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.
Original language | English |
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Title of host publication | 2022 European Control Conference, ECC 2022 |
Publisher | IEEE |
Pages | 146-153 |
Number of pages | 8 |
ISBN (Electronic) | 9783907144077 |
DOIs | |
Publication status | Published - 2022 |
Event | 20th European Control Conference: ECC22 - London, Hybrider Event, United Kingdom Duration: 12 Jul 2022 → 15 Jul 2022 https://ecc22.euca-ecc.org/ |
Conference
Conference | 20th European Control Conference |
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Abbreviated title | ECC 2022 |
Country/Territory | United Kingdom |
City | London, Hybrider Event |
Period | 12/07/22 → 15/07/22 |
Internet address |
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems and Management
- Control and Systems Engineering
- Control and Optimization
- Modelling and Simulation