Search Results for author: Marion Leibold

Found 19 papers, 1 papers with code

Adaptive Stochastic Predictive Control from Noisy Data: A Sampling-based Approach

no code implementations3 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.

Safe and Non-Conservative Trajectory Planning for Autonomous Driving Handling Unanticipated Behaviors of Traffic Participants

1 code implementation19 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.

Autonomous Driving Model Predictive Control +1

Minimal Constraint Violation Probability in Model Predictive Control for Linear Systems

no code implementations16 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.

Model Predictive Control

Combining Belief Function Theory and Stochastic Model Predictive Control for Multi-Modal Uncertainty in Autonomous Driving

no code implementations1 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.

Autonomous Driving Collision Avoidance +2

Sampling-based Stochastic Data-driven Predictive Control under Data Uncertainty

no code implementations1 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.

LEMMA

Optimal Control for Indoor Vertical Farms Based on Crop Growth

no code implementations14 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.

Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive Controlled Vehicles by Inverse Reinforcement Learning

no code implementations23 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.

Autonomous Driving Model Predictive Control

Offline Uncertainty Sampling in Data-driven Stochastic MPC

no code implementations6 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.

Safe Stochastic Model Predictive Control

no code implementations13 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.

Model Predictive Control

Data-driven tube-based stochastic predictive control

no code implementations8 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.

LEMMA Model Predictive Control

Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning

no code implementations10 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.

reinforcement-learning Reinforcement Learning +2

Multistage Stochastic Model Predictive Control for Urban Automated Driving

no code implementations1 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.

Model Predictive Control Motion Planning +1

Grid-Based Stochastic Model Predictive Control for Trajectory Planning in Uncertain Environments

no code implementations24 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.

Autonomous Driving Model Predictive Control +1

Minimization of Constraint Violation Probability in Model Predictive Control

no code implementations3 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.

Model Predictive Control

Combined Robust and Stochastic Model Predictive Control for Models of Different Granularity

no code implementations14 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.

Collision Avoidance Model Predictive Control

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