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Energy-Efficient Driving in Dynamic Environment: Globally Optimal MPC-like Motion Planning Framework

  • Zlatan AjanovićEmail author
  • Michael Stolz
  • Martin Horn
Conference paper
Part of the Lecture Notes in Mobility book series (LNMOB)

Abstract

Predictive motion planning is a key for achieving energy-efficient driving, which is one of the major visions of automated driving nowadays. Motion planning is a challenging task, especially in the presence of other dynamic traffic participants. Two main issues have to be addressed. First, for globally optimal driving, the entire trip has to be considered at once. Second, the movement of other traffic participants is usually not known in advance. Both issues lead to increased computational effort. The length of the prediction horizon is usually large and the problem of unknown future movement of other traffic participants usually requires frequent replanning. This work proposes a novel motion planning approach for vehicles operating in dynamic environments. The above-mentioned problems are addressed by splitting the planning into a strategic planning part and situation-dependent replanning part. Strategic planning is done without considering other dynamic participants and is reused later in order to lower the computational effort during replanning phase.

Keywords

Eco-driving Optimal speed trajectory Dynamic environment Real-time capability Replanning 

Notes

Acknowledgements

The project leading to this study has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 675999, ITEAM project.

VIRTUAL VEHICLE Research Center is funded within the COMET—Competence Centers for Excellent Technologies—program by the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT), the Federal Ministry of Science, Research and Economy (BMWFW), the Austrian Research Promotion Agency (FFG), and the province of Styria and the Styrian Business Promotion Agency (SFG). The COMET program is administrated by FFG.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Electrics/Electronics and SoftwareVirtual Vehicle Research CenterGrazAustria
  2. 2.Institute of Automation and ControlGraz University of TechnologyGrazAustria

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