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@PHDTHESIS{Klingbeil:1009074,
      author       = {Klingbeil, Xiaonan},
      othercontributors = {Andert, Jakob Lukas and Pischinger, Stefan},
      title        = {{P}rädiktive kooperative {F}ahrfunktion zur energie- und
                      verkehrseffizienten {F}ahrzeugführung},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2025-03319},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2024},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University 2025; Dissertation, Rheinisch-Westfälische
                      Technische Hochschule Aachen, 2024},
      abstract     = {The present dissertation focuses on the development and
                      evaluation of a predictive cooperative driving function,
                      which includes both optimization-based vehicle control and a
                      holistic driving maneuver management. Using realistic
                      traffic simulations, the predictive cooperative driving
                      function is assessed for its potential energy savings and
                      its capability to optimize traffic flow. Initially, a
                      comprehensive simulation environment is established, based
                      on the inner ring of Paderborn’s city center, to generate
                      realistic traffic scenarios. This environment enables an
                      accurate modeling of route planning, traffic light switching
                      programs, and traffic volume. By adjusting the intelligent
                      driver model using real measurement data, the realistic
                      driving behavior of traffic participants is simulated. The
                      parameterization of the simulation scenario is validated
                      based on traffic metrics, ensuring the assessment of the
                      driving strategy under realistic conditions. Two LSTM
                      networks predict the longitudinal velocity of the preceding
                      vehicle and the longitudinal and lateral velocities of
                      neighboring vehicles, especially during a lane change
                      maneuver. The accuracy of these models was validated and
                      overall showed reliable results with minor deviations. A
                      centralized approach is devised for both optimization-based
                      vehicle control and maneuver management. In this approach,
                      the vehicle states of the platoon leader and all following
                      vehicles are simultaneously considered in an optimization
                      problem based on the model predictive control algorithm. The
                      developed driving function allows for an energy-optimized
                      velocity adjustment in both following driving and during
                      various maneuvers. In a microscopic analysis of the
                      simulation scenarios, varying energy-saving potentials are
                      identified based on traffic volume. In uncongested traffic
                      scenarios, it is evident that while the model reduces the
                      distance between vehicles, it occasionally leads to delays
                      and adverse effects for neighboring traffic participants.
                      Hence, the total energy-saving potential of all platoon
                      vehicles is 2.92 $\%,$ and the reduction in average velocity
                      is 8.68 $\%.$ In congested traffic, the comprehensive
                      coordination of the driving function results in more
                      efficient maneuvers and improved traffic flow. The
                      energy-saving potential ranges between 7.89 $\%$ and 9.14
                      $\%,$ and the increase in average velocity is between 3.66
                      $\%$ and 12.06 $\%.$ On a macroscopic level, it was
                      determined that the developed function enables energy
                      savings of 0.58 $\%$ and 1.62 $\%$ for all vehicles in
                      simulations during free-driving and steady traffic
                      conditions. In congested traffic, a marginal increase in
                      energy demand of 0,34 $\%$ is observed. In correlation, an
                      increase in the average velocity across all traffic
                      scenarios of 0.47 $\%,$ 1.91 $\%,$ and 2.94 $\%$ is
                      achieved. Furthermore, fundamental diagrams confirm the
                      advantages of the anticipatory and optimization-based
                      velocity control, which offers better adaptation of the
                      velocity to dynamic traffic conditions and contributes to
                      stabilizing the traffic flow.},
      cin          = {412330 / 422320},
      ddc          = {620},
      cid          = {$I:(DE-82)412330_20140620$ / $I:(DE-82)422320_20210420$},
      pnm          = {EFRE-0800354 - Hy-Nets4all: Ganzheitliche Entwicklungs- und
                      Validierungsumgebung zur Optimierung des elektrifizierten
                      Fahrens im urbanen Raum (0800354)},
      pid          = {G:(EFRE)0800354},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2025-03319},
      url          = {https://publications.rwth-aachen.de/record/1009074},
}