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Perez-Cerrolaza, J., Abella, J., Borg, M., Donzella, C., Cerquides, J., Cazorla, F. J., . . . Flores, J. L. (2024). Artificial Intelligence for Safety-Critical Systems in Industrial and Transportation Domains: A Survey. ACM Computing Surveys, 56(7), Article ID 176.
Open this publication in new window or tab >>Artificial Intelligence for Safety-Critical Systems in Industrial and Transportation Domains: A Survey
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2024 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 56, no 7, article id 176Article in journal (Refereed) Published
Abstract [en]

Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-critical systems in which Machine Learning (ML) algorithms learn optimized and safe solutions. AI can also support and assist human safety engineers in developing safety-critical systems. However, reconciling both cutting-edge and state-of-the-art AI technology with safety engineering processes and safety standards is an open challenge that must be addressed before AI can be fully embraced in safety-critical systems. Many works already address this challenge, resulting in a vast and fragmented literature. Focusing on the industrial and transportation domains, this survey structures and analyzes challenges, techniques, and methods for developing AI-based safety-critical systems, from traditional functional safety systems to autonomous systems. AI trustworthiness spans several dimensions, such as engineering, ethics and legal, and this survey focuses on the safety engineering dimension.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2024
Keywords
Accident prevention; Engineering education; Ethical technology; Machine learning; Artificial intelligence technologies; Autonomous system; Cutting edges; Functional Safety; Human safety; Learn+; Machine learning algorithms; Safety critical systems; State of the art; Transportation domain; Security systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:ri:diva-73311 (URN)10.1145/3626314 (DOI)2-s2.0-85191063705 (Scopus ID)
Available from: 2024-06-03 Created: 2024-06-03 Last updated: 2024-06-03Bibliographically approved
Arvidsson, M., Sawirot, S., Englund, C., Alonso-Fernandez, F., Torstensson, M. & Duran, B. (2024). Drone Navigation and License Place Detection for Vehicle Location in Indoor Spaces. In: Lect. Notes Comput. Sci.: . Paper presented at 8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023. Varadero. 27 September 2023 through 29 September 2023 (pp. 362-374). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Drone Navigation and License Place Detection for Vehicle Location in Indoor Spaces
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2024 (English)In: Lect. Notes Comput. Sci., Springer Science and Business Media Deutschland GmbH , 2024, p. 362-374Conference paper, Published paper (Refereed)
Abstract [en]

Millions of vehicles are transported every year, tightly parked in vessels or boats. To reduce the risks of associated safety issues like fires, knowing the location of vehicles is essential, since different vehicles may need different mitigation measures, e.g. electric cars. This work is aimed at creating a solution based on a nano-drone that navigates across rows of parked vehicles and detects their license plates. We do so via a wall-following algorithm, and a CNN trained to detect license plates. All computations are done in real-time on the drone, which just sends position and detected images that allow the creation of a 2D map with the position of the plates. Our solution is capable of reading all plates across eight test cases (with several rows of plates, different drone speeds, or low light) by aggregation of measurements across several drone journeys. 

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Keywords
License plate detection, Nano-drone, UAV, Vehicle location
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:ri:diva-71941 (URN)10.1007/978-3-031-49552-6_31 (DOI)2-s2.0-85180752157 (Scopus ID)9783031495519 (ISBN)
Conference
8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023. Varadero. 27 September 2023 through 29 September 2023
Funder
VinnovaSwedish Research Council
Note

 The authors acknowledge the Swedish Innovation Agency (VINNOVA) for funding their research. Author F. A.-F. also thanks the Swedish Research Council (VR). 

Available from: 2024-02-27 Created: 2024-02-27 Last updated: 2024-05-22Bibliographically approved
Abella, J., Perez, J., Englund, C., Zonooz, B., Giordana, G., Donzella, C., . . . Cunial, D. (2023). SAFEXPLAIN: Safe and Explainable Critical Embedded Systems Based on AI. In: Proceedings -Design, Automation and Test in Europe, DATE: . Paper presented at 2023 Design, Automation and Test in Europe Conference and Exhibition, DATE 2023. Antwerp. 17 April through 19 April, 2023. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>SAFEXPLAIN: Safe and Explainable Critical Embedded Systems Based on AI
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2023 (English)In: Proceedings -Design, Automation and Test in Europe, DATE, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Deep Learning (DL) techniques are at the heart of most future advanced software functions in Critical Autonomous AI-based Systems (CAIS), where they also represent a major competitive factor. Hence, the economic success of CAIS industries (e.g., automotive, space, railway) depends on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost. However, there is a fundamental gap between Functional Safety (FUSA) requirements on CAIS and the nature of DL solutions. This gap stems from the development process of DL libraries and affects high-level safety concepts such as (1) explainability and traceability, (2) suitability for varying safety requirements, (3) FUSA-compliant implementations, and (4) real-time constraints. As a matter of fact, the data-dependent and stochastic nature of DL algorithms clashes with current FUSA practice, which instead builds on deterministic, verifiable, and pass/fail test-based software. The SAFEXPLAIN project tackles these challenges and targets by providing a flexible approach to allow the certification - hence adoption - of DL-based solutions in CAIS building on: (1) DL solutions that provide end-to-end traceability, with specific approaches to explain whether predictions can be trusted and strategies to reach (and prove) correct operation, in accordance to certification standards; (2) alternative and increasingly sophisticated design safety patterns for DL with varying criticality and fault tolerance requirements; (3) DL library implementations that adhere to safety requirements; and (4) computing platform configurations, to regain determinism, and probabilistic timing analyses, to handle the remaining non-determinism.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
Deep learning, Embedded systems, Product design, Software testing, Stochastic systems, Advanced softwares, Automotives, Competitive factor, Economic success, Embedded-system, Functional Safety, Learning techniques, Safety requirements, Software functions, Software products, Fault tolerance
National Category
Software Engineering
Identifiers
urn:nbn:se:ri:diva-65679 (URN)10.23919/DATE56975.2023.10137128 (DOI)2-s2.0-85162662708 (Scopus ID)9783981926378 (ISBN)
Conference
2023 Design, Automation and Test in Europe Conference and Exhibition, DATE 2023. Antwerp. 17 April through 19 April, 2023
Note

ACKNOWLEDGEMENTS The research leading to these results has received funding from the Horizon Europe Programme under the SAFEXPLAIN Project (www.safexplain.eu), grant agreement num. 101069595. BSC authors have also been supported by the Spanish Ministry of Science and Innovation under grant PID2019-107255GBC21/AEI/10.13039/501100011033.

Available from: 2023-08-11 Created: 2023-08-11 Last updated: 2023-08-11Bibliographically approved
Svanström, F., Alonso-Fernandez, F. & Englund, C. (2022). Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities. Drones, 6(11), Article ID 317.
Open this publication in new window or tab >>Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities
2022 (English)In: Drones, ISSN 2504-446X, Vol. 6, no 11, article id 317Article in journal (Refereed) Published
Abstract [en]

Automatic detection of flying drones is a key issue where its presence, especially if unauthorized, can create risky situations or compromise security. Here, we design and evaluate a multi-sensor drone detection system. In conjunction with standard video cameras and microphone sensors, we explore the use of thermal infrared cameras, pointed out as a feasible and promising solution that is scarcely addressed in the related literature. Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest. The sensing solutions are complemented with an ADS-B receiver, a GPS receiver, and a radar module. However, our final deployment has not included the latter due to its limited detection range. The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution. Two other novelties of our work are the creation of a new public dataset of multi-sensor annotated data that expands the number of classes compared to existing ones, as well as the study of the detector performance as a function of the sensor-to-target distance. Sensor fusion is also explored, showing that the system can be made more robust in this way, mitigating false detections of the individual sensors. © 2022 by the authors.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
anti-drone systems, drone detection, UAV detection
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:ri:diva-61199 (URN)10.3390/drones6110317 (DOI)2-s2.0-85141807932 (Scopus ID)
Note

Funding details: VINNOVA; Funding details: Vetenskapsrådet, VR; Funding text 1: Author F.A.-F. thanks the Swedish Research Council (VR) for funding their research. Authors F.A.-F. and C.E. thank the Swedish Innovation Agency (VINNOVA) for funding their research.

Available from: 2022-12-06 Created: 2022-12-06 Last updated: 2022-12-06Bibliographically approved
Svanström, F., Alonso-Fernandez, F. & Englund, C. (2021). A dataset for multi-sensor drone detection. Data in Brief, 39, Article ID 107521.
Open this publication in new window or tab >>A dataset for multi-sensor drone detection
2021 (English)In: Data in Brief, E-ISSN 2352-3409, Vol. 39, article id 107521Article in journal (Refereed) Published
Abstract [en]

The use of small and remotely controlled unmanned aerial vehicles (UAVs), referred to as drones, has increased dramatically in recent years, both for professional and recreative purposes. This goes in parallel with (intentional or unintentional) misuse episodes, with an evident threat to the safety of people or facilities [1]. As a result, the detection of UAV has also emerged as a research topic [2]. Most of the existing studies on drone detection fail to specify the type of acquisition device, the drone type, the detection range, or the employed dataset. The lack of proper UAV detection studies employing thermal infrared cameras is also acknowledged as an issue, despite its success in detecting other types of targets [2]. Beside, we have not found any previous study that addresses the detection task as a function of distance to the target. Sensor fusion is indicated as an open research issue as well to achieve better detection results in comparison to a single sensor, although research in this direction is scarce too [3–6]. To help in counteracting the mentioned issues and allow fundamental studies with a common public benchmark, we contribute with an annotated multi-sensor database for drone detection that includes infrared and visible videos and audio files. The database includes three different drones, a small-sized model (Hubsan H107D+), a medium-sized drone (DJI Flame Wheel in quadcopter configuration), and a performance-grade model (DJI Phantom 4 Pro). It also includes other flying objects that can be mistakenly detected as drones, such as birds, airplanes or helicopters. In addition to using several different sensors, the number of classes is higher than in previous studies [4]. The video part contains 650 infrared and visible videos (365 IR and 285 visible) of drones, birds, airplanes and helicopters. Each clip is of ten seconds, resulting in a total of 203,328 annotated frames. The database is complemented with 90 audio files of the classes drones, helicopters and background noise. To allow studies as a function of the sensor-to-target distance, the dataset is divided into three categories (Close, Medium, Distant) according to the industry-standard Detect, Recognize and Identify (DRI) requirements [7], built on the Johnson criteria [8]. Given that the drones must be flown within visual range due to regulations, the largest sensor-to-target distance for a drone in the dataset is 200 m, and acquisitions are made in daylight. The data has been obtained at three airports in Sweden: Halmstad Airport (IATA code: HAD/ICAO code: ESMT), Gothenburg City Airport (GSE/ESGP) and Malmö Airport (MMX/ESMS). The acquisition sensors are mounted on a pan-tilt platform that steers the cameras to the objects of interest. All sensors and the platform are controlled with a standard laptop vis a USB hub.

Place, publisher, year, edition, pages
Elsevier Inc., 2021
Keywords
Anti-drone systems, Drone detection, UAV detection
National Category
Computer Systems
Identifiers
urn:nbn:se:ri:diva-56907 (URN)10.1016/j.dib.2021.107521 (DOI)2-s2.0-85118496043 (Scopus ID)
Note

 Funding details: VINNOVA; Funding details: Vetenskapsrådet, VR; Funding details: Högskolan i Halmstad, HH; Funding text 1: This work has been carried out by Fredrik Svanstr?m in the context of his Master Thesis at Halmstad University (Master's Programme in Embedded and Intelligent Systems). Author F. A.-F. thanks the Swedish Research Council and VINNOVA for funding his research.; Funding text 2: This work has been carried out by Fredrik Svanström in the context of his Master Thesis at Halmstad University (Master's Programme in Embedded and Intelligent Systems). Author F. A.-F. thanks the Swedish Research Council and VINNOVA for funding his research.

Available from: 2021-11-22 Created: 2021-11-22 Last updated: 2021-11-22Bibliographically approved
Rosell, J., Englund, C., Vahidi, A., Mowla, N., Magazinius, A. & Järpe, E. (2021). A Frequency-based Data Mining Approach to Enhance in-vehicle Network Intrusion Detection. In: : . Paper presented at Fast Zero´21, Society of Automotive Engineers of Japan, 2021. JSAE
Open this publication in new window or tab >>A Frequency-based Data Mining Approach to Enhance in-vehicle Network Intrusion Detection
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2021 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Modern vehicles have numerous electronic control units (ECUs) that constantly communicate over embedded in-vehicle networks (IVNs) comprised of controlled area network (CAN) segments. The simplicity and size-constrained 8-byte payload of the CAN bus technology makes it infeasible to integrate authenticity and integrity-based protection mechanisms. Thus, a malicious component will be able to inject malicious data into the network with minimal risk for detection. Such vulnerabilities have been demonstrated with various security attacks such as the flooding, fuzzing, and malfunction attacks. A practical approach to improve security in modern vehicles is to monitor the CAN bus traffic to detect anomalies. However, to administer such an intrusion detection system (IDS) with a general approach faces some challenges. First, the proprietary encodings of the CAN data fields need to be omitted as they are intellectual property of the original equipment manufacturers (OEMs) and differ across vehicle manufacturers and their models. Secondly, such general and practical IDS approach must also be computationally efficient in terms of speed and accuracy. Traditional IDSs for computer networks generally utilize a rule or signature-based approach. More recently, the approach of using machine learning (ML) with efficient feature representation has shown significant success because of faster detection and lower development and maintenance costs. Therefore, an efficient data aggregation technique with enhanced frequency-based feature representation to improve the performance of MLbased IDS for the IVNs is proposed. The performance gain was verified with the Survival Analysis Dataset for automobile IDS.

Place, publisher, year, edition, pages
JSAE, 2021
National Category
Computer Systems
Identifiers
urn:nbn:se:ri:diva-58989 (URN)
Conference
Fast Zero´21, Society of Automotive Engineers of Japan, 2021
Available from: 2022-04-12 Created: 2022-04-12 Last updated: 2023-05-22Bibliographically approved
Rosell, J., Englund, C., Vahidi, A., Mowla, N. I., Magazinius, A. & Järpe, E. (2021). A Frequency-based Data Mining Approach to Enhance in-vehicle Network Intrusion Detection. In: FAST-zero '21: . Paper presented at Fast Zero´21, Society of Automotive Engineers of Japan, 2021. Japan: Society of Automotive Engineers
Open this publication in new window or tab >>A Frequency-based Data Mining Approach to Enhance in-vehicle Network Intrusion Detection
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2021 (English)In: FAST-zero '21, Japan: Society of Automotive Engineers, 2021Conference paper, Published paper (Refereed)
Abstract [en]

Modern vehicles have numerous electronic control units (ECUs) that constantly communicate over embedded in-vehicle networks (IVNs) comprised of controlled area network (CAN) segments. The simplicity and size-constrained 8-byte payload of the CAN bus technology makes it infeasible to integrate authenticity and integrity-based protection mechanisms. Thus, a malicious component will be able to inject malicious data into the network with minimal risk for detection. Such vulnerabilities have been demonstrated with various security attacks such as the flooding, fuzzing, and malfunction attacks. A practical approach to improve security in modern vehicles is to monitor the CAN bus traffic to detect anomalies. However, to administer such an intrusion detection system (IDS) with a general approach faces some challenges. First, the proprietary encodings of the CAN data fields need to be omitted as they are intellectual property of the original equipment manufacturers (OEMs) and differ across vehicle manufacturers and their models. Secondly, such general and practical IDS approach must also be computationally efficient in terms of speed and accuracy. Traditional IDSs for computer networks generally utilize a rule or signature-based approach. More recently, the approach of using machine learning (ML) with efficient feature representation has shown significant success because of faster detection and lower development and maintenance costs. Therefore, an efficient data aggregation technique with enhanced frequency-based feature representation to improve the performance of MLbased IDS for the IVNs is proposed. The performance gain was verified with the Survival Analysis Dataset for automobile IDS.

Place, publisher, year, edition, pages
Japan: Society of Automotive Engineers, 2021
Keywords
Data mining, in-vehicle network, intrusion detection, random forest, feature selection.
National Category
Computer Sciences
Identifiers
urn:nbn:se:ri:diva-63967 (URN)
Conference
Fast Zero´21, Society of Automotive Engineers of Japan, 2021
Available from: 2023-02-08 Created: 2023-02-08 Last updated: 2023-05-22Bibliographically approved
Aramrattana, M., Larsson, T., Englund, C., Jansson, J. & Nabo, A. (2021). A Simulation Study on Effects of Platooning Gaps on Drivers of Conventional Vehicles in Highway Merging Situations. IEEE transactions on intelligent transportation systems (Print)
Open this publication in new window or tab >>A Simulation Study on Effects of Platooning Gaps on Drivers of Conventional Vehicles in Highway Merging Situations
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2021 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016Article in journal (Refereed) Accepted
Abstract [en]

Platooning refers to a group of vehicles that--enabled by wireless vehicle-to-vehicle (V2V) communication and vehicle automation--drives with short inter-vehicular distances. Before its deployment on public roads, several challenging traffic situations need to be handled. Among the challenges are cut-in situations, where a conventional vehicle--a vehicle that has no automation or V2V communication--changes lane and ends up between vehicles in a platoon. This paper presents results from a simulation study of a scenario, where a conventional vehicle, approaching from an on-ramp, merges into a platoon of five cars on a highway. We created the scenario with four platooning gaps: 15, 22.5, 30, and 42.5 meters. During the study, the conventional vehicle was driven by 37 test persons, who experienced all the platooning gaps using a driving simulator. The participants' opinions towards safety, comfort, and ease of driving between the platoon in each gap setting were also collected through a questionnaire. The results suggest that a 15-meter gap prevents most participants from cutting in, while causing potentially dangerous maneuvers and collisions when cut-in occurs. A platooning gap of at least 30 meters yield positive opinions from the participants, and facilitating more smooth cut-in maneuvers while less collisions were observed. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2021
Keywords
cooperative adaptive cruise control., cut-in, Driving simulator, highway platooning, Vehicle to vehicle communications, Highway merging, On-ramp, Public roads, Simulation studies, Traffic situations, V2V communications, Vehicle automations, Road vehicles
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-52104 (URN)10.1109/TITS.2020.3040085 (DOI)2-s2.0-85098774184 (Scopus ID)
Available from: 2021-01-26 Created: 2021-01-26 Last updated: 2022-01-14Bibliographically approved
Englund, C., Aksoy, E. E., Alonso-Fernandez, F., Cooney, M. D., Pashami, S. & Åstrand, B. (2021). AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control. Smart Cities, 4(2), 783-802
Open this publication in new window or tab >>AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control
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2021 (English)In: Smart Cities, ISSN 2624-6511, Vol. 4, no 2, p. 783-802Article in journal (Refereed) Published
Abstract [en]

Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, smart traffic control and driver modeling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, availability of data from different stakeholders is necessary. Further, though AI technologies provide accurate predictions and classifications, there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability: models can have difficulty explaining how they came to certain conclusions, so it is difficult for humans to trust them.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
smart cities, artificial intelligence, perception, smart traffic control, driver modeling
National Category
Computer Sciences
Identifiers
urn:nbn:se:ri:diva-55191 (URN)10.3390/smartcities4020040 (DOI)
Available from: 2021-07-05 Created: 2021-07-05 Last updated: 2023-11-06Bibliographically approved
Autili, M., Chen, L., Englund, C., Pompilio, C. & Tivoli, M. (2021). Cooperative Intelligent Transport Systems: Choreography-Based Urban Traffic Coordination. IEEE transactions on intelligent transportation systems (Print), 22(4), 2088-2099
Open this publication in new window or tab >>Cooperative Intelligent Transport Systems: Choreography-Based Urban Traffic Coordination
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2021 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 22, no 4, p. 2088-2099Article in journal (Refereed) Published
Abstract [en]

With the emerging connected automated vehicles, 5G and Internet of Things (IoT), vehicles and road infrastructure become connected and cooperative, enabling Cooperative Intelligent Transport Systems (C-ITS). C-ITS are transport system of systems that involves many stakeholders from different sectors. While running their own systems and providing services independently, stakeholders cooperate with each other for improving the overall transport performance such as safety, efficiency and sustainability. Massive information on road and traffic is already available and provided through standard services with different protocols. By reusing and composing the available heterogeneous services, novel value-added applications can be developed. This paper introduces a choreography-based service composition platform, i.e. the CHOReVOLUTION Integrated Development and Runtime Environment (IDRE), and it reports on how the IDRE has been successfully exploited to accelerate the reuse-based development of a choreography-based Urban Traffic Coordination (UTC) application. The UTC application takes the shape of eco-driving services that through real-time eco-route evaluation assist the drivers for the most eco-friendly and comfortable driving experience. The eco-driving services are realized through choreography and they are exploited through a mobile app for online navigation. From specification to deployment to execution, the CHOReVOLUTION IDRE has been exploited to support the realization of the UTC application by automatizing the generation of the distributed logic to properly bind, coordinate and adapt the interactions of the involved parties. The benefits brought by CHOReVOLUTION IDRE have been assessed through the evaluation of a set of Key Performance Indicators (KPIs).

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2021
Keywords
C-ITS, eco-driving, Service choreographies, service composition, system of systems.
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:ri:diva-52916 (URN)10.1109/TITS.2021.3059394 (DOI)2-s2.0-85101755202 (Scopus ID)
Available from: 2021-04-09 Created: 2021-04-09 Last updated: 2023-05-25Bibliographically approved
Projects
DIFFUSE: Disentanglement of Features For Utilization in Systematic Evaluation [2021-05038_Vinnova]; RISE - Research Institutes of Sweden (2017-2019) (Closed down 2019-12-31)
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1043-8773

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