no code implementations • 3 Sep 2024 • Johannes Teutsch, Christopher Narr, Sebastian Kerz, Dirk Wollherr, Marion Leibold
This prior knowledge is used to construct an initial set of data-consistent system parameters and a distribution that allows for sample generation.
1 code implementation • 19 Jun 2024 • Tommaso Benciolini, Michael Fink, Nehir Güzelkaya, Dirk Wollherr, Marion Leibold
Trajectory planning for autonomous driving is challenging because the unknown future motion of traffic participants must be accounted for, yielding large uncertainty.
no code implementations • 16 Feb 2024 • Michael Fink, Tim Brüdigam, Dirk Wollherr, Marion Leibold
This is achieved by first determining a set of inputs that minimize the probability of constraint violation.
no code implementations • 1 Feb 2024 • Tommaso Benciolini, Yuntian Yan, Dirk Wollherr, Marion Leibold
Therefore, the measure of reliability of the estimation provided by Belief Function Theory is used in the design of collision-avoidance safety constraints, in particular to increase safety when the intention of traffic participants is not clear.
no code implementations • 1 Feb 2024 • Johannes Teutsch, Sebastian Kerz, Dirk Wollherr, Marion Leibold
We present a stochastic constrained output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances.
no code implementations • 3 Nov 2023 • Tommaso Benciolini, Chen Tang, Marion Leibold, Catherine Weaver, Masayoshi Tomizuka, Wei Zhan
In the exploration, a MPC collects diverse data by balancing the racing objectives and the exploration criterion; then the GP is re-trained.
no code implementations • 14 Sep 2023 • Annalena Daniels, Michael Fink, Marion Leibold, Dirk Wollherr, Senthold Asseng
Vertical farming allows for year-round cultivation of a variety of crops, overcoming environmental limitations and ensuring food security.
no code implementations • 23 Aug 2023 • Ni Dang, Tao Shi, Zengjie Zhang, Wanxin Jin, Marion Leibold, Martin Buss
Nevertheless, an important indicator of the driving style, i. e., how an AV reacts to its nearby AVs, is not fully incorporated in the feature design of previous ME-IRL methods.
no code implementations • 6 Apr 2023 • Johannes Teutsch, Sebastian Kerz, Tim Brüdigam, Dirk Wollherr, Marion Leibold
In this work, we exploit an offline-sampling based strategy for the constrained data-driven predictive control of an unknown linear system subject to random measurement noise.
no code implementations • 13 Apr 2022 • Tim Brüdigam, Robert Jacumet, Dirk Wollherr, Marion Leibold
In this work, we propose a safety algorithm that is compatible with any stochastic Model Predictive Control method for linear systems with additive uncertainty and polytopic constraints.
no code implementations • 8 Dec 2021 • Sebastian Kerz, Johannes Teutsch, Tim Brüdigam, Dirk Wollherr, Marion Leibold
A powerful result from behavioral systems theory known as the fundamental lemma allows for predictive control akin to Model Predictive Control (MPC) for linear time invariant (LTI) systems with unknown dynamics purely from data.
no code implementations • 10 Sep 2021 • Zhehua Zhou, Ozgur S. Oguz, Yi Ren, Marion Leibold, Martin Buss
Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process.
no code implementations • 18 Aug 2021 • Tim Brüdigam, Daniel Prader, Dirk Wollherr, Marion Leibold
Horizon length and model accuracy are defining factors when designing a Model Predictive Controller.
no code implementations • 1 Jul 2021 • Tommaso Benciolini, Tim Brüdigam, Marion Leibold
For motion optimization, we propose to use a two-stage hierarchical structure that plans the trajectory and the maneuver separately.
no code implementations • 15 Jun 2021 • Tim Brüdigam, Jie Zhan, Dirk Wollherr, Marion Leibold
Model Predictive Control (MPC) has shown to be a successful method for many applications that require control.
no code implementations • 20 Sep 2020 • Tim Brüdigam, Michael Olbrich, Dirk Wollherr, Marion Leibold
Automated vehicles require efficient and safe planning to maneuver in uncertain environments.
no code implementations • 24 Jul 2020 • Tim Brüdigam, Fulvio di Luzio, Lucia Pallottino, Dirk Wollherr, Marion Leibold
Then, the probabilistic grid is transformed into a binary grid of admissible and inadmissible cells by applying a threshold, representing a risk parameter.
no code implementations • 3 Jun 2020 • Tim Brüdigam, Victor Gaßmann, Dirk Wollherr, Marion Leibold
We propose a novel Model Predictive Control scheme that yields a solution with minimal constraint violation probability for a norm constraint in an environment with uncertainty.
no code implementations • 14 Mar 2020 • Tim Brüdigam, Johannes Teutsch, Dirk Wollherr, Marion Leibold
We therefore propose combining RMPC on a detailed model for short-term predictions and Stochastic MPC (SMPC), with chance constraints, on a simplified model for long-term predictions.