arXiv subjects

Jürgen Adamy

Publications and source records attributed to Jürgen Adamy.

17 recordsLinked to original sources

arXiv2025

Hybrid Many-Objective Optimization in Probabilistic Mission Design for Compliant and Effective UAV Routing

Advanced Aerial Mobility encompasses many outstanding applications that promise to revolutionize modern logistics and pave the way for various public services and industry uses. However, throughout its history, the development of such systems has been impeded by the complexity of legal restrictions and physical constraints. While airspaces are often tightly shaped by various legal requirements, Unmanned Aerial Vehicles (UAV) must simultaneously consider, among others, energy demands, signal quality, and noise pollution. In this work, we address this challenge by presenting a novel architecture that integrates methods of Probabilistic Mission Design (ProMis) and Many-Objective Optimization for UAV routing. Hereby, our framework is able to comply with legal requirements under uncertainty while producing effective paths that minimize various physical costs a UAV needs to consider when traversing human-inhabited spaces. To this end, we combine hybrid probabilistic first-order logic for spatial reasoning with mixed deterministic-stochastic route optimization, incorporating physical objectives such as energy consumption and radio interference with a logical, probabilistic model of legal requirements. We demonstrate the versatility and advantages of our system in a large-scale empirical evaluation over real-world, crowd-sourced data from a map extract from the city of Paris, France, showing how a network of effective and compliant paths can be formed.

cs.RO↗

arXiv2025

Learning from Disengagements: An Analysis of Safety Driver Interventions during Remote Driving

This study investigates disengagements of Remote Driving Systems (RDS) based on interventions by an in-vehicle Safety Drivers (SD) in real-world Operational Design Domains (ODD) with a focus on Remote Driver (RD) performance during their driving training. Based on an analysis of over 14,000 km on remote driving data, the relationship between the driving experience of 25 RD and the frequency of disengagements is systematically investigated. The results show that the number of SD interventions decreases significantly within the first 400 km of driving experience, which illustrates a clear learning curve of the RD. In addition, the most common causes for 183 disengagements analyzed are identified and categorized, whereby four main scenarios for SD interventions were identified and illustrated. The results emphasize the need for experience-based and targeted training programs aimed at developing basic driving skills early on, thereby increasing the safety, controllability and efficiency of RDS, especially in complex urban environment ODDs.

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arXiv2025

Evaluation of Remote Driver Performance in Urban Environment Operational Design Domains

Remote driving has emerged as a solution for enabling human intervention in scenarios where Automated Driving Systems (ADS) face challenges, particularly in urban Operational Design Domains (ODDs). This study evaluates the performance of Remote Drivers (RDs) of passenger cars in a representative urban ODD in Las Vegas, focusing on the influence of cumulative driving experience and targeted training approaches. Using performance metrics such as efficiency, braking, acceleration, and steering, the study shows that driving experience can lead to noticeable improvements of RDs and demonstrates how experience up to 600 km correlates with improved vehicle control. In addition, driving efficiency exhibited a positive trend with increasing kilometers, particularly during the first 300 km of experience, which reaches a plateau from 400 km within a range of 0.35 to 0.42 km/min in the defined ODD. The research further compares ODD-specific training methods, where the detailed ODD training approaches attains notable advantages over other training approaches. The findings underscore the importance of tailored ODD training in enhancing RD performance, safety, and scalability for Remote Driving System (RDS) in real-world applications, while identifying opportunities for optimizing training protocols to address both routine and extreme scenarios. The study provides a robust foundation for advancing RDS deployment within urban environments, contributing to the development of scalable and safety-critical remote operation standards.

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arXiv2025

Identification and Classification of Human Performance related Challenges during Remote Driving

Remote driving of vehicles is gaining in importance in the transportation sector, especially when Automated Driving Systems (ADSs) reach the limits of their system boundaries. This study investigates the challenges faced by human Remote Drivers (RDs) during remote driving, particularly focusing on the identification and classification of human performance-related challenges through a comprehensive analysis of real-world remote driving data Las Vegas. For this purpose, a total of 183 RD performance-related Safety Driver (SD) interventions were analyzed and classified using an introduced severity classification. As it is essential to prevent the need for SD interventions, this study identified and analyzed harsh driving events to detect an increased likelihood of interventions by the SD. In addition, the results of the subjective RD questionnaire are used to evaluate whether the objective metrics from SD interventions and harsh driving events can also be confirmed by the RDs and whether additional challenges can be uncovered. The analysis reveals learning curves, showing a significant decrease in SD interventions as RD experience increases. Early phases of remote driving experience, especially below 200 km of experience, showed the highest frequency of safety-related events, including braking late for traffic signs and responding impatiently to other traffic participants. Over time, RDs follow defined rules for improving their control, with experience leading to less harsh braking, acceleration, and steering maneuvers. The study contributes to understanding the requirements of RDS, emphasizing the importance of targeted training to address human performance limitations. It further highlights the need for system improvements to address challenges like latency and the limited haptic feedback replaced by visual feedback, which affect the RDs' perception and vehicle control.

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arXiv2024

Evaluating the Impact of Data Availability on Machine Learning-augmented MPC for a Building Energy Management System

A major challenge in the development of Model Predictive Control (MPC)-based energy management systems (EMSs) for buildings is the availability of an accurate model. One approach to address this is to augment an existing gray-box model with data-driven residual estimators. The efficacy of such estimators, and hence the performance of the EMS, relies on the availability of sufficient and suitable training data. In this work, we evaluate how different data availability scenarios affect estimator and controller performance. To do this, we perform software-in-the-loop (SiL) simulation with a physics-based digital twin using real measurement data. Simulation results show that acceptable estimation and control performance can already be achieved with limited available data, and we confirm that leveraging historical data for pretraining boosts efficacy.

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arXiv2023

Online and Predictive Warning System for Forced Lane Changes using Risk Maps

The survival analysis of driving trajectories allows for holistic evaluations of car-related risks caused by collisions or curvy roads. This analysis has advantages over common Time-To-X indicators, such as its predictive and probabilistic nature. However, so far, the theoretical risks have not been demonstrated in real-world environments. In this paper, we therefore present Risk Maps (RM) for online warning support in situations with forced lane changes, due to the end of roads. For this purpose, we first unify sensor data in a Relational Local Dynamic Map (R-LDM). RM is afterwards able to be run in real-time and efficiently probes a range of situations in order to determine risk-minimizing behaviors. Hereby, we focus on the improvement of uncertainty-awareness and transparency of the system. Risk, utility and comfort costs are included in a single formula and are intuitively visualized to the driver. In the conducted experiments, a low-cost sensor setup with a GNSS receiver for localization and multiple cameras for object detection are leveraged. The final system is successfully applied on two-lane roads and recommends lane change advices, which are separated in gap and no-gap indications. These results are promising and present an important step towards interpretable safety.

cs.RO↗

arXiv2022

Projected gradient-tracking in multi-cluster games and its application to power management

We are concerned with a distributed approach to solve multi-cluster games arising in multi-agent systems. In such games, agents are separated into distinct clusters. The agents belonging to the same cluster cooperate with each other to achieve a common cluster goal while a non-cooperative game is played between the clusters. To be able to deal with the sparsity of information, as each agent only knows a specific part of the problem, we combine gradient-tracking and consensus methods for information distribution into an algorithm that can solve both the cooperative and non-cooperative problem in a single run. The constraints of the problem are taken into account by the corresponding projection operators and linear convergence is proven given an appropriate constant step size. The algorithm is applied to a day-ahead power management problem, posed as a multi-cluster game, and its efficiency is demonstrated by simulations.

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arXiv2021

Theory of Mind Based Assistive Communication in Complex Human Robot Cooperation

When cooperating with a human, a robot should not only care about its environment and task but also develop an understanding of the partner's reasoning. To support its human partner in complex tasks, the robot can share information that it knows. However simply communicating everything will annoy and distract humans since they might already be aware of and not all information is relevant in the current situation. The questions when and what type of information the human needs, are addressed through the concept of Theory of Mind based Communication which selects information sharing actions based on evaluation of relevance and an estimation of human beliefs. We integrate this into a communication assistant to support humans in a cooperative setting and evaluate performance benefits. We designed a human robot Sushi making task that is challenging for the human and generates different situations where humans are unaware and communication could be beneficial. We evaluate the influence of the human centric communication concept on performance with a user study. Compared to the condition without information exchange, assisted participants can recover from unawareness much earlier. The approach respects the costs of communication and balances interruptions better than other approaches. By providing information adapted to specific situations, the robot does not instruct but enable the human to make good decision.

cs.RO↗

arXiv2021

Gradient Play in $n$-Cluster Games with Zero-Order Information

We study a distributed approach for seeking a Nash equilibrium in $n$-cluster games with strictly monotone mappings. Each player within each cluster has access to the current value of her own smooth local cost function estimated by a zero-order oracle at some query point. We assume the agents to be able to communicate with their neighbors in the same cluster over some undirected graph. The goal of the agents in the cluster is to minimize their collective cost. This cost depends, however, on actions of agents from other clusters. Thus, a game between the clusters is to be solved. We present a distributed gradient play algorithm for determining a Nash equilibrium in this game. The algorithm takes into account the communication settings and zero-order information under consideration. We prove almost sure convergence of this algorithm to a Nash equilibrium given appropriate estimations of the local cost functions' gradients.

cs.GT↗

arXiv2021

Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games

We are concerned with finding Nash Equilibria in agent-based multi-cluster games, where agents are separated into distinct clusters. While the agents inside each cluster collaborate to achieve a common goal, the clusters are considered to be virtual players that compete against each other in a non-cooperative game with respect to a coupled cost function. In such scenarios, the inner-cluster problem and the game between the clusters need to be solved simultaneously. Therefore, the resulting inter-cluster Nash Equilibrium should also be a minimizer of the social welfare problem inside the clusters. In this work, this setup is cast as a distributed optimization problem with sparse state information. Hence, critical information, such as the agent's cost functions, remain private. We present a distributed algorithm that converges with a linear rate to the optimal solution. Furthermore, we apply our algorithm to an extended cournot game to verify our theoretical results.

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arXiv2020

Improved Multi-Source Domain Adaptation by Preservation of Factors

Domain Adaptation (DA) is a highly relevant research topic when it comes to image classification with deep neural networks. Combining multiple source domains in a sophisticated way to optimize a classification model can improve the generalization to a target domain. Here, the difference in data distributions of source and target image datasets plays a major role. In this paper, we describe based on a theory of visual factors how real-world scenes appear in images in general and how recent DA datasets are composed of such. We show that different domains can be described by a set of so called domain factors, whose values are consistent within a domain, but can change across domains. Many DA approaches try to remove all domain factors from the feature representation to be domain invariant. In this paper we show that this can lead to negative transfer since task-informative factors can get lost as well. To address this, we propose Factor-Preserving DA (FP-DA), a method to train a deep adversarial unsupervised DA model, which is able to preserve specific task relevant factors in a multi-domain scenario. We demonstrate on CORe50, a dataset with many domains, how such factors can be identified by standard one-to-one transfer experiments between single domains combined with PCA. By applying FP-DA, we show that the highest average and minimum performance can be achieved.

cs.CV↗

arXiv2020

Revisiting Consensus-Based Energy-Management in Smart Grid with Transmission Losses and Directed Communication

We discovered a deficiency in Algorithm 1 and Theorem 3 of [1]. The algorithm called CEMA aims to solve an energy management problem distributively. However, by means of a counter example, we show that Theorem 2 and 3 of [1] contradict each other in the case of a valid scenario, proving that the suggested algorithm does not always find the optimum. Furthermore, we provide theoretic results, showing that Theorem 3 of [1] does not hold generally. At last, we provide a rectification by adjusting the algorithm and the corresponding proof of Theorem 3.

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arXiv2020

Projected Push-Sum Gradient Descent-Ascent for Convex Optimizationwith Application to Economic Dispatch Problems

We propose a novel algorithm for solving convex, constrained and distributed optimization problems defined on multi-agent-networks, where each agent has exclusive access to a part of the global objective function. The agents are able to exchange information over a directed, weighted communication graph, which can be represented as a column-stochastic matrix. The algorithm combines an adjusted push-sum consensus protocol for information diffusion and a gradient descent-ascent on the local cost functions, providing convergence to the optimum of their sum. We provide results on a reformulation of the push-sum into single matrix-updates and prove convergence of the proposed algorithm to an optimal solution, given standard assumptions in distributed optimization. The algorithm is applied to a distributed economic dispatch problem, in which the constraints can be expressed in local and global subsets.

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arXiv2019

Optimal Output Regulation for Square, Over-Actuated and Under-Actuated Linear Systems

This paper considers two different problems in trajectory tracking control for linear systems. First, if the control is not unique which is most input energy efficient. Second, if exact tracking is infeasible which control performs most accurately. These are typical challenges for over-actuated systems and for under-actuated systems, respectively. We formulate both goals as optimal output regulation problems. Then we contribute two new sets of regulator equations to output regulation theory that provide the desired solutions. A thorough study indicates solvability and uniqueness under weak assumptions. E.g., we can always determine the solution of the classical regulator equations that is most input energy efficient. This is of great value if there are infinitely many solutions. We derive our results by a linear quadratic tracking approach and establish a useful link to output regulation theory.

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arXiv2019

Train-borne Localization Exploiting Track-Geometry Constraints -- A Practical Evaluation

Today's railway signalling system heavily relies on trackside infrastructure such as axle counters and track balises. This system has proven itself to be reliable, however, it is not very efficient and, moreover, very costly. Thus, it is not suited to overcome the future challenges in railway transportation. For this reason, signalling systems based on train-borne sensors have gained interest recently. In this context, train-borne localization is one of the main research challenges. So far there is no sensor set-up which meets the demanding requirements for a localization system, both in the sense of accuracy as well as safety. To help overcome these issues in the near future we present our latest research results here. Earlier we published a localization algorithm which is characterized by an increased accuracy in cross-track direction compared to a standard Kalman filter (KF) approach, as has been shown in simulations [1]. To verify these results practically, we recorded data from a Global Navigation Satellite System (GNSS) and an inertial measurement unit (IMU) on a test drive. The localization accuracy is evaluated with the help of OpenStreetMap (OSM) data and site plans. Furthermore, we evaluate the quality of the estimated geometric track-map, which is additionally provided in the process of the localization algorithm [2]. We conclude with some remarks on the research challenges towards train-borne localization and suggest further steps to overcome them.

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arXiv2019

Generating Compact Geometric Track-Maps for Train Positioning Applications

In this paper, we present a method to generate compact geometric track-maps for train-borne localization applications. Therefore, we first give a brief overview on the purpose of track maps in train-positioning applications. It becomes apparent that there are hardly any adequate methods to generate suitable geometric track-maps. This is why we present a novel map generation procedure. It uses an optimization formulation to find the continuous sequence of track geometries that fits the available measurement data best. The optimization is initialized with the results from a localization filter developed in our previous work. The localization filter also provides the required information for shape identification and measurement association. The presented approach will be evaluated on simulated data as well as on real measurements.

cs.LG↗

arXiv2018

Optimal Stationary Synchronization of Heterogeneous Linear Multi-Agent Systems

In this paper, we address the output synchronization of heterogeneous linear networks. In the literature, all agents are typically required to synchronize exactly to a common trajectory. Here, we introduce optimal stationary synchronization (OSS) instead which permits non-zero steady-state synchronization errors. As a benefit, we are able to relax standard requirements. E.g., agents are allowed to participate in the network even when they usually cannot synchronize exactly. In addition, OSS enables agents to save input-energy by synchronizing within tolerable error-bounds. Our new method combines the synchronization of bounded exosystems with local infinite-time linear quadratic tracking (LQT). This results in an optimal balance of each agent's synchronization error versus its consumed input-energy. Moreover, we extend recent results in LQT such that the derived time-invariant optimal control guarantees that the synchronization error satisfies given strict bounds. All these aspects are demonstrated by an illustrative simulation example with a detailed analysis.

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