arXiv subjects

Tobias Schürmann

Publications and source records attributed to Tobias Schürmann.

5 recordsLinked to original sources

arXiv2026

Generative AI in Systems Engineering: A Framework for Risk Assessment of Large Language Models

The increasing use of Large Language Models (LLMs) offers significant opportunities across the engineering lifecycle, including requirements engineering, software development, process optimization, and decision support. Despite this potential, organizations face substantial challenges in assessing the risks associated with LLM use, resulting in inconsistent integration, unknown failure modes, and limited scalability. This paper introduces the LLM Risk Assessment Framework (LRF), a structured approach for evaluating the application of LLMs within Systems Engineering (SE) environments. The framework classifies LLM-based applications along two fundamental dimensions: autonomy, ranging from supportive assistance to fully automated decision making, and impact, reflecting the potential severity of incorrect or misleading model outputs on engineering processes and system elements. By combining these dimensions, the LRF enables consistent determination of corresponding risk levels across the development lifecycle. The resulting classification supports organizations in identifying appropriate validation strategies, levels of human oversight, and required countermeasures to ensure safe and transparent deployment. The framework thereby helps align the rapid evolution of AI technologies with established engineering principles of reliability, traceability, and controlled process integration. Overall, the LRF provides a basis for risk-aware adoption of LLMs in complex engineering environments and represents a first step toward standardized AI assurance practices in systems engineering.

cs.SE↗

arXiv2025

Structuring Automotive Data for Systems Engineering: A Taxonomy-Based Approach

Vehicle data is essential for advancing data-driven development throughout the automotive lifecycle, including requirements engineering, design, verification, and validation, and post-deployment optimization. Developers currently collect data in a decentralized and fragmented manner across simulations, test benches, and real-world driving, resulting in data silos, inconsistent formats, and limited interoperability. This leads to redundant efforts, inefficient integration, and suboptimal use of data. This fragmentation results in data silos, inconsistent storage structures, and limited interoperability, leading to redundant data collection, inefficient integration, and suboptimal application. To address these challenges, this article presents a structured literature review and develops an inductive taxonomy for automotive data. This taxonomy categorizes data according to its sources and applications, improving data accessibility and utilization. The analysis reveals a growing emphasis on real-world driving and machine learning applications while highlighting a critical gap in data availability for requirements engineering. By providing a systematic framework for structuring automotive data, this research contributes to more efficient data management and improved decision-making in the automotive industry.

eess.SY↗

arXiv2025

Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review

Despite advancements in perception and planning for autonomous vehicles (AVs), validating their performance remains a significant challenge. The deployment of planning algorithms in real-world environments is often ineffective due to discrepancies between simulations and real traffic conditions. Evaluating AVs planning algorithms in simulation typically involves replaying driving logs from recorded real-world traffic. However, entities replayed from offline data are not reactive, lack the ability to respond to arbitrary AV behavior, and cannot behave in an adversarial manner to test certain properties of the driving policy. Therefore, simulation with realistic and potentially adversarial entities represents a critical task for AV planning software validation. In this work, we aim to review current research efforts in the field of traffic simulation, focusing on the application of advanced techniques for modeling realistic and adversarial behaviors of traffic entities. The objective of this work is to categorize existing approaches based on the proposed classes of traffic entity behavior and scenario behavior control. Moreover, we collect traffic datasets and examine existing traffic simulations with respect to their employed default traffic entities. Finally, we identify challenges and open questions that hold potential for future research.

cs.RO↗

arXiv2024

Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning

Mobile robots are being used on a large scale in various crowded situations and become part of our society. The socially acceptable navigation behavior of a mobile robot with individual human consideration is an essential requirement for scalable applications and human acceptance. Deep Reinforcement Learning (DRL) approaches are recently used to learn a robot's navigation policy and to model the complex interactions between robots and humans. We propose to divide existing DRL-based navigation approaches based on the robot's exhibited social behavior and distinguish between social collision avoidance with a lack of social behavior and socially aware approaches with explicit predefined social behavior. In addition, we propose a novel socially integrated navigation approach where the robot's social behavior is adaptive and emerges from the interaction with humans. The formulation of our approach is derived from a sociological definition, which states that social acting is oriented toward the acting of others. The DRL policy is trained in an environment where other agents interact socially integrated and reward the robot's behavior individually. The simulation results indicate that the proposed socially integrated navigation approach outperforms a socially aware approach in terms of ego navigation performance while significantly reducing the negative impact on all agents within the environment.

cs.RO↗

arXiv2024

ReACT: Reinforcement Learning for Controller Parametrization using B-Spline Geometries

Robust and performant controllers are essential for industrial applications. However, deriving controller parameters for complex and nonlinear systems is challenging and time-consuming. To facilitate automatic controller parametrization, this work presents a novel approach using deep reinforcement learning (DRL) with N-dimensional B-spline geometries (BSGs). We focus on the control of parameter-variant systems, a class of systems with complex behavior which depends on the operating conditions. For this system class, gain-scheduling control structures are widely used in applications across industries due to well-known design principles. Facilitating the expensive controller parametrization task regarding these control structures, we deploy an DRL agent. Based on control system observations, the agent autonomously decides how to adapt the controller parameters. We make the adaptation process more efficient by introducing BSGs to map the controller parameters which may depend on numerous operating conditions. To preprocess time-series data and extract a fixed-length feature vector, we use a long short-term memory (LSTM) neural networks. Furthermore, this work contributes actor regularizations that are relevant to real-world environments which differ from training. Accordingly, we apply dropout layer normalization to the actor and critic networks of the truncated quantile critic (TQC) algorithm. To show our approach's working principle and effectiveness, we train and evaluate the DRL agent on the parametrization task of an industrial control structure with parameter lookup tables.

cs.LG↗