arXiv subjects

Lutz Eckstein

Publications and source records attributed to Lutz Eckstein.

At least 19 recordsLinked to original sources

arXiv2026

Scalable Distributed Simulation-Based Testing for Automated Driving Systems

Virtual scenario-based testing is a key enabler for validating automated driving systems (ADS) and intelligent transport systems (ITS). However, executing large-scale test suites involving possibly thousands of scenarios remains labor-intensive and difficult to scale. This paper presents an end-to-end, DevOps-driven framework that automates build, deployment, and distributed execution of CARLA-based scenario tests of an ADS on a lightweight Kubernetes cluster. ROS 2 applications are packaged as standardized Kubernetes Helm charts generated from repository specifications, while entire simulation environments are composed declaratively via dynamic Helmfile manifests. The paper describes how a distributed testing workflow can be implemented in Argo Workflows to provision environments, aggregate and batch OpenSCENARIO test cases from configurable sources, execute scenarios in parallel across cluster nodes, and collect logs and resource metrics. In an evaluation on a multi-node K3s cluster running 200 scenarios, the best configuration speeds up end-to-end workflow time by more than a factor of eight compared to a sequential baseline. The results demonstrate significant gains in end-to-end execution time and quantify trade-offs between parallelism, orchestration overhead, and cluster stability. The framework is further demonstrated in a real-world ADS test application with connections to scenario sources and downstream evaluation modules. This demonstrates that the approach provides a strong foundation not only for scalable simulation testing, but also for generating traceable evidence that can support safety arguments.

cs.RO↗

arXiv2026

Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment

Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially in complex environments. However, their complex nature and lack of transparency can hinder explainability and trustworthiness and complicate safety assurance. Motivated by these challenges, we propose a hybrid planning architecture that combines the advantages of machine learning with the verifiability and the determinism of classical approaches. Specifically, we developed a deep neural network to interpret complex traffic scenes and propose driving behavior, while an optimization-based supervision layer validates this proposal and enforces explicit drivability and safety constraints. We evaluate the learned planner's driving behavior in open-loop studies on real-world urban data, discuss system integration aspects for stable closed-loop operation, and report results from real-world deployment on our research vehicle karl..

cs.RO↗

arXiv2026

karl. -- A Research Vehicle for Automated and Connected Driving

As highly automated driving is transitioning from single-vehicle closed-access testing to commercial deployments of public ride-hailing in selected areas (e.g., Waymo), automated driving and connected cooperative intelligent transport systems (C-ITS) remain active fields of research. Even though simulation is omnipresent in the development and validation life cycle of automated and connected driving technology, the complex nature of public road traffic and software that masters it still requires real-world integration and testing with actual vehicles. Dedicated vehicles for research and development allow testing and validation of software and hardware components under real-world conditions early on. They also enable collecting and publishing real-world datasets that let others conduct research without vehicle access, and support early demonstration of futuristic use cases. In this paper, we present karl., our new research vehicle for automated and connected driving. Apart from major corporations, few institutions worldwide have access to their own L4-capable research vehicles, restricting their ability to carry out independent research. This paper aims to help bridge that gap by sharing the reasoning, design choices, and technical details that went into making karl. a flexible and powerful platform for research, engineering, and validation in the context of automated and connected driving. More impressions of karl. are available at https://karl.ac.

cs.AR↗

arXiv2026

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment

Deep Neural Networks have become the dominant solution for Autonomous Driving perception, but their opacity conflicts with emerging Trustworthy AI guidelines and complicates safety assurance, debugging, and human oversight. While theoretical frameworks for safe and Explainable AI (XAI) exist, concrete implementations of Trustworthy AI for 3D scene understanding remain scarce. We address this gap by proposing a Trustworthy AI perception module that is remarkably robust, integrates faithful explainability, and calibrated uncertainty estimates. Building on a transformer-based detector, we derive explanation from the attention mechanism at inference time and validate their faithfulness using perturbation-based consistency tests. We further integrate an uncertainty estimation and calibration module, and apply robustness-enhancing training methods. Experiments show faithful saliency behavior, improved robustness, and well-calibrated uncertainty estimates. Finally, we deploy these Trustworthy AI elements in a prototype vehicle and provide an XAI Interface that visualizes documentation artifacts, model uncertainty state, and saliency maps, demonstrating the feasibility of trustworthy perception monitoring in real time. Supplementary materials are available at https://tillbeemelmanns.github.io/trustworthy_ai/ .

cs.RO↗

arXiv2026

Cloud-Native Operation of Roadside Infrastructure Enabling Demand-Driven Collective Perception via V2X

Intelligent roadside infrastructure is a key enabler for cooperative intelligent transport systems (C-ITS), supporting vehicles equipped with automated driving systems (ADS), e.g., through enhanced environment perception. With a growing number and an expanding functional scope of roadside units, scalable and efficient operation becomes a challenge. This paper presents a cloud-native architecture for the operation of distributed roadside infrastructure based on a Kubernetes cluster spanning roadside units and a cloud server. Building on this architecture, a demand-driven orchestration approach is implemented to dynamically deploy resource-intensive services only when required. As a representative use case, a V2X-based collective perception application is deployed on-demand when a connected vehicle is nearby. The approach is validated in a real-world experiment in our test field in Aachen, demonstrating that the collective perception application starts in time for the vehicle to benefit from it. Without any demand, the application remains inactive, reducing energy consumption, channel congestion, and hardware wear. Beyond the primary evaluation, V2X recordings from the test field are analyzed to estimate the energy-saving potential of demand-driven operation. In summary, the results demonstrate the practical feasibility of cloud-native, demand-driven operation of roadside infrastructure and indicate its potential to improve scalability and (energy) efficiency in future C-ITS deployments.

cs.DC↗

arXiv2026

Integration of an Agent Model into an Open Simulation Architecture for Scenario-Based Testing of Automated Vehicles

Simulative and scenario-based testing are crucial methods in the safety assurance for automated driving systems. To ensure that simulation results are reliable, the real world must be modeled with sufficient fidelity, including not only the static environment but also the surrounding traffic of a vehicle under test. Thus, the availability of traffic agent models is of common interest to model naturalistic and parameterizable behavior, similar to human drivers. The interchangeability of agent models across different simulation environments represents a major challenge and necessitates harmonization and standardization. To address this challenge, we present a standardized and modular simulation integration architecture that enables the tool-independent integration of traffic agent models. The architecture builds upon the Open Simulation Interface (OSI) as a structured message format and the Functional Mock-up Interface (FMI) for dynamic model exchange. Rather than introducing yet another model or simulation tool, we provide a reusable reference implementation that translates these standards into a practical integration blueprint, including clear interfaces, data mappings, and execution semantics. The generic nature of the architecture is demonstrated by integrating an exemplary agent model into three widely used simulation environments: OpenPASS, CARLA, and CarMaker. As part of the evaluation, we show that the model yields consistent behavior in all simulation platforms, thereby validating the interoperability, modularity, and standard compliance of the proposed architecture. The reference implementation lowers integration barriers, serves as a foundation for future research, and is made publicly available at github.com/ika-rwth-aachen/agent-model-integration

cs.RO↗

arXiv2026

A Five-Layer MLOps Architecture for Connected Automated Driving

The continual assurance of safety and performance of automated driving systems (ADSs) poses significant challenges. ADSs operate in complex, dynamic, open-world environments allowing a wide range of scenarios, including ones that are rare or not foreseen during initial development. While the incorporation of artificial intelligence (AI) and machine learning (ML) technology allows ADSs to learn from data gathered during operation and thus enables them to adapt over time, these approaches come with their own challenges. A key advantage of ADSs compared to human drivers is their greater ability to gather data collectively across a fleet of vehicles, or even across multiple fleets operated by different entities, and to learn from this data collectively. Vehicles can share and combine their data to identify additional learning opportunities otherwise missed by individual vehicles. This creates new opportunities to tackle the challenges of continual assurance of safety and performance, but requires the implementation of architectures that leverage the collective learning potential. Based on established MLOps principles and existing work in the field of connected automated driving, this paper presents a five-layer architecture for collective learning-enabled MLOps processes for ADSs. The goal of this architecture is to provide a conceptual blueprint for the design and implementation of MLOps processes by fleet operators and other relevant stakeholders. The paper describes the main responsibilities of each layer, their interactions, and how multi-level self-assessments enabled by the architecture can support the detection and reduction of edge cases including black swan events.

cs.RO↗

arXiv2026

Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift

Reliable uncertainty estimation for 3D object detection is critical for deploying safe autonomous systems, yet modern detectors remain poorly calibrated, especially under distribution shifts. Although post-hoc calibration methods address this issue and provide improved calibration for in-distribution tests, they fail to adapt in distribution-shifted scenarios. In this work, we address this issue and introduce a density-aware calibration method that couples post-hoc calibrators with the feature density of latent object queries from DETR-style 3D object detectors. These queries form a compact, location and class-aware feature, ideal for density estimation, allowing our approach to adjust model confidences in distribution-shift scenarios. By fitting a density estimator on these query features, our approach jointly recalibrates both classification and bounding box regression uncertainties. On both a multi-view camera and LiDAR-based detector, our approach consistently outperforms standard post-hoc methods in both in-distribution and distribution-shifted scenarios. Code available https://tillbeemelmanns.github.io/query2uncertainty/ .

cs.CV↗

arXiv2026

Robust Fusion of Object-Level V2X for Learned 3D Object Detection

Perception for automated driving is largely based on onboard environmental sensors, such as cameras and radar, which are cost-effective but limited by line-of-sight and field-of-view constraints. These inherent limitations may cause onboard perception to fail under occlusions or poor visibility conditions. In parallel, cooperative awareness via vehicle-to-everything (V2X) communication is becoming increasingly available, enabling vehicles and infrastructure to share their own state as object-level information that complements onboard perception. In this work, we study how such V2X information can be integrated into 3D object detection and how robust the resulting system is to realistic V2X imperfections. Using the nuScenes dataset, we emulate object-level cooperative awareness messages from ground truth, injecting controlled noise and object dropout to mimic real-world conditions such as latency, localization errors, and low V2X penetration rates. We convert these messages into a dedicated bird's-eye view (BEV) input and fuse them into a BEVFusion-style detector. Our results demonstrate that while object-level cooperative information can substantially improve detection performance, achieving an NDS of 0.80 under favorable conditions, models trained on idealized data become fragile and over-reliant on V2X. Conversely, our proposed noise-aware training strategy, coupled with explicit confidence encoding, enhances robustness, maintaining performance gains even under severe noise and reduced V2X penetration.

cs.CV↗

arXiv2026

Integrated Wheel Sensor Communication using ESP32 -- A Contribution towards a Digital Twin of the Road System

While current onboard state estimation methods are adequate for most driving and safety-related applications, they do not provide insights into the interaction between tires and road surfaces. This paper explores a novel communication concept for efficiently transmitting integrated wheel sensor data from an ESP32 microcontroller. Our proposed approach utilizes a publish-subscribe system, surpassing comparable solutions in the literature regarding data transmission volume. We tested this approach on a drum tire test rig with our prototype sensors system utilizing a diverse selection of sample frequencies between 1 Hz and 32 000 Hz to demonstrate the efficacy of our communication concept. The implemented prototype sensor showcases minimal data loss, approximately 0.1% of the sampled data, validating the reliability of our developed communication system. This work contributes to advancing real-time data acquisition, providing insights into optimizing integrated wheel sensor communication.

cs.RO↗

arXiv2025

Application Management in C-ITS: Orchestrating Demand-Driven Deployments and Reconfigurations

Vehicles are becoming increasingly automated and interconnected, enabling the formation of cooperative intelligent transport systems (C-ITS) and the use of offboard services. As a result, cloud-native techniques, such as microservices and container orchestration, play an increasingly important role in their operation. However, orchestrating applications in a large-scale C-ITS poses unique challenges due to the dynamic nature of the environment and the need for efficient resource utilization. In this paper, we present a demand-driven application management approach that leverages cloud-native techniques - specifically Kubernetes - to address these challenges. Taking into account the demands originating from different entities within the C-ITS, the approach enables the automation of processes, such as deployment, reconfiguration, update, upgrade, and scaling of microservices. Executing these processes on demand can, for example, reduce computing resource consumption and network traffic. A demand may include a request for provisioning an external supporting service, such as a collective environment model. The approach handles changing and new demands by dynamically reconciling them through our proposed application management framework built on Kubernetes and the Robot Operating System (ROS 2). We demonstrate the operation of our framework in the C-ITS use case of collective environment perception and make the source code of the prototypical framework publicly available at https://github.com/ika-rwth-aachen/application_manager.

cs.RO↗

arXiv2025

Requirement Identification for Traffic Simulations in Driving Simulators

This paper addresses the challenge of ensuring realistic traffic conditions by proposing a methodology that systematically identifies traffic simulation requirements. Using a structured approach based on sub-goals in each study phase, specific technical needs are derived for microscopic levels, agent models, and visual representation. The methodology aims to maintain a high degree of fidelity, enhancing both the validity of experimental outcomes and participant engagement. By providing a clear link between study objectives and traffic simulation design, this approach supports robust automotive development and testing.

cs.SE↗

arXiv2025

OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction

Autonomous driving has the potential to significantly enhance productivity and provide numerous societal benefits. Ensuring robustness in these safety-critical systems is essential, particularly when vehicles must navigate adverse weather conditions and sensor corruptions that may not have been encountered during training. Current methods often overlook uncertainties arising from adversarial conditions or distributional shifts, limiting their real-world applicability. We propose an efficient adaptation of an uncertainty estimation technique for 3D occupancy prediction. Our method dynamically calibrates model confidence using epistemic uncertainty estimates. Our evaluation under various camera corruption scenarios, such as fog or missing cameras, demonstrates that our approach effectively quantifies epistemic uncertainty by assigning higher uncertainty values to unseen data. We introduce region-specific corruptions to simulate defects affecting only a single camera and validate our findings through both scene-level and region-level assessments. Our results show superior performance in Out-of-Distribution (OoD) detection and confidence calibration compared to common baselines such as Deep Ensembles and MC-Dropout. Our approach consistently demonstrates reliable uncertainty measures, indicating its potential for enhancing the robustness of autonomous driving systems in real-world scenarios. Code and dataset are available at https://github.com/ika-rwth-aachen/OCCUQ .

cs.CV↗

arXiv2024

Enabling the Deployment of Any-Scale Robotic Applications in Microservice Architectures through Automated Containerization

In an increasingly automated world -- from warehouse robots to self-driving cars -- streamlining the development and deployment process and operations of robotic applications becomes ever more important. Automated DevOps processes and microservice architectures have already proven successful in other domains such as large-scale customer-oriented web services (e.g., Netflix). We recommend to employ similar microservice architectures for the deployment of small- to large-scale robotic applications in order to accelerate development cycles, loosen functional dependence, and improve resiliency and elasticity. In order to facilitate involved DevOps processes, we present and release a tooling suite for automating the development of microservices for robotic applications based on the Robot Operating System (ROS). Our tooling suite covers the automated minimal containerization of ROS applications, a collection of useful machine learning-enabled base container images, as well as a CLI tool for simplified interaction with container images during the development phase. Within the scope of this paper, we embed our tooling suite into the overall context of streamlined robotics deployment and compare it to alternative solutions. We release our tools as open-source software at https://github.com/ika-rwth-aachen/dorotos.

cs.RO↗

arXiv2024

Scenario-based Thermal Management Parametrization Through Deep Reinforcement Learning

The thermal system of battery electric vehicles demands advanced control. Its thermal management needs to effectively control active components across varying operating conditions. While robust control function parametrization is required, current methodologies show significant drawbacks. They consume considerable time, human effort, and extensive real-world testing. Consequently, there is a need for innovative and intelligent solutions that are capable of autonomously parametrizing embedded controllers. Addressing this issue, our paper introduces a learning-based tuning approach. We propose a methodology that benefits from automated scenario generation for increased robustness across vehicle usage scenarios. Our deep reinforcement learning agent processes the tuning task context and incorporates an image-based interpretation of embedded parameter sets. We demonstrate its applicability to a valve controller parametrization task and verify it in real-world vehicle testing. The results highlight the competitive performance to baseline methods. This novel approach contributes to the shift towards virtual development of thermal management functions, with promising potential of large-scale parameter tuning in the automotive industry.

cs.LG↗

arXiv2024

V2AIX: A Multi-Modal Real-World Dataset of ETSI ITS V2X Messages in Public Road Traffic

Connectivity is a main driver for the ongoing megatrend of automated mobility: future Cooperative Intelligent Transport Systems (C-ITS) will connect road vehicles, traffic signals, roadside infrastructure, and even vulnerable road users, sharing data and compute for safer, more efficient, and more comfortable mobility. In terms of communication technology for realizing such vehicle-to-everything (V2X) communication, the WLAN-based peer-to-peer approach (IEEE 802.11p, ITS-G5 in Europe) competes with C-V2X based on cellular technologies (4G and beyond). Irrespective of the underlying communication standard, common message interfaces are crucial for a common understanding between vehicles, especially from different manufacturers. Targeting this issue, the European Telecommunications Standards Institute (ETSI) has been standardizing V2X message formats such as the Cooperative Awareness Message (CAM). In this work, we present V2AIX, a multi-modal real-world dataset of ETSI ITS messages gathered in public road traffic, the first of its kind. Collected in measurement drives and with stationary infrastructure, we have recorded more than 285 000 V2X messages from more than 2380 vehicles and roadside units in public road traffic. Alongside a first analysis of the dataset, we present a way of integrating ETSI ITS V2X messages into the Robot Operating System (ROS). This enables researchers to not only thoroughly analyze real-world V2X data, but to also study and implement standardized V2X messages in ROS-based automated driving applications. The full dataset is publicly available for non-commercial use at v2aix.ika.rwth-aachen.de.

cs.MA↗

arXiv2024

Open-Source Tool Based Framework for Automated Performance Evaluation of an AD Function

As automation in the field of automated driving (AD) progresses, ensuring the safety and functionality of AD functions (ADFs) becomes crucial. Virtual scenario-based testing has emerged as a prevalent method for evaluating these systems, allowing for a wider range of testing environments and reproducibility of results. This approach involves AD-equipped test vehicles operating within predefined scenarios to achieve specific driving objectives. To comprehensively assess the impact of road network properties on the performance of an ADF, varying parameters such as intersection angle, curvature and lane width is essential. However, covering all potential scenarios is impractical, necessitating the identification of feasible parameter ranges and automated generation of corresponding road networks for simulation. Automating the workflow of road network generation, parameter variation, simulation, and evaluation leads to a comprehensive understanding of an ADF's behavior in diverse road network conditions. This paper aims to investigate the influence of road network parameters on the performance of a prototypical ADF through virtual scenario-based testing, ultimately advocating the importance of road topology in assuring safety and reliability of ADFs.

cs.RO↗

arXiv2024

MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection

Multi-modal 3D object detection models for automated driving have demonstrated exceptional performance on computer vision benchmarks like nuScenes. However, their reliance on densely sampled LiDAR point clouds and meticulously calibrated sensor arrays poses challenges for real-world applications. Issues such as sensor misalignment, miscalibration, and disparate sampling frequencies lead to spatial and temporal misalignment in data from LiDAR and cameras. Additionally, the integrity of LiDAR and camera data is often compromised by adverse environmental conditions such as inclement weather, leading to occlusions and noise interference. To address this challenge, we introduce MultiCorrupt, a comprehensive benchmark designed to evaluate the robustness of multi-modal 3D object detectors against ten distinct types of corruptions. We evaluate five state-of-the-art multi-modal detectors on MultiCorrupt and analyze their performance in terms of their resistance ability. Our results show that existing methods exhibit varying degrees of robustness depending on the type of corruption and their fusion strategy. We provide insights into which multi-modal design choices make such models robust against certain perturbations. The dataset generation code and benchmark are open-sourced at https://github.com/ika-rwth-aachen/MultiCorrupt.

cs.CV↗