On XYZ-Motion Planning Using a Full Car Model

Sayan Chakraborty, Yu Jiang, Zhong Ping Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper studies XYZ-motion planning using a full car model with active suspension components. The proposed approach involves harnessing perception data to create a two-tier representation of the road surface, consisting of the encoded road and the estimated road. The encoded road isolates specific road events, while the estimated road provides a holistic road profile. Leveraging on the information of the estimated road, a generic nonlinear optimization problem involving a full car model is formulated to craft an XYZ-motion plan. To ensure practical in-vehicle implementation, the optimization problem is decomposed into to two distinct phases. First, an XY-path is optimized by leveraging information from the isolated road events. Second, the generated XY-path is used to devise an XYZ-trajectory, including a vertical motion plan and an optimal speed profile. Finally, numerical results obtained by using both synthetic and real-world road surface data are provided to illustrate the effectiveness of the proposed methodology.

Original languageEnglish (US)
Title of host publication2024 American Control Conference, ACC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages245-250
Number of pages6
ISBN (Electronic)9798350382655
DOIs
StatePublished - 2024
Event2024 American Control Conference, ACC 2024 - Toronto, Canada
Duration: Jul 10 2024Jul 12 2024

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Conference

Conference2024 American Control Conference, ACC 2024
Country/TerritoryCanada
CityToronto
Period7/10/247/12/24

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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