Vehicle Trajectory Planning: Minimum Violation Planning and Model Predictive Control Comparison

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00359576" target="_blank" >RIV/68407700:21230/22:00359576 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/IV51971.2022.9827430" target="_blank" >https://doi.org/10.1109/IV51971.2022.9827430</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IV51971.2022.9827430" target="_blank" >10.1109/IV51971.2022.9827430</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Vehicle Trajectory Planning: Minimum Violation Planning and Model Predictive Control Comparison

Popis výsledku v původním jazyce

State trajectory planning is one of the primary self-driving cars technology enablers. However, state trajectory planning is a more complex and computationally demanding task compared to path planning. The vehicle’s east and north position, yaw, yaw rate, velocity, and battery state of charge variables trajectory planning with a particular focus on the safety and economy of the vehicle operation is concerned in this paper. Comparison of Model Predictive Control (MPC) and Minimum Violation Planning (MVP) used for trajectory planning is brought in this paper. The latter is a sampling-based algorithm based on the RRT* algorithm compared to the other optimization-based algorithm. A heuristic is introduced to convert the complex non-convex optimization planning task to a convex optimization problem. Next, MVP algorithm enhancement is proposed to reduce the calculation time. Both algorithms are tested on a selected testing scenario using a high fidelity nonlinear single-track model implemented in Matlab & Simulink environment.

Název v anglickém jazyce

Vehicle Trajectory Planning: Minimum Violation Planning and Model Predictive Control Comparison

Popis výsledku anglicky

State trajectory planning is one of the primary self-driving cars technology enablers. However, state trajectory planning is a more complex and computationally demanding task compared to path planning. The vehicle’s east and north position, yaw, yaw rate, velocity, and battery state of charge variables trajectory planning with a particular focus on the safety and economy of the vehicle operation is concerned in this paper. Comparison of Model Predictive Control (MPC) and Minimum Violation Planning (MVP) used for trajectory planning is brought in this paper. The latter is a sampling-based algorithm based on the RRT* algorithm compared to the other optimization-based algorithm. A heuristic is introduced to convert the complex non-convex optimization planning task to a convex optimization problem. Next, MVP algorithm enhancement is proposed to reduce the calculation time. Both algorithms are tested on a selected testing scenario using a high fidelity nonlinear single-track model implemented in Matlab & Simulink environment.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GJ20-11626Y" target="_blank" >GJ20-11626Y: Koncept Koopmanova operátoru pro řízení komplexních nelineárních dynamických systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 2022 IEEE Intelligent Vehicles Symposium (IV)

ISBN

978-1-6654-8821-1

ISSN

—

e-ISSN

1931-0587

Počet stran výsledku

6

Strana od-do

145-150

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Aachen

Datum konání akce

4. 6. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000854106700021