Multi-Stage Estimation Algorithm for Vehicle Trajectory Tracking

Hamidreza Alai; Rajesh Rajamani

doi:10.1016/j.ifacol.2023.12.016

Multi-Stage Estimation Algorithm for Vehicle Trajectory Tracking

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper develops a multi-stage estimation algorithm for vehicle trajectory tracking applications. Previously designed nonlinear observers for vehicle trajectory tracking lack either the ability to handle variable velocity or have a high sensitivity to sensor noise. To overcome these shortcomings, the original model of the non-ego vehicle is translated into three separate models for speed, orientation, and position. Three stable observers are designed for these models which are all shown to be stable and robust to uncertainties. The new estimation algorithm outperforms previous high-gain and LMI-based nonlinear observers. The developed observer is useful for collision prediction and avoidance applications.

Original language	English (US)
Title of host publication	IFAC-PapersOnLine
Editors	Marcello Canova
Publisher	Elsevier B.V.
Pages	151-156
Number of pages	6
Edition	3
ISBN (Electronic)	9781713872344
DOIs	https://doi.org/10.1016/j.ifacol.2023.12.016
State	Published - Oct 1 2023
Event	3rd Modeling, Estimation and Control Conference, MECC 2023 - Lake Tahoe, United States Duration: Oct 2 2023 → Oct 5 2023

Publication series

Name	IFAC-PapersOnLine
Number	3
Volume	56
ISSN (Electronic)	2405-8963

Conference

Conference	3rd Modeling, Estimation and Control Conference, MECC 2023
Country/Territory	United States
City	Lake Tahoe
Period	10/2/23 → 10/5/23

Bibliographical note

Publisher Copyright:
Copyright © 2023 The Authors.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

collision avoidance
nonlinear observer
trajectory estimation
vehicle tracking

Publisher link

10.1016/j.ifacol.2023.12.016

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title = "Multi-Stage Estimation Algorithm for Vehicle Trajectory Tracking",

abstract = "This paper develops a multi-stage estimation algorithm for vehicle trajectory tracking applications. Previously designed nonlinear observers for vehicle trajectory tracking lack either the ability to handle variable velocity or have a high sensitivity to sensor noise. To overcome these shortcomings, the original model of the non-ego vehicle is translated into three separate models for speed, orientation, and position. Three stable observers are designed for these models which are all shown to be stable and robust to uncertainties. The new estimation algorithm outperforms previous high-gain and LMI-based nonlinear observers. The developed observer is useful for collision prediction and avoidance applications.",

keywords = "collision avoidance, nonlinear observer, trajectory estimation, vehicle tracking",

author = "Hamidreza Alai and Rajesh Rajamani",

note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors.; 3rd Modeling, Estimation and Control Conference, MECC 2023 ; Conference date: 02-10-2023 Through 05-10-2023",

year = "2023",

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doi = "10.1016/j.ifacol.2023.12.016",

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series = "IFAC-PapersOnLine",

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TY - GEN

T1 - Multi-Stage Estimation Algorithm for Vehicle Trajectory Tracking

AU - Alai, Hamidreza

AU - Rajamani, Rajesh

PY - 2023/10/1

Y1 - 2023/10/1

N2 - This paper develops a multi-stage estimation algorithm for vehicle trajectory tracking applications. Previously designed nonlinear observers for vehicle trajectory tracking lack either the ability to handle variable velocity or have a high sensitivity to sensor noise. To overcome these shortcomings, the original model of the non-ego vehicle is translated into three separate models for speed, orientation, and position. Three stable observers are designed for these models which are all shown to be stable and robust to uncertainties. The new estimation algorithm outperforms previous high-gain and LMI-based nonlinear observers. The developed observer is useful for collision prediction and avoidance applications.

AB - This paper develops a multi-stage estimation algorithm for vehicle trajectory tracking applications. Previously designed nonlinear observers for vehicle trajectory tracking lack either the ability to handle variable velocity or have a high sensitivity to sensor noise. To overcome these shortcomings, the original model of the non-ego vehicle is translated into three separate models for speed, orientation, and position. Three stable observers are designed for these models which are all shown to be stable and robust to uncertainties. The new estimation algorithm outperforms previous high-gain and LMI-based nonlinear observers. The developed observer is useful for collision prediction and avoidance applications.

KW - collision avoidance

KW - nonlinear observer

KW - trajectory estimation

KW - vehicle tracking

UR - https://www.scopus.com/pages/publications/85184345607

UR - https://www.scopus.com/pages/publications/85184345607#tab=citedBy

U2 - 10.1016/j.ifacol.2023.12.016

DO - 10.1016/j.ifacol.2023.12.016

M3 - Conference contribution

AN - SCOPUS:85184345607

T3 - IFAC-PapersOnLine

SP - 151

EP - 156

BT - IFAC-PapersOnLine

A2 - Canova, Marcello

PB - Elsevier B.V.

T2 - 3rd Modeling, Estimation and Control Conference, MECC 2023

Y2 - 2 October 2023 through 5 October 2023

ER -

Multi-Stage Estimation Algorithm for Vehicle Trajectory Tracking

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

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