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  • 1.
    Delooz, Quentin
    Halmstad University, School of Information Technology.
    Sensor Data Sharing in V2X Communications: Protocol Design and Performance Optimization of Collective Perception2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Sensor data sharing involves exchanging sensor data among multiple devices, systems, or platforms through various means, such as wired or wireless communication, cloud storage, and distributed computing. In Vehicle-to-Everything (V2X) communication, sensor data sharing is known as Collective Perception (CP). V2X Collective Perception is the principle of exchanging sensor data among V2X-capable stations, such as vehicles, vulnerable road users, or roadside units, by exchanging lists of perceived objects in the allocated 5.9 GHz frequency band for road safety and traffic efficiency. An object can be anything relevant to traffic safety and is described using its characteristics such as position, heading, and velocity. Objects are detected thanks to sensors such as cameras, LiDARs, and radars mounted on V2X stations. This thesis investigates the message generation rule for CP, specifically how often and with which objects a Collective Perception Message (CPM) should be generated for transmission. The contained studies focus on the challenges posed by the limited bandwidth available in the 5.9 GHz channel against the object selection for inclusion in CPMs. In the first part of the realized studies, the protocol design and the requirements of CP are comprehended from the network and application-related aspects, concluding that the process of filtering objects is necessary to control the channel usage of CP. Moreover, results show that object filtering is only beneficial in high-traffic density scenarios and should not be applied when channel resources are plenty available. In the second part, methods are developed and assessed to adapt the object filtering mechanism to the available channel resources and control information redundancy, i.e., controlling the number of vehicles transmitting updates about the same objects. Through a combination of theoretical analysis, large-scale simulations, and experimental evaluation, this thesis provides a better understanding of the requirements of CP for object filtering and shows the benefits of a developed novel algorithm to adapt object filtering to the available channel resources. Additionally, it elaborates on new metrics and provides a requirements analysis and performance assessment of selected information redundancy reduction techniques. Finally, the results show that combining both approaches enables efficient control of information redundancy while allowing efficient channel resource usage.

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  • 2.
    Delooz, Quentin
    et al.
    Technische Hochschule Ingolstadt, CARISSMA, Ingolstadt, Germany.
    Festag, Andreas
    Technische Hochschule Ingolstadt, CARISSMA, Ingolstadt, Germany.
    Network Load Adaptation for Collective Perception in V2X Communications2019In: 2019 Conference Proceedings: 8th IEEE International Conference on Connected Vehicles and Expo, ICCVE, Piscataway: IEEE, 2019, article id 8964988Conference paper (Refereed)
    Abstract [en]

    Collective perception uses V2X communications to increase the perception capabilities of vehicles. Relying on the perceived data from their local sensors, nodes exchange information about the objects they detect in their surroundings. An object can be anything significant for the nodes' safety, e.g., obstacles on the road, other vehicles or pedestrians. The amount of data generated by each node is determined by the number of perceived objects and the generation frequency of the messages carrying the detected objects. Considering the limited bandwidth of the wireless channel, the data load generated by collective perception can easily exceed the channel capacity. In this paper, we investigate three schemes that filter the number of objects in the messages and thereby adjust the network load in order to optimize the transmission of perceived objects. Our simulation-based performance evaluation indicates that the use of filtering is an effective approach to improve network-related performance metrics, whereas the expected impairment of the perception quality is rather small. The comparison of the filtering algorithms provide insights into the tradeoff between network-related metrics and perception quality. ©2019 by IEEE

  • 3.
    Delooz, Quentin
    et al.
    CARISSMA, Technische Hochschule Ingolstadt, Ingolstadt, Germany.
    Festag, Andreas
    CARISSMA, Technische Hochschule Ingolstadt, Ingolstadt, Germany.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Congestion Aware Objects Filtering for Collective Perception2021In: Electronic Communications of the EASST, E-ISSN 1863-2122, Vol. 80Article in journal (Refereed)
    Abstract [en]

    This paper addresses collective perception for connected and automateddriving. It proposes the adaptation of filtering rules based on the currently availablechannel resources, referred to as Enhanced DCC-Aware Filtering (EDAF). © 2021. All Rights Reserved.

  • 4.
    Delooz, Quentin
    et al.
    Technische Hochschule Ingolstadt / CARISSMA Ingolstadt, Germany.
    Festag, Andreas
    Technische Hochschule Ingolstadt / CARISSMA Ingolstadt, Germany.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Revisiting Message Generation Strategies for Collective Perception in Connected and Automated Driving2020Conference paper (Refereed)
    Abstract [en]

    Collective perception enables vehicles to exchange pre-processed sensor data and is being standardized as a 2nd generation V2X communication service. The European standardization in ETSI foresees the exchange of detected objects and defined a dedicated message type (Collective Perception Message, CPM) with rules to decide when and with which objects the message should be generated, referred to as generation rules. The choice of these rules is not straightforward and influences both channel load and perception quality. For the object inclusion, ETSI currently follows a similar policy as for the generation of Cooperative Awareness Messages (CAM): The objects are filtered based on their dynamics. We regard this approach as conservative. The present paper revisits the generation rules for the CPM and applies two approaches for object inclusion to the CPM -- the conservative strategy of ETSI and a more 'greedy' strategy. We assess the performance by discrete-event simulations in a scenario representing a city with realistic vehicle densities and mobility patterns. The simulations take into account the effects imposed by decentralized congestion control. Considering that ETSI currently follows the conservative strategy, we conclude that the application of a greedy strategy improves the perception quality in low-density scenarios.

  • 5.
    Delooz, Quentin
    et al.
    Halmstad University, School of Information Technology. Technische Hochschule Ingolstadt, CARISSMA, Ingolstadt, Germany.
    Festag, Andreas
    Technische Hochschule Ingolstadt, CARISSMA, Ingolstadt, Germany; Fraunhofer IVI, Technische Universität Dresden, Dresden, Germany.
    Vinel, Alexey
    Halmstad University, School of Information Technology. Karlsruhe Institute of Technology, Universität Karlsruhe, Karlsruhe, Germany.
    Lobo, Silas C.
    Technische Hochschule Ingolstadt, CARISSMA, Ingolstadt, Germany.
    Simulation-based Performance Optimization of V2X Collective Perception by Adaptive Object Filtering2023In: 2023 IEEE Intelligent Vehicles Symposium (IV), Piscataway, NJ: IEEE, 2023Conference paper (Refereed)
    Abstract [en]

    V2X Collective Perception is the principle of exchanging sensor data among V2X-capable stations, such as vehicles or roadside units, by exchanging lists of perceived objects in the 5.9 GHz frequency band for road safety and traffic efficiency. An object can be anything relevant to traffic safety, e.g.,vehicles or pedestrians. The current standardization of Collective Perception in Europe considers filtering objects for transmission based on their locally perceived dynamics and freshness to preserve channel resources. However, two remaining problems of object filtering are: information redundancy and adapting object filtering to the available channel resources. In this paper, we combine redundancy mitigation and congestion control-aware filtering. We evaluate the performance of the resulting object filtering techniques by realizing realistic, large-scale simulations of a mid-size city in Germany. We assess the performance using ascoring metric. The results show better information redundancy control and adjustable channel usage for object filtering. © Copyright 2023 IEEE

  • 6.
    Delooz, Quentin
    et al.
    Technische Hochschule Ingolstadt / CARISSMA, Ingolstadt, Germany.
    Riebl, Raphael
    Technische Hochschule Ingolstadt / CARISSMA, Ingolstadt, Germany.
    Festag, Andreas
    Technische Hochschule Ingolstadt / CARISSMA, Ingolstadt, Germany.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Design and Performance of Congestion-Aware Collective Perception2021In: 2020 IEEE Vehicular Networking Conference (VNC) / [ed] Frank Kargl, Onur Altintas, Ana Aguiar, André Weimerskirch & Takamasa Higuchi, Piscataway, NJ: IEEE, 2021, article id 9318335Conference paper (Refereed)
    Abstract [en]

    In vehicular ad hoc networks, congestion control prevents the overloading of the wireless channel and ensures a fair distribution of the transmission resources. For ITS-G5-based vehicular networks, the European standardization by ETSI has specified a Decentralized Congestion Control (DCC) function at the access layer. This function controls the medium occupancy of a network node by enforcing maximum values of message transmission parameters. In the present paper, we study the impact of DCC on the performance of the collective perception service. This communication service enables vehicles and roadside stations to exchange messages with pre-processed sensor data. Since collective perception can considerably contribute to the network load, the transmission restrictions imposed by DCC affect the performance of the information exchange and the quality of the perception. The current design of collective perception in ETSI does not adapt the messages to the actual DCC constraints. We propose a novel approach for DCC-aware collective perception, which enhances the object filtering process of collective perception by dynamically adapting the message size to the DCC constraints and implicitly the message generation rate. Compared to the current ETSI design, the obtained results show a better quality of perception and channel usage, with a reduced message generation rate. © IEEE 2020

  • 7.
    Delooz, Quentin
    et al.
    CARISSMA – Center of Automotive Research on Integrated Safety Systems and Measurement Area, Technische Hochschule Ingolstadt, Ingolstadt, Germany,.
    Vinel, Alexey
    AIFB – Institute of Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology, Karlsruhe, Germany.
    Festag, Andreas
    CARISSMA – Center of Automotive Research on Integrated Safety Systems and Measurement Area, Technische Hochschule Ingolstadt, Ingolstadt, Germany,.
    Optimizing the channel resource usage for sensor data sharing with V2X communications2023In: at – Automatisierungstechnik, ISSN 0178-2312, Vol. 71, no 4, p. 311-317Article in journal (Refereed)
    Abstract [en]

    Sensor data sharing in V2X communication enables vehicles to exchange locally perceived sensor data with each other to increase their environmental awareness. It relies on the periodic exchange of selected, safety-relevant objects. Object selection is used to reduce channel resource usage. Additionally, vehicles use congestion control mechanisms to avoid overloading the channel. Currently, both object selection and congestion control mechanisms operate independently. We study a congestion-aware object filtering approach combining both and improving the performance of sensor data sharing. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

  • 8.
    Delooz, Quentin
    et al.
    Technische Hochschule Ingolstadt, Ingolstadt, Germany.
    Willecke, Alexander
    Technische Universität Braunschweig, Braunschweig, Germany.
    Garlichs, Keno
    Technische Universität Braunschweig, Braunschweig, Germany.
    Hagau, Andreas Christian
    Technische Universität Braunschweig, Braunschweig, Germany.
    Wolf, Lars
    Technische Universität Braunschweig, Braunschweig, Germany.
    Vinel, Alexey
    Halmstad University, School of Information Technology. University Of Passau, Passau, Germany.
    Festag, Andreas
    Technische Hochschule Ingolstadt, Ingolstadt, Germany.
    Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception2022In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 47076-47093Article in journal (Refereed)
    Abstract [en]

    Sensor data sharing enables vehicles to exchange locally perceived sensor data among each other and with the roadside infrastructure to increase their environmental awareness. It is commonly regarded as a next-generation vehicular communication service beyond the exchange of highly aggregated messages in the first generation. The approach is being considered in the European standardization process, where it relies on the exchange of locally detected objects representing anything safety-relevant, such as other vehicles or pedestrians, in periodically broadcasted messages to vehicles in direct communication range. Objects filtering methods for inclusion in a message are necessary to avoid overloading a channel and provoking unnecessary data processing. Initial studies provided in a pre-standardization report about sensor data sharing elaborated a first set of rules to filter objects based on their characteristics, such as their dynamics or type. However, these rules still lack the consideration of information received by other stations to operate. Specifically, to address the problem of information redundancy, several rules have been proposed, but their performance has not been evaluated yet comprehensively. In the present work, the rules are further analyzed, assessed, and compared. Functional and operational requirements are investigated. A performance evaluation is realized by discrete-event simulations in a scenario for a representative city with realistic vehicle densities and mobility patterns. A score and other redundancy-level metrics are elaborated to ease the evaluation and comparison of the filtering rules. Finally, improvements and future works to the filtering methods are proposed. Author

  • 9.
    Lyamin, Nikita
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Kleyko, Denis
    Luleå University of Technology, Luleå, Sweden.
    Delooz, Quentin
    University of Liège, Liège, Belgium.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    AI-Based Malicious Network Traffic Detection in VANETs2018In: IEEE Network, ISSN 0890-8044, E-ISSN 1558-156X, Vol. 32, no 6, p. 15-21Article in journal (Refereed)
    Abstract [en]

    Inherent unreliability of wireless communications may have crucial consequences when safety-critical C-ITS applications enabled by VANETs are concerned. Although natural sources of packet losses in VANETs such as network traffic congestion are handled by decentralized congestion control (DCC), losses caused by malicious interference need to be controlled too. For example, jamming DoS attacks on CAMs may endanger vehicular safety, and first and foremost are to be detected in real time. Our first goal is to discuss key literature on jamming modeling in VANETs and revisit some existing detection methods. Our second goal is to present and evaluate our own recent results on how to address the real-time jamming detection problem in V2X safety-critical scenarios with the use of AI. We conclude that our hybrid jamming detector, which combines statistical network traffic analysis with data mining methods, allows the achievement of acceptable performance even when random jitter accompanies the generation of CAMs, which complicates the analysis of the reasons for their losses in VANETs. The use case of the study is a challenging platooning C-ITS application, where V2X-enabled vehicles move together at highway speeds with short inter-vehicle gaps.

  • 10.
    Volk, Georg
    et al.
    University of Tübingen, Faculty of Science, Embedded Systems Group, Department of Computer Science, Germany.
    Delooz, Quentin
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Schiegg, Florian A.
    Robert Bosch GmbH, Corporate Research, Connected Mobility Systems, Hildesheim, Germany.
    Von Bernuth, Alexander
    University of Tübingen, Faculty of Science, Embedded Systems Group, Department of Computer Science, Germany.
    Festag, Andreas
    CARISSMA – Research and Test Center for Vehicle Safety, Ingolstadt, Germany; Fraunhofer Institute for Transportation and Infrastructure Systems IVI, Ingolstadt, Germany.
    Bringmann, Oliver
    University of Tübingen, Faculty of Science, Embedded Systems Group, Department of Computer Science, Germany.
    Towards Realistic Evaluation of Collective Perception for Connected and Automated Driving2021In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), IEEE, 2021, p. 1049-1056Conference paper (Refereed)
    Abstract [en]

    Collective perception in Vehicle-to-Everything (V2X) communications allows vehicles to exchange preprocessed sensor data with other traffic participants. It is currently standardized by ETSI as a second generation V2X communication service. The use of collective perception as a communication service for future fully autonomous driving requires a thorough evaluation and validation. Most of the previous work on collective perception has considered large scale-simulations with a focus on communications. However, the perception pipeline used for collective perception is equally important and must not be neglected or over-simplified. Also, to study collective perception in detail, large-scale field testing is practically infeasible. In this paper we extend an existing simulation framework with a realistic model for V2X communications and sensor-data based processing delays. The result is a simulation framework that incorporates the entire collective perception pipeline, which enables to comprehensively study sensor-based perception. We demonstrate the capabilities of this enhanced framework by analyzing the delay of each component involved in the perception pipeline. This allows a detailed insight in end-to-end delays and the age of information within the environmental model of autonomous vehicles. © 2021 IEEE.

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