Abstract
This paper proposes an optimization-based approach to predict trajectories of autonomous race cars. We assume that the observed trajectory is the result of an optimization problem that trades off path progress against acceleration and jerk smoothness, and which is restricted by constraints. The algorithm predicts a trajectory by solving a parameterized nonlinear program (NLP) which contains path progress and smoothness in cost terms. By observing the actual motion of a vehicle, the parameters of prediction are updated by means of solving an inverse optimal control problem that contains the parameters of the predicting NLP as optimization variables. The algorithm therefore learns to predict the observed vehicle trajectory in a least-squares relation to measurement data and to the presumed structure of the predicting NLP. This work contributes with an algorithm that allows for accurate and interpretable predictions with sparse data. The algorithm is implemented on embedded hardware in an autonomous real-world race car that is competing in the challenge Roborace and analyzed with respect to recorded data.
Originalsprache | englisch |
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Titel | 2022 European Control Conference, ECC 2022 |
Herausgeber (Verlag) | IEEE |
Seiten | 146-153 |
Seitenumfang | 8 |
ISBN (elektronisch) | 9783907144077 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2022 |
Veranstaltung | 20th European Control Conference: ECC22 - London, Hybrider Event, Großbritannien / Vereinigtes Königreich Dauer: 12 Juli 2022 → 15 Juli 2022 https://ecc22.euca-ecc.org/ |
Konferenz
Konferenz | 20th European Control Conference |
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Kurztitel | ECC 2022 |
Land/Gebiet | Großbritannien / Vereinigtes Königreich |
Ort | London, Hybrider Event |
Zeitraum | 12/07/22 → 15/07/22 |
Internetadresse |
ASJC Scopus subject areas
- Artificial intelligence
- Computernetzwerke und -kommunikation
- Informationssysteme und -management
- Steuerungs- und Systemtechnik
- Steuerung und Optimierung
- Modellierung und Simulation