J. Marius Zöllner - IEEE Xplore Author Profile

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Autonomous mobile systems exploring unknown environments face the challenge of optimizing their routes to minimize time and distance traveled. This paper explores integrating SLAM algorithms with optimized routing techniques to address the Mixed Chinese Postman Problem (MCPP). The MCPP arises when a robot aims to efficiently explore an area by determining the most optimal path that covers all edge...Show More
Fault diagnosis is crucial for complex autonomous mobile systems, especially for modern-day autonomous driving (AD). Different actors, numerous use cases, and complex heterogeneous components motivate a fault diagnosis of the system and overall system integrity. AD systems are composed of many heterogeneous components, each with different functionality and possibly using a different algorithm (e.g...Show More
Understanding emotions and expressions is a task of interest across multiple disciplines, especially for improving user experiences. Contrary to the common perception, it has been shown that emotions are not discrete entities but instead exist along a continuum. People understand discrete emotions differently due to a variety of factors, including cultural background, individual experiences, and c...Show More
Model compression and hardware acceleration are essential for the resource-efficient deployment of deep neural networks. Modern object detectors have highly interconnected convolutional layers with concatenations. In this work, we study how pruning can be applied to such architectures, exemplary for YOLOv7. We propose a method to handle concatenation layers, based on the connectivity graph of conv...Show More
Reinforcement Learning is a highly active research field with promising advancements. In the field of autonomous driving, however, often very simple scenarios are being examined. Common approaches use non-interpretable control commands as the action space and unstructured reward designs, which are unsuitable for complex scenarios. In this work, we introduce Informed Reinforcement Learning, where a...Show More
This survey analyzes intermediate fusion methods in collaborative perception for autonomous driving, categorized by real-world challenges. We examine various methods, detailing their features and the evaluation metrics they employ. The focus is on addressing challenges like transmission efficiency, localization errors, communication disruptions, and heterogeneity. Moreover, we explore strategies t...Show More
With consistently increasing amounts of transported goods, autonomous cargo transport has gained increasing interest as a potential solution. In addition to reliable autonomous driving functions, Autonomous cargo transport requires a wide range of additional software and hardware components to ensure a safe and efficient transport of cargo as well as a pleasant user experience for the customers. T...Show More
This survey offers a comprehensive examination of collaborative perception datasets in the context of Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Everything (V2X). It highlights the latest developments in large-scale benchmarks that accelerate advancements in perception tasks for autonomous vehicles. The paper systematically analyzes a variety of datasets, comparing t...Show More
As autonomous vehicles become increasingly preva-lent, robust remote operation systems are imperative to ensure safety and reliability in unpredictable scenarios. Current remote operation systems in research often lack scalability and adaptability, hindering their integration into diverse autonomous driving platforms. This paper addresses these challenges by introducing a scalable remote operation...Show More
Reinforcement learning has emerged as an important approach for autonomous driving. A reward function is used in reinforcement learning to establish the learned skill objectives and guide the agent toward the optimal policy. Since autonomous driving is a complex domain with partly conflicting objectives with varying degrees of priority, developing a suitable reward function represents a fundamenta...Show More
The accurate prediction of surrounding traffic actors’ movements is vital for the large-scale safe deployment of autonomous vehicles. Existing motion forecasting methods primarily aim to minimize prediction error by optimizing a loss function, which can sometimes lead to physically infeasible predictions or states that violate external constraints. This paper proposes a method that integrates expl...Show More
In this paper, we introduce a method for estimating blind spots for sensor setups of autonomous or automated vehicles and/or robotics applications. In comparison to previous methods that rely on geometric approximations, our presented approach provides more realistic coverage estimates by utilizing accurate and detailed 3D simulation environments. Our method leverages point clouds from LiDAR senso...Show More
Verifying highly automated driving functions can be challenging, requiring identifying relevant test scenarios. Scenario-based testing will likely play a significant role in verifying these systems, predominantly occurring within simulation. In our approach, we use traffic scenes as a starting point (seedscene) to address the individuality of various highly automated driving functions and to avoid...Show More
The European Green Deal aims to achieve climate neutrality by 2050, which demands improved emissions efficiency from the transportation industry. This study uses an agent-based simulation to analyze the sustainability impacts of shared autonomous shuttles. We forecast travel demands for 2050 and simulate regulatory interventions in the form of replacing private cars with a fleet of shared autonomo...Show More
Autonomous vehicles hold great promise in improving the future of transportation. The driving models used in these vehicles are based on neural networks, which can be difficult to validate. However, ensuring the safety of these models is crucial. Traditional field tests can be costly, time-consuming, and dangerous. To address these issues, scenario-based closed-loop simulations can simulate many h...Show More
Precisely predicting the future trajectories of surrounding traffic participants is a crucial but challenging problem in autonomous driving, due to complex interactions between traffic agents, map context and traffic rules. Vector-based approaches have recently shown to achieve among the best performances on trajectory prediction benchmarks. These methods model simple interactions between traffic ...Show More
Providing realistic simulators is critical to the development of automated vehicles (AVs). A main aspect of simulation is the validation and verification of the driving function in scenarios with vulnerable road users (VRUs), such as cyclists. Modeling VRUs is challenging because their behavior is highly dynamic and the number of possible trajectories is much higher than for vehicles. Their behavi...Show More
Real-time traffic light recognition is essential for autonomous driving. Yet, a cohesive overview of the underlying model architectures for this task is currently missing. In this work, we conduct a comprehensive survey and analysis of traffic light recognition methods that use convolutional neural networks (CNNs). We focus on two essential aspects: datasets and CNN architectures. Based on an unde...Show More
High precision trajectory data is crucial for the validation and verification of algorithms for automated driving. For instance, ego-vehicle-localization algorithms, traffic prediction, traffic scene assessment and other components profit from highly accurate trajectory data for evaluation and bench-marking. In this work we present a ground truth data generation pipeline that is able to produce tr...Show More
Control centers for automated vehicle fleets (CC-AVFs) are pivotal in enabling autonomous driving at a large scale. This work conducts a holistic survey of CC-AVFs, focusing on European studies. It discusses operational concepts, requirements, architectures, transmission technologies, monitoring, and remote operation methods. Additionally, we present a design concept for CC-AVFs that structures th...Show More
Simulation environments for autonomous mobile robots often include simplified sensors and associated environment models. The output of these sensors are essential for the evaluation of robotic capabilities. Invalid assumptions in sensor models and the associated environment models thus lead to false assumptions of the subsequent algorithms, whose decisions the robot is later defeated by. In order ...Show More
Proving grounds and test areas equipped with smart infrastructure might provide a solution to bridge the gap between laboratory testbeds and real-life testing by challenging the autonomous vehicle with critical scenarios in a risk-less way. Whereas actual research focuses on the infrastructure-sided support of automated and assisted driving, we focus on the integration of real experimental vehicle...Show More
Anomalies in the domain of autonomous driving are a major hindrance to the large-scale deployment of autonomous vehicles. In this work, we focus on high-resolution camera data from urban scenes that include anomalies of various types and sizes. Based on a Variational Autoencoder, we condition its latent space to classify samples as either normal data or anomalies. In order to emphasize especially ...Show More
In recent years there have been remarkable advancements in autonomous driving. While autonomous vehicles demonstrate high performance in closed-set conditions, they encounter difficulties when confronted with unexpected situations. At the same time, world models emerged in the field of model-based reinforcement learning as a way to enable agents to predict the future depending on potential actions...Show More
Ensuring validation for highly automated driving poses significant obstacles to the widespread adoption of highly automated vehicles. Scenario-based testing offers a potential solution by reducing the homologation effort required for these systems. However, a crucial prerequisite, yet unresolved, is the definition and reduction of the test space to a finite number of scenarios. To tackle this chal...Show More