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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Vehicle Trajectory Planning: Minimum Violation Planning and Model Predictive Control Comparison
Original language description
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.
Czech name
—
Czech description
—

Project
<a href="/en/project/GJ20-11626Y" target="_blank" >GJ20-11626Y: Koopman operator framework for control of complex nonlinear dynamical systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of 2022 IEEE Intelligent Vehicles Symposium (IV)
ISBN
978-1-6654-8821-1
ISSN
—
e-ISSN
1931-0587
Number of pages
6
Pages from-to
145-150
Publisher name
IEEE
Place of publication
Piscataway
Event location
Aachen
Event date
Jun 4, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000854106700021

Similar results(10)