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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Buch/Bericht

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.
Originalspracheenglisch
TitelAdvanced Microsystems for Automotive Applications 2017
Herausgeber (Verlag)Springer International Publishing AG
ISBN (elektronisch)978-3-319-66972-4
ISBN (Print)978-3-319-66971-7
PublikationsstatusVeröffentlicht - Jan 2018

Publikationsreihe

NameLecture Notes in Mobility

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  • Dieses zitieren

    Ajanovic, Z., Stolz, M., & Horn, M. (2018). Energy-Efficient Driving in Dynamic Environment: Globally Optimal MPC-like Motion Planning Framework. in Advanced Microsystems for Automotive Applications 2017 (Lecture Notes in Mobility). Springer International Publishing AG .