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    <completedYear>2020</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
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    <pageFirst/>
    <pageLast/>
    <pageNumber>23</pageNumber>
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    <issue>6</issue>
    <volume>10</volume>
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    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
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    <completedDate>2021-12-20</completedDate>
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    <title language="eng">Measuring the Realtime Capability of Parallel-Discrete-Event-Simulations</title>
    <abstract language="eng">Speeding up Discrete Event Simulations (DESs) is a broad research field. Promising Parallel Discrete Event Simulation (PDES) approaches with optimistic and conservative synchronisation schemes have emerged throughout the years. However, in the area of real-time simulation, PDESs are rarely considered. This is caused by the complex problem of fitting parallel executed DES models to a real-time clock. Hence, this paper gives an extensive review of existing conservative and optimistic synchronisation schemes for PDESs. It introduces a metric to compare their real-time capabilities to determine whether they can be used for soft or firm real-time simulation. Examples are given on how to apply this metric to evaluate PDESs using synthetic and real-world examples. The results of the investigation reveal that no final answer can be given if PDESs can be used for soft or firm real-time simulation as they are. However, boundary conditions were defined, which allow a use-case specific evaluation of the real-time capabilities of a certain parallel executed DES. Using this in-depth knowledge and can lead to predictability of the real-time behaviour of a simulation run.</abstract>
    <parentTitle language="eng">Electronics</parentTitle>
    <identifier type="issn">2079-9292</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-10883</identifier>
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    <author>
      <first_name>Christina</first_name>
      <last_name>Obermaier</last_name>
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    <author>
      <first_name>Raphael</first_name>
      <last_name>Riebl</last_name>
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    <author>
      <first_name>Ali H.</first_name>
      <last_name>Al-Bayatti</last_name>
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    <author>
      <first_name>Sarmadullah</first_name>
      <last_name>Khan</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Facchi</last_name>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>real-time simulation</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Vehicular Ad Hoc Networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Discrete Event Simulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>network simulation</value>
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    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="persons" number="26695">Facchi, Christian</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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  <doc>
    <id>4855</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>144</pageFirst>
    <pageLast>155</pageLast>
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    <publisherName>SciTePress</publisherName>
    <publisherPlace>Setúbal</publisherPlace>
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    <completedDate>2024-07-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">What Is the Right Bounding Box of a VRU Cluster in V2X Communication? How to Form a Good Shape?</title>
    <abstract language="eng">Among the possible traffic members on a Vehicle-to-Everything network, the term Vulnerable Road User (VRU) is assigned e.g. to pedestrians and cyclists. The VRU Awareness Message (VAM) is used by VRUs to inform other users of their presence and ensure they are perceived in a traffic system. Since the number of VRUs in crowded areas might be very high, the over-the-air traffic might be overloaded. To reduce channel overload, VAMs offer a clustering feature in which VRUs with similar kinematics and positions can group themselves so that only one device transmits messages. The VRU Basic Service specification describes the cluster as a bounding box that must cover all its members using a geometric shape so that other vehicles in the vicinity can avoid colliding with the contained VRUs. This paper contributes to the standardization effort by introducing a data structure, the Cluster Map, for the clustering in the VRU Basic Service. Furthermore, this work is the first to suggest strategies for forming bounding box shapes. Simulation results show that each of the geometry types is useful in different situations, thus further research on the topic is advised.</abstract>
    <parentTitle language="eng">Vehits 2024: 10th International Conference on Vehicle Technology and Intelligent Transport Systems Proceedings</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:573-48553</identifier>
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    <author>
      <first_name>Leonardo</first_name>
      <last_name>Barbosa da Silva</last_name>
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    <editor>
      <first_name>Alexey</first_name>
      <last_name>Vinel</last_name>
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    <author>
      <first_name>Silas</first_name>
      <last_name>Lobo</last_name>
    </author>
    <editor>
      <first_name>Karsten</first_name>
      <last_name>Berns</last_name>
    </editor>
    <author>
      <first_name>Evelio</first_name>
      <last_name>Fernández</last_name>
    </author>
    <editor>
      <first_name>Jeroen</first_name>
      <last_name>Ploeg</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Facchi</last_name>
    </author>
    <editor>
      <first_name>Oleg</first_name>
      <last_name>Gusikhin</last_name>
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    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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  </doc>
  <doc>
    <id>3096</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7</pageNumber>
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    <volume/>
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    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2023-02-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Enhancing the Safety of Vulnerable Road Users: Messaging Protocols for V2X Communication</title>
    <parentTitle language="eng">2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) Proceedings</parentTitle>
    <identifier type="isbn">978-1-6654-5468-1</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/VTC2022-Fall57202.2022.10012775</enrichment>
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    <author>
      <first_name>Silas</first_name>
      <last_name>Lobo</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Facchi</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>road safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>vulnerable road users</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>V2X communication</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>messaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>collective perception</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CPM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>VAM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>VRU</value>
    </subject>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26695">Facchi, Christian</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="25591">Festag, Andreas</collection>
    <collection role="institutes" number="19568">Fakultät Nachhaltige Infrastruktur</collection>
  </doc>
  <doc>
    <id>3046</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>28</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>23</volume>
    <articleNumber>159</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2023-01-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics</title>
    <abstract language="eng">A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-30464</identifier>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Alberto</first_name>
      <last_name>Flores Fernández</last_name>
    </author>
    <author>
      <first_name>Eduardo</first_name>
      <last_name>Sánchez Morales</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Facchi</last_name>
    </author>
    <author>
      <first_name>Andrés</first_name>
      <last_name>García Higuera</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>On-Board Diagnostics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Transformer Neural Network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Autonomous Driving</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ADAS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inertial Navigation Systems</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26695">Facchi, Christian</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3046/sensors-23-00159.pdf</file>
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    <publisherName>Springer</publisherName>
    <publisherPlace>Cham</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-04-04</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Is Maximum Entropy Deep Inverse Reinforcement Learning Suitable for Pedestrian Path Prediction?</title>
    <parentTitle language="eng">Proceedings of the 3rd Cognitive Mobility Conference</parentTitle>
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      <first_name>Laura</first_name>
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      <last_name>Zöldy</last_name>
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    <title language="eng">Priority-Based Cooperative Driving Planner for V2X Maneuver Coordination Application</title>
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    <author>
      <first_name>Daniel</first_name>
      <last_name>Maksimovski</last_name>
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      <first_name>Christian</first_name>
      <last_name>Facchi</last_name>
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    <title language="eng">Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction</title>
    <abstract language="eng">The prediction of the motion of traffic participants is a crucial aspect for the research and development of Automated Driving Systems (ADSs). Recent approaches are based on multi-modal motion prediction, which requires the assignment of a probability score to each of the multiple predicted motion hypotheses. However, there is a lack of ground truth for this probability score in the existing datasets. This implies that current Machine Learning (ML) models evaluate the multiple predictions by comparing them with the single real trajectory labeled in the dataset. In this work, a novel data-based method named Probabilistic Traffic Motion Labeling (PROMOTING) is introduced in order to (a) generate probable future routes and (b) estimate their probabilities. PROMOTING is presented with the focus on urban intersections. The generation of probable future routes is (a) based on a real traffic dataset and consists of two steps: first, a clustering of intersections with similar road topology, and second, a clustering of similar routes that are driven in each cluster from the first step. The estimation of the route probabilities is (b) based on a frequentist approach that considers how traffic participants will move in the future given their motion history. PROMOTING is evaluated with the publicly available Lyft database. The results show that PROMOTING is an appropriate approach to estimate the probabilities of the future motion of traffic participants in urban intersections. In this regard, PROMOTING can be used as a labeling approach for the generation of a labeled dataset that provides a probability score for probable future routes. Such a labeled dataset currently does not exist and would be highly valuable for ML approaches with the task of multi-modal motion prediction. The code is made open source.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-24341</identifier>
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    <author>
      <first_name>Alberto</first_name>
      <last_name>Flores Fernández</last_name>
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      <first_name>Jonas</first_name>
      <last_name>Wurst</last_name>
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      <first_name>Eduardo</first_name>
      <last_name>Sánchez Morales</last_name>
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      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
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    <author>
      <first_name>Christian</first_name>
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      <first_name>Andrés</first_name>
      <last_name>García Higuera</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>PROMOTING</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated driving systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>autonomous vehicles</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multi-modal</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>motion prediction</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>route prediction</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>real traffic data</value>
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    <title language="eng">Data Rate Reduction for Video Streams in Teleoperated Driving</title>
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      <first_name>Jörg</first_name>
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      <value>bandwidth optimization</value>
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      <value>teleoperated driving</value>
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      <value>user study</value>
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      <language>eng</language>
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      <value>video stream</value>
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    <title language="eng">Cooperative Driving: Research on Generic Decentralized Maneuver Coordination for Connected and Automated Vehicles</title>
    <parentTitle language="eng">Smart Cities, Green Technologies, and Intelligent Transport Systems: 10th International Conference, SMARTGREENS 2021, and 7th International Conference, VEHITS 2021, Virtual Event, April 28-30, 2021, Revised Selected Papers</parentTitle>
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    <author>
      <first_name>Daniel</first_name>
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    <editor>
      <first_name>Cornel</first_name>
      <last_name>Klein</last_name>
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      <first_name>Matthias</first_name>
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      <first_name>Andreas</first_name>
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    <title language="eng">A Survey on Decentralized Cooperative Maneuver Coordination for Connected and Automated Vehicles</title>
    <abstract language="eng">V2X communications can be applied for maneuver coordination of automated vehicles, where the vehicles exchange messages to inform each other of their driving intentions and to negotiate for joint maneuvers. For motion and maneuver planning of automated vehicles, the cooperative maneuver coordination extends the perception range of the sensors, enhances the planning horizon and allows complex interactions among the vehicles. For specific scenarios, various schemes for maneuver coordination of connected automated vehicles exist. Recently, several proposals for maneuver coordination have been made that address generic instead of specific scenarios and apply different schemes for the message exchange of driving intentions and maneuver negotiation. This paper presents use cases for maneuver coordination and classifies existing generic approaches for decentralized maneuver coordination considering implicit and explicit trajectory broadcast, cost values and space-time reservation. We systematically describe the approaches, compare them and derive future research topics.</abstract>
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    <title language="eng">Adaptive Message Generation Rules for V2X Maneuver Coordination Service</title>
    <abstract language="eng">Maneuver coordination enables connected and automated vehicles (CAVs) to collaboratively plan, negotiate, and execute driving maneuvers, aiming to enhance safety, traffic flow, and energy efficiency. As part of the Vehicle-to-Everything (V2X) communication system, it operates as a dedicated service that relies on detailed, bidirectional interactions between vehicles, unlike traditional broadcast-based V2X services. While maneuver coordination is the focus of ongoing research and standardization, the rules that govern when and how vehicles generate Maneuver Coordination Messages (MCMs) within the Maneuver Coordination Service (MCS) are still not fully defined. These rules are essential for ensuring timely and reliable coordination. This paper proposes three adaptive MCM generation strategies based on the operation mode of the MCS, maneuver priority, and current channel load. The first approach defines MCM rules for intent sharing, maneuver negotiation, and execution phases, factoring in maneuver importance. The second adjusts the frequency of intent MCMs when a negotiation message is received from another vehicle. The third adapts frequency based on real-time channel load during intent sharing. For comparison, two baseline strategies are also evaluated. The proposed rules are tested for highway merging and lane change use cases in traffic scenarios with increasing vehicle density and high channel loads. A comprehensive evaluation is conducted using metrics related to the network, message generation frequency, congestion control, and maneuver coordination. The results demonstrate significantly enhanced channel efficiency and communication reliability across all metrics. For example, under the final approach, maneuver negotiation time is reduced by a factor of five in congested V2X environments.</abstract>
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