@inproceedings{Reiter2023Hierarchical,

title = {A Hierarchical Approach for Strategic Motion Planning in Autonomous Racing},

author = {Rudolf Reiter and Jasper Hoffman and Joschka Boedecker and Moritz Diehl},

doi = {10.23919/ECC57647.2023.10178143},

isbn = {978-3-907144-08-4},

year  = {2023},

date = {2023-07-17},

urldate = {2023-07-17},

booktitle = {2023 European Control Conference (ECC)},

pages = {1-8},

publisher = {IEEE},

address = {Bucharest, Romania},

abstract = {We present an approach for safe trajectory planning, where a strategic task related to autonomous racing is learned sample efficiently within a simulation environment. A high-level policy, represented as a neural network, outputs a reward specification that is used within the function of a parametric nonlinear model predictive controller. By including constraints and vehicle kinematics in the nonlinear program, we can guarantee safe and feasible trajectories related to the used model. Compared to classical reinforcement learning, our approach restricts the exploration to safe trajectories, starts with an excellent prior performance and yields complete trajectories that can be passed to a tracking lowest-level controller. We do not address the lowest-level controller in this work and assume perfect tracking of feasible trajectories. We show the superior performance of our algorithm on simulated racing tasks that include high-level decision-making. The vehicle learns to efficiently overtake slower vehicles and avoids getting overtaken by blocking faster ones.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Baumgärtner2022MHE,

title = {Moving Horizon Estimation with Adaptive Regularization for Ill-Posed State and Parameter Estimation Problems},

author = {Katrin Baumgärtner and Rudolf Reiter and Moritz Diehl},

doi = {10.1109/CDC51059.2022.9993416},

isbn = {978-1-6654-6761-2},

year  = {2023},

date = {2023-01-10},

urldate = {2023-01-10},

booktitle = {2022 IEEE 61st Conference on Decision and Control (CDC)},

pages = {2165-2171},

publisher = {IEEE},

address = {Cancun, Mexico},

abstract = {We investigate the usage of Moving Horizon Estimation (MHE) for state and parameter estimation for partially non-detectable systems with measurements corrupted by outliers. We propose an arrival cost update formula based on the Generalized Gauss-Newton method and illustrate how it can be generalized to nonconvex loss functions that can be effectively used for outlier rejection. Moreover, we propose an adaptive regularization scheme for the arrival cost which introduces forgetting as well as additional pseudo-measurements to the arrival cost update. We illustrate the performance of the proposed algorithms on a longitudinal vehicle state and parameter estimation problem.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{reiter_inverse_2022,

title = {An Inverse Optimal Control Approach for Trajectory Prediction of Autonomous Race Cars},

author = {Rudolf Reiter and Florian Messerer and Markus Schratter and Daniel Watzenig and Moritz Diehl},

url = {https://ieeexplore.ieee.org/document/9838100/},

doi = {10.23919/ECC55457.2022.9838100},

isbn = {978-3-907144-07-7},

year  = {2022},

date = {2022-07-01},

urldate = {2022-09-09},

booktitle = {2022 European Control Conference (ECC)},

pages = {146--153},

publisher = {IEEE},

address = {London, United Kingdom},

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.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}