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ZeTeM > Research and Applications > Projects > OPA³L - Optimally Assisted, Highly Automated, Autonomous and Cooperative Vehicle Navigation and Localization

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OPA³L - Optimally Assisted, Highly Automated, Autonomous and Cooperative Vehicle Navigation and Localization

Working Group:WG Optimization and Optimal Control
Leadership: Prof. Dr. Christof Büskens ((0421) 218-63861, E-Mail: bueskens@math.uni-bremen.de )
Arne Berger ((0421) 218-63872, E-Mail: a.berger@uni-bremen.de)
Dr.-Ing. Mitja Echim ((0421) 218-63865, E-Mail: mitja.echim@uni-bremen.de)
Matthias Rick ((0421) 218-64356, E-Mail: mrick@math.uni-bremen.de)
Andreas Folkers ((0421) 218-63867, E-Mail: afolkers@uni-bremen.de)
Processor: Matthias Rick ((0421) 218-64356, E-Mail: mrick@math.uni-bremen.de)
Andreas Folkers ((0421) 218-63867, E-Mail: afolkers@uni-bremen.de)
Grace Anulika Eze ((0421) 218-63871, E-Mail: eze@uni-bremen.de)
Dr.-Ing. Mahmood Shubbak ((0421) 218-59892, E-Mail: shubbak@uni-bremen.de)
Markus Weber (E-Mail: mweber@uni-bremen.de)
Fabienne Klatt (E-Mail: fklatt@uni-bremen.de)
Lennart Evers ((0421) 218-63891, E-Mail: levers@uni-bremen.de)
Xibo Li
Funding: DLR, Raumfahrtmanagement, Bonn
Bundesministerium für Wirtschaft und Energie
Project partner: AG Kognitive Neuroinformatik, Universität Bremen
AG Computergrafik und Virtuelle Realität, Universität Bremen
LRT 9.3 Navigation, Universität BW München
LRT 9.1 Raumfahrttechnik, Universität BW München
IAV, IAV - Ingenieurgesellschaft Auto und Verkehr GmbH
ANavS, ANavS - Advanced Navigation Solutions
Time period: 01.03.2019 - 14.02.2023
Website:http://www.math.uni-bremen.de/zetem/opa3l
Bild des Projekts OPA³L - Optimal Assistierte, hoch Automatisierte, Autonome und kooperative Fahrzeugnavigation und Lokalisation The goal of the project is to automate recurring drives in known areas and in particular to present solutions for cooperative maneuvers in such areas. For this the partners are working on the implementation in an application-oriented test field.

The hybrid vehicle used in the project enables an effective and environmentally friendly choice between electric and conventional propulsion. Furthermore, the research car is equipped with a multitude of sensors, such as cameras, radar, ultrasonic and laser scanners, in order to be able to act autonomously. With these sensor data, the car can recognize its surroundings. A computer-generated image of the real test vehicle and the recognized environment then allows the implementation of optimal, fast, comfortable, safe and fuel-efficient driving maneuvers.

The project is coordinated by the Working Group for Optimization and Optimal Control of the Center for Industrial Mathematics and carried out in close cooperation with the Working Group for Cognitive Neuroinformatics. Further partners in the project are the Computer Graphics and Virtual Reality Working Group of the University of Bremen and the Institute for Space Technology and Utilisation of Space at the Bundeswehr University Munich, as well as the industrial companies IAV and ANavS.

Bundesministerium für Wirtschaft und Energie

Publications

  1. A. Folkers, M. Rick, C. Büskens.
    Time-Dependent Hybrid-State A⁎ and Optimal Control for Autonomous Vehicles in Arbitrary and Dynamic Environments.
    21th IFAC World Congress, 11.07.-17.07.2020, Berlin, Germany.
    Proceedings of the 21th IFAC World Congress, 53(2):15077-15083, 2021.

    DOI: 10.1016/j.ifacol.2020.12.2029
    online at: https://www.sciencedirect.com/science/article/pii/S2405896320326653

  2. A. Folkers, M. Rick, C. Büskens.
    Controlling an Autonomous Vehicle with Deep Reinforcement Learning.
    Intelligent Vehicles Symposium, 09.06.-12.06.2019, Paris, France.
    Proceedings of the 30th IEEE Intelligent Vehicles Symposium, pp. 2025-2031, 2019.

    Best Student Paper

    DOI: 10.1109/ivs.2019.8814124

  3. M. Rick, J. Clemens, L. Sommer, A. Folkers, K. Schill, C. Büskens.
    Autonomous Driving Based on Nonlinear Model Predictive Control and Multi-Sensor Fusion.
    10th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2019), 03.07.-05.07.2019.

    DOI: 10.1016/j.ifacol.2019.08.068

  4. A. Folkers.
    Steuerung eines autonomen Fahrzeugs durch Deep Reinforcement Learning.
    BestMasters, 75 pages, Springer Verlag, 2019.

    DOI: 10.1007/978-3-658-28886-0