Corinna Köpke’s research while affiliated with Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut and other places

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Publications (27)


Fig. 3 Crossing at the Frankenburg in Aachen, location b of the InD dataset. The colors of the bounding boxes correspond to the different road users in the data set: Red for cars, orange for trucks or busses, blue for bicycles and green for pedestrians [4]
Fig. 4 Layout of location b in (a), with the different risk-areas, their corresponding risk-values and obstacles indicated in the legend. The InD trajectories plotted over the layout in (b)
Fig. 5 A single path generated by the A* algorithm, with and without considering the risk-areas. Green dots indicate the steps checked by the A* algorithm, while blue dots indicate the chosen steps. The dark blue and purple dots indicate the start and end points. The Blue line indicates the finished path. The colouring of the areas of the layout indicates the risk-values, which correspond to the values and the areas shown in Fig. 4a
Fig. 6 The simulated trajectories in red and the trajectories of the InD of recording 24 in blue, plotted over the layout of location b. (a) shows the simulation that does not consider risk-areas, and (b) shows the simulation that does consider risk-areas very similar to the real trajectories, as visible by the overlapping red and blue trajectories in Fig. 6b. The improved A* algorithm suggests paths that prefer the crosswalk and cross the street in similar areas as the observed pedestrians. Furthermore, Figure 6b also shows that even trajectories which end in the middle of the street and thus in an area with very high risk can be simulated. This shows that no functionality of the simulation is limited by the introduced risk-areas. It is also visible in Fig. 6b that the tendency of the A* algorithm to cross the south street further to the south than
The chosen values ofˆr ofˆ ofˆr n and corresponding values for r n according to Eq. 6

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Simulation of Pedestrian Behaviour in Traffic Situations Using Risk-Based A* Pathfinding
  • Article
  • Full-text available

January 2025

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15 Reads

International Journal of Intelligent Transportation Systems Research

Tobias Rinnert

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Corinna Köpke

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Antonio Kruse

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[...]

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Fatally injured vulnerable road users, especially pedestrians who collided with motorised vehicles, account for 31% of all recorded fatalities in urban traffic in the EU. Autonomous vehicles will improve this situation in the future, reducing the impact of the human factor in critical traffic situations. The development of autonomous driving functions requires simulation environments to train certain behaviours. Consequently, these simulations need well represented vulnerable road users. In this paper, an approach for accurate prediction of pedestrian behaviour at street crossings is presented. The suggested solution involves an agent-based model working with A* pathfinding and risk-based areas, validated by a drone dataset on German road crossings.

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Investigating a Toolchain from Trajectory Recording to Resimulation

November 2024

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14 Reads

Applied Sciences

The growing variety of transportation options and increasing traffic congestion pose new challenges for road safety. As a result, there is an intensified focus on developing automated driving features and assistance systems aimed at minimizing accidents caused by human errors. The creation of these systems requires a substantial amount of testing kilometers, with estimates suggesting that around 2.1 billion kilometers would be necessary to ensure that each situation pertinent to the driving function is encountered at least once with a probability of 50%. This paper advances the microscopic simulation of traffic scenarios beyond linear patterns, utilizing the open-source environment openPASS. It addresses the research question of whether existing microscopic simulations are able to realistically represent non-linear traffic scenarios. A comprehensive toolchain integrates simulation with video recordings and laser scans. The study compares recorded traffic flow data with simulations at a T-junction, assessing the realism of vehicle models and trajectory representation. Three scenarios are analyzed, considering vehicles and pedestrians. The 3D geometry of the scene was captured with a laser scanner, enabling the mapping of recorded video data onto a geo-referenced environment. Object trajectories were extracted using an ’Regions with Convolutional Neural Networks features’ object detector. While openPASS simulated vehicle and pedestrian behaviors effectively, limitations in trajectory variability and reaction times were observed. These findings highlight the need for more realistic behavior models. This research emphasizes the necessity for improvements to accommodate complex driving behaviors and pedestrian dynamics.




Figure 1. EOL options and sub-options for 'Decommissioning' and 'Re-powering'.
Figure 2. OSS schematic with heights of different levels indicated, adopted from [17].
Risk assessment for the OSS.
Risk assessment for the three EOL options.
Testing Resilience Aspects of Operation Options for Offshore Wind Farms beyond the End-of-Life

June 2023

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73 Reads

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4 Citations

Energies

An anticipated challenge for the offshore wind industry is the legally standardized decommissioning of offshore wind infrastructure after the expiration of the respective approval period. To meet the energy and climate targets set by, e.g., the German Federal Government, this challenge must be mastered in the context of sustainability. Potential concepts are (i) the deconstruction of offshore infrastructure without replacement, (ii) the continued operation of the plants, (iii) partially or even completely replacing them with newer, modernized plants (re-powering). Re-powering could also be a combination of existing infrastructures with other innovative technologies, such as hydrogen. In this work, the three concepts are analyzed along with their risks and additional factors, such as feasibility, cost-effectiveness, predictability of technological progress, and, planning security, are discussed. A quantitative risk and resilience analysis is conceptually demonstrated for the specific risk of extreme weather and wave conditions caused by climate change. Synthetic wave height data are generated and the corresponding load changes are applied to example offshore wind farms. The three end-of-life options are compared using resilience indicators that serve as exemplary measures for the energy output, which serves as the key performance indicator


Methodology for Resilience Assessment for Rail Infrastructure Considering Cyber-Physical Threats

February 2023

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36 Reads

Lecture Notes in Computer Science

In the EU project SAFETY4RAILS, the project partners developed a collaborative toolkit that is able to assess and eventually improve the resilience of rail and metro transportation and its infrastructure against various cyber, physical and combined cyber-physical threats. In general, to improve a property of a system such as resilience, it is necessary to assess that property first. Therefore, in this paper, we focus on the aspect of assessing the resilience by the synergistic collaboration of two tools out of this toolkit: CuriX, which is a tool for monitoring and detecting abnormal behaviour of infrastructure in the presence of threats, and CaESAR, which can asses propagation of performance losses over distributed systems that reflects its resilience. We showcase a resilience assessment for an exemplary scenario of combined cyber-physical threats which is applied to a metro system. In this assessment, the main functionalities and results of both tools as well as their combined usage will be described to demonstrate how their collaboration can contribute to an improved resilience assessment.


Modelling and Simulation of Railway Networks for Resilience Analysis

February 2023

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40 Reads

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2 Citations

Lecture Notes in Computer Science

The work focuses on the impact of disruptions on a railway transportation network. The modeling of the transportation network with the help of graph theory is presented and criticality/vulnerability assessment and impact propagation in these networks is studied. Furthermore, the work emulates defined mitigation measures in the modelled network and quantifies the resilience of the network. The results are produced from an agent-based simulation tool called CaESAR (Cascading Effects Simulation in Areas for increasing Resilience) that uses network graphs in cooperation with their behavioral characteristics. The tool is under integration with a broader framework (S4RIS platform) designed under the EU H2020 project SAFETY4RAILS aimed at integrating multiple solutions to be made available to operators and first responders for better responses in case of threats and disruptions.


Resilience management processes in the offshore wind industry: schematization and application to an export-cable attack

January 2023

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572 Reads

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6 Citations

Environment Systems and Decisions

Offshore wind energy (OWE) production is a crucial element for increasing the amount of renewable energy. Consequently, one can observe a strong and constant rise of the OWE industry, turning it to an important contributor of national energy provision. This trend, however, is accompanied by increasing pressure on the reliability, safety, and security of the OWE infrastructure. Related security threats are characterized by high uncertainty regarding impact and probability leading to considerable complication of the risk assessment. On the other hand, the resilience concept emphasizes the consideration of the system’s response to such threats, and thus, offers a solution for dealing with the high uncertainty. In this work, we present an approach for combining the strengths of risk and resilience management to provide a solution for handling security threats in OWE infrastructures. Within this context, we introduce a quality assessment enabling the quantification of the trustworthiness of obtained results.




Citations (17)


... This paper presents an adjustment of a simulation environment presented in Köpke et al. [11] and adapted in Meyer et al. [17] to predict pedestrian behaviour at mass events. The simulation uses the social force model [10] to represent the pedestrian's movements, see Fig. 1. ...

Reference:

Simulation of Pedestrian Behaviour in Traffic Situations Using Risk-Based A* Pathfinding
Towards a Specification of Behaviour Models for Crowds
  • Citing Chapter
  • July 2024

... A case study conducted within this framework demonstrates a tangible improvement in local power system resilience (Bose et al. 2024). This approach highlights the importance of real-time monitoring and adaptive battery operation in ensuring reliable power supply in the face of growing external threats (Ungerland et al. 2023). D. Bose et al. (Bose et al. 2023) partition the grid into manageable sections or voltage control areas to enable more flexible and effective control. ...

Improving Power System Resilience Based on Grid-Forming Converter Control and Real-Time Monitoring
  • Citing Conference Paper
  • October 2023

... Dynamic reconfiguration can be effectively modeled using Markov processes, which are suited to describe probabilistic transitions between system states based on failures, reconfigurations, or repairs [35,59]. An example of a Markov transition diagram of a 2-out-of-3 System with failure rates λ i and repair rates µ i can be found in Appendix 2, Fig. 12. ...

Advanced Markov Modeling and Simulation for Safety Analysis of Autonomous Driving Functions up to SAE 5 for Development, Approval and Main Inspection
  • Citing Conference Paper
  • August 2022

... The shipping industry encounters difficulties from changes in sea routes and vulnerabilities in ports, which impede trade (Liu et al. 2022: Viljoen andJoubert 2016). Uncertainties surround renewable energy sources such as offshore wind and wave power due to fluctuating ocean conditions, impacting the feasibility of projects (Köpke, Mielniczek, and Stolz 2023;Lyden et al. 2022). These sectoral impacts emphasise the complexity of sustainable ocean resource management amidst climate change. ...

Testing Resilience Aspects of Operation Options for Offshore Wind Farms beyond the End-of-Life

Energies

... It indicates how likely disruptions or failures are within the system. A higher probability of failure under a given distress level suggests vulnerability, while a lower one implies robustness for disasters of a certain magnitude [6], [17], [106]- [108]. By using historical data, expert opinions, and predictive models, this attribute helps identify risks. ...

Modelling and Simulation of Railway Networks for Resilience Analysis
  • Citing Chapter
  • February 2023

Lecture Notes in Computer Science

... As available sites on land are limited, and to utilize the more abundant energy resource available in offshore regions, many governments and companies are looking to install offshore wind turbines (OWT). The cumulative offshore wind power capacity in 2019 was 27.2 GW, which displayed a growth of 24 % as compared to 2018 [3]. ...

Resilience management processes in the offshore wind industry: schematization and application to an export-cable attack

Environment Systems and Decisions

... One issue to overcome is the need for a sufficiently broad overall system definition (generalized Markov state space) that needs to include environment information covering street and weather scenario (street geometry and conditions, weather, time-of-the-day), driver and occupants, other road users as well as the technical system (sensors, hardware, software, interfaces, cloud services, car-to-X communication). Examples of overall Markov models for AD considering technical system and driver are given in (Nyberg 2018) and considering in addition environment are given in (Satsrisakul 2018) (Häring et al. 2022). ...

Advanced Markov Modeling and Simulation for Safety Analysis of Autonomous Driving Functions up to SAE 5 for Development, Approval and Main Inspection

... For example, airports may prioritize certain flights or adjust schedules in response to anticipated weather changes, reducing the likelihood of cascading delays throughout the network [26,27]. Second, using advanced machine learning models like the KAN model can improve the accuracy of weather impact predictions, thus enabling airports and airlines to make more informed decisions about resource allocation and emergency response planning [28,29]. Finally, by focusing on Xi'an Xianyang International Airport, this study provides a valuable case study for other regional airports facing similar challenges. ...

Resilience Quantification for Critical Infrastructure: Exemplified for Airport Operations
  • Citing Chapter
  • February 2022

Lecture Notes in Computer Science

... Bayesian networks, which are probabilistic graphical models (Pearl, 1988), are often used in the literature to model the effects of natural hazards on CI (e.g. see Kameshwar et al., 2019or Ramírez-Agudelo et al., 2021. ...

An Expert-Driven Probabilistic Assessment of the Safety and Security of Offshore Wind Farms

Energies

... On the other hand, there are several frameworks which consider attacks combining both physical and cyber-threats. However, as shown in Table 1, most of them are tailored to a specific CI domain, such as industrial plants [18], healthcare [6], water infrastructures [8] and air transportation [9]. Additionally, only few frameworks take into account the interrelations between CIs. ...

Security and Resilience for Airport Infrastructure
  • Citing Conference Paper
  • January 2020