion, narrow street canyons, narrow tunnels, online parameter estimation, parameter estimation, road vehicles, robust vehicle localization, sensor fusion, state noise covariances, time-variant multilevel Kalman filter, vehicle tracking algorithm}},
  pages        = {{235--242}},
  title        = {{{Robust vehicle localization based on multi-level sensor fusion and online parameter estimation}}},
  doi          = {{10.1109/WPNC.2009.4907833}},
  year         = {{2009}},
}

@inproceedings{11881,
  abstract     = {{A combination of GPS (global positioning system) and INS (inertial navigation system) is known to provide high precision and highly robust vehicle localization. Notably during times when the GPS signal has a poor quality, e.g. due to the lack of a sufficiently large number of visible satellites, the INS, which may consist of a gyroscope and an odometer, will lead to improved positioning accuracy. In this paper we show how velocity information obtained from GSM (global system for mobile communications) signalling, rather than from a tachometer, can be used together with a gyroscope sensor to support localization in the presence of temporarily unavailable GPS data. We propose a sensor fusion system architecture and present simulation results that show the effectiveness of this approach.}},
  author       = {{Peschke, Sven and Bevermeier, Maik and Haeb-Umbach, Reinhold}},
  booktitle    = {{6th Workshop on Positioning Navigation and Communication (WPNC 2009)}},
  keywords     = {{cellular radio, distance measurement, global positioning system, Global Positioning System, global system for mobile communications, GPS positioning approach, GSM velocity, gyroscopes, gyroscope sensor, inertial navigation, inertial navigation system, odometer, sensor fusion system architecture, sensors}},
  pages        = {{195--202}},
  title        = {{{A GPS positioning approach exploiting GSM velocity estimates}}},
  doi          = {{10.1109/WPNC.2009.4907827}},
  year         = {{2009}},
}