18 citations · 52 across the 16 of their papers we have counts for
17 papers
Inference-Time Attention Steering for Vision-Language-Action Driving Models
Darshan Nagendra Prasad, Lars Ullrich, Knut Graichen
Vision-language-action (VLA) driving models couple a reasoning stage with a diffusion-based trajectory decoder, but do not give a direct way to redirect attention toward safety-cri…
Asynchronous Sensitivity-Based Distributed NMPC
Maximilian Pierer von Esch, Andres Völz, Knut Graichen
This paper presents a cooperative distributed model predictive control (MPC) scheme for nonlinear continuous-time systems. The centralized optimal control problem is solved asynchr…
Toward Fully Autonomous Driving: AI, Challenges, Opportunities, and Needs
Lars Ullrich, Michael Buchholz, Klaus Dietmayer +1
Automated driving (AD) is promising, but the transition to fully autonomous driving is, among other things, subject to the real, ever-changing open world and the resulting challeng…
An Overview of Sensitivity-Based Distributed Optimization and Model Predictive Control
Maximilian Pierer von Esch, Andreas Völz, Knut Graichen
This paper presents a concise overview of sensitivity-based methods for solving large-scale optimization problems in distributed fashion. The approach relies on sensitivities and p…
Enforcing Convergence in Sensitivity-based Distributed Programming via Transformed Primal-Dual Updates
Maximilian Pierer von Esch, Andreas Völz, Knut Graichen
Sensitivity-based distributed programming (SBDP) is a decomposition method for solving large-scale nonlinear programs over graph-structured networks. However, its convergence depen…
A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differences
Lars Ullrich, Michael Buchholz, Jonathan Petit +2
Efficient scalability of automated driving (AD) is key to reducing costs, enhancing safety, conserving resources, and maximizing impact. However, research focuses on specific vehic…
A New Perspective On AI Safety Through Control Theory Methodologies
Lars Ullrich, Walter Zimmer, Ross Greer +3
While artificial intelligence (AI) is advancing rapidly and mastering increasingly complex problems with astonishing performance, the safety assurance of such systems is a major co…
Enhancing System Self-Awareness and Trust of AI: A Case Study in Trajectory Prediction and Planning
Lars Ullrich, Zurab Mujirishvili, Knut Graichen
In the trajectory planning of automated driving, data-driven statistical artificial intelligence (AI) methods are increasingly established for predicting the emergent behavior of o…
AI Safety Assurance for Automated Vehicles: A Survey on Research, Standardization, Regulation
Lars Ullrich, Michael Buchholz, Klaus Dietmayer +1
Assuring safety of artificial intelligence (AI) applied to safety-critical systems is of paramount importance. Especially since research in the field of automated driving shows tha…
Sensitivity-Based Distributed Programming for Non-Convex Optimization
Maximilian Pierer von Esch, Andreas Völz, Knut Graichen
This paper presents a novel sensitivity-based distributed programming (SBDP) approach for non-convex, large-scale nonlinear programs (NLP). The algorithm relies on first-order sens…
Expanding the Classical V-Model for the Development of Complex Systems Incorporating AI
Lars Ullrich, Michael Buchholz, Klaus Dietmayer +1
Research in the field of automated vehicles, or more generally cognitive cyber-physical systems that operate in the real world, is leading to increasingly complex systems. Among ot…
A software framework for stochastic model predictive control of nonlinear continuous-time systems (GRAMPC-S)
Daniel Landgraf, Andreas Völz, Knut Graichen
This paper presents the open-source stochastic model predictive control framework GRAMPC-S for nonlinear uncertain systems with chance constraints. It provides several uncertainty…
Robust Meta-Learning of Vehicle Yaw Rate Dynamics via Conditional Neural Processes
Lars Ullrich, Andreas Völz, Knut Graichen
Trajectory planners of autonomous vehicles usually rely on physical models to predict the vehicle behavior. However, despite their suitability, physical models have some shortcomin…
Sensitivity-Based Distributed Model Predictive Control for Nonlinear Systems under Inexact Optimization
Maximilian Pierer von Esch, Andreas Völz, Knut Graichen
This paper presents a distributed model predictive control (DMPC) scheme for nonlinear continuous-time systems. The underlying distributed optimal control problem is cooperatively…
Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows
Georg Rabenstein, Lars Ullrich, Knut Graichen
Alongside optimization-based planners, sampling-based approaches are often used in trajectory planning for autonomous driving due to their simplicity. Model predictive path integra…
Transfer Learning Study of Motion Transformer-based Trajectory Predictions
Lars Ullrich, Alex McMaster, Knut Graichen
Trajectory planning in autonomous driving is highly dependent on predicting the emergent behavior of other road users. Learning-based methods are currently showing impressive resul…
Concept for an Automatic Annotation of Automotive Radar Data Using AI-segmented Aerial Camera Images
Marcel Hoffmann, Sandro Braun, Oliver Sura +4
This paper presents an approach to automatically annotate automotive radar data with AI-segmented aerial camera images. For this, the images of an unmanned aerial vehicle (UAV) abo…