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Cooperative perception represents an important technology to fulfil the higher automation levels of connected and automated mobility (CAM). In cooperative perception, the sensor data, either raw or processed, is shared among neighbour vehicles with the objective of enhancing or complementing the perception obtained by on-board sensors. The vehicle...
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... This impetus has led to the emergence of novel computing paradigms like fog computing and multi-access edge computing (MEC) [20], [21]. MEC stands out in vehicular applications for its support of high dynamicity and mobile clients [19], enabling diverse applications within the context of C-V2X such as cooperative autonomous driving [22], collision avoidance [23], platooning [24], [25], back situation awareness [3], video streaming between vehicles [4], [5] or Edge Dynamic Maps [6]. ...
Advances in connectivity and computing infrastructure facilitate the introduction of innovative Cooperative Intelligent Transport Systems (C-ITS) services. However, meeting the requirements of these highly demanding services calls for novel computing architectures that handle extensive device connections, minimize latency, and support multiple resource-intensive services concurrently. To overcome these challenges, this work presents an architecture that comprises three layers: (i) the on-board unit (OBU) mainly as a data producer, (ii) an intermediate edge layer where low-latency backend services can be deployed, and (iii) a cloud layer for non-real-time backend services. The OBU software stack implements the ETSI C-ITS standard and supports multicast over the cellular network. The edge layer includes an in-memory database, and the cloud layer a persistent database. Each layer has its own Application Programming Interface (API) for data consumption. We conducted several experiments to demonstrate the feasibility of our proposed system that ensures scalability and interconnection between vehicles, edge and cloud servers. We also assess the delay caused by each of the elements of the architecture, and we discuss the potential solutions for the identified issues.
... In a TSDB, old data can efficiently be removed from the database, and data can be queried for a specific time span. This work builds upon the work presented in [15], extending and improving several aspects: 1) adding the static layer and creating the EDM concept; 2) making the proposed method suitable for a variety of ITS use cases instead of being dedicated only to a vehicle discovery service; 3) adapting the architecture to address the challenge of node mobility [16]; 4), adding geolocation-based vehicle filtering using a geospatial indexing library; 5) improving the insertion of data to the database using data buffering and batch insertion; and 6) studying the performance and scalability of the new solution. The rest of the paper is organised as follows: Section II describes the architecture of the proposed EDM, Section III describes the implementation of the architecture, Section IV shows the results obtained in the experimentation, and Section V concludes the paper. ...
Cooperative Intelligent Transport Systems (C-ITS) create, share and process massive amounts of data which needs to be real-time managed to enable new cooperative and autonomous driving applications. Vehicle-to-Everything (V2X) communications facilitate information exchange among vehicles and infrastructures using various protocols. By providing computer power, data storage, and low latency capabilities, Multi-access Edge Computing (MEC) has become a key enabling technology in the transport industry. The Local Dynamic Map (LDM) concept has consequently been extended to its utilisation in MECs, into an efficient, collaborative, and centralised Edge Dynamic Map (EDM) for C-ITS applications. This research presents an EDM architecture for V2X communications and implements a real-time proof-of-concept using a Time-Series Database (TSDB) engine to store vehicular message information. The performance evaluation includes data insertion and querying, assessing the system's capacity and scale for low-latency Cooperative Awareness Message (CAM) applications. Traffic simulations using SUMO have been employed to generate virtual routes for thousands of vehicles, demonstrating the transmission of virtual CAM messages to the EDM.
... The 5G-MEC [13] approach enables a cooperative perception between AVs in a specified targeted area. The AVs are registered with MEC and share their information in a periodical manner, which are stored in a database. ...
Autonomous vehicles and smart roads are not new concepts and the undergoing development to empower the vehicles for higher levels of automation has achieved initial milestones. However, the transportation industry and relevant research communities still require making considerable efforts to create smart and intelligent roads for autonomous driving. To achieve the results of such efforts, the CCAM infrastructure is a game changer and plays a key role in achieving higher levels of autonomous driving. In this paper, we present a smart infrastructure and autonomous driving capabilities enhanced by CCAM infrastructure. Meaning thereby, we lay down the technical requirements of the CCAM infrastructure: identify the right set of the sensory infrastructure, their interfacing, integration platform, and necessary communication interfaces to be interconnected with upstream and downstream solution components. Then, we parameterize the road and network infrastructures (and automated vehicles) to be advanced and evaluated during the research work, under the very distinct scenarios and conditions. For validation, we demonstrate the machine learning algorithms in mobility applications such as traffic flow and mobile communication demands. Consequently, we train multiple linear regression models and achieve accuracy of over 94% for predicting aforementioned demands on a daily basis. This research therefore equips the readers with relevant technical information required for enhancing CCAM infrastructure. It also encourages and guides the relevant research communities to implement the CCAM infrastructure towards creating smart and intelligent roads for autonomous driving.
... • Browsing. This geo-binned index can be exploited by a discovery service to accelerate the selection of candidates in a ROI, discarding irrelevant data sources [21]. • Meeting. ...
The reliability and availability of network connectivity, which significantly varies with mobility, is crucial in Connected, Cooperative and Automated Mobility (CCAM). Handover and roaming are the most challenging situations in terms of connectivity of cellular networks, which require switching across cells of the same cellular network or between Public Land Mobile Networks (PLMNs). This paper proposes a set of solutions for vehicular applications to mitigate the impact of mobility in service continuity, including a dual modem solution that reduces the interruption time when switching PLMNs, an adaptive bitrate mechanism for media streaming that increases reliability, a WebRTC server acting as a gateway in media streaming sessions between vehicles, and a MEC discovery and handover method. The proposed solutions have been evaluated executing an Extended Sensors application in several commercial and experimental 5G Non-Standalone (NSA) and Stand Alone (SA) setups with different Multi-access Edge Computing (MEC), edge-cloud and cloud infrastructures to host services. It can be concluded from the results obtained that 5G networks have not yet achieved the required performance for CCAM, and that practitioners need to implement solutions and workarounds, such as the ones proposed in this work, to mitigate the issues. As lessons learnt from the deployment and experimentation, this paper also overviews a detailed set of problems and the proposed solutions that CCAM industry and cellular network stakeholders need to consider.
... WebRTC technology is a good option to send video flows between automotive systems [4]. It compiles different standard technologies to bridge peers from different network domains, such as Session Traversal Utilities for NAT (STUN) to negotiate endpoints behind a network infrastructure, Datagram Transport Layer Security (DTLS) to perform the credentials handshake and protect the data flows, and Real Time Transport Control Protocol (RTCP) to capture metrics which monitor network spanning bandwidth and round-trip time. ...
Vehicles shipping sensors for onboard systems are gaining connectivity. This enables information sharing to realize a more comprehensive understanding of the environment. However, peer communication through public cellular networks brings multiple networking hurdles to address, needing in-network systems to relay communications and connect parties that cannot connect directly. Web Real-Time Communication (WebRTC) is a good candidate for media streaming across vehicles as it enables low latency communications, while bringing standard protocols to security handshake, discovering public IPs and transverse Network Address Translation (NAT) systems. However, the end-to-end Quality of Service (QoS) adaptation in an infrastructure where transmission and reception are decoupled by a relay, needs a mechanism to adapt the video stream to the network capacity efficiently. To this end, this paper investigates a mechanism to apply changes on resolution, framerate and bitrate by exploiting the Real Time Transport Control Protocol (RTCP) metrics, such as bandwidth and round-trip time. The solution aims to ensure that the receiving onboard system gets relevant information in time. The impact on end-to-end throughput efficiency and reaction time when applying different approaches to QoS adaptation are analyzed in a real 5G testbed.
... The RTMaps [27] framework was used to test the performance of our LDM implementation in a real-time environment. RTMaps stands for Real-Time Multisensor Applications and is a component-based development and execution software tool specialising in ADAS functions. ...
... With the aim of putting the proposed implementation into practice and testing the system in a near-real case using the KITTI data set parsed to the OpenLABEL format, a simple ADAS function named VDS was defined. The application is based on the same VDS used in [27] to retrieve vehicles in front of an ego-vehicle. Using the geospatial position of an ego-vehicle and its perception of other dynamic objects stored into the iLDM, a query was executed against the database to obtain all the stored vehicles that are in a given radius of the ego-vehicle. ...
... Furthermore, m and n are half of the ellipse width and half of the ellipse height. Even though in [27], it has been defined as 50 m for the height and 14 m for the width, calculated as four times the standard lane width of a road, a simple GUI with sliders was created to modify this in real time. In addition, as the ellipse is oriented towards the heading of the ego-vehicle, h and k were calculated as follows. ...
Local dynamic map (LDM) is a key component in the future of autonomous and connected vehicles. An LDM serves as a local database with the necessary tools to have a common reference system for both static data (i.e., map information) and dynamic data (vehicles, pedestrians, etc.). The LDM should have a common and well-defined input system in order to be interoperable across multiple data sources such as sensor detections or V2X communications. In this work, we present an interoperable graph-based LDM (iLDM) using Neo4j as our database engine and OpenLABEL as a common data format. An analysis on data insertion and querying time to the iLDM is reported, including a vehicle discovery service function in order to test the capabilities of our work and a comparative analysis with other LDM implementations showing that our proposed iLDM outperformed in several relevant features, furthering its practical utilisation in advanced driver assistance system development.
In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing of data involving different aspects of the vehicular traffic. Moreover, new resource-hungry applications have been envisaged, such as autonomous driving or in-cruise entertainment, hence making the demand for computation and storage resources one of the most important challenges in vehicular networks. In this context, Mobile Edge Computing (MEC) has become the key technology to handle these problems by providing cloud-like capabilities at the edge of mobile networks to support delay-sensitive and computation-intensive tasks. In the meantime, researchers have envisaged use of onboard vehicle resources to extend the computing capabilities of MEC systems. This paper presents a comprehensive review of the most recent works related to MEC-assisted vehicular networks, as well as vehicle-assisted MEC systems. We illustrate the MEC system architecture and discuss its deployment in vehicular environments, as well as the key technologies to realize this integration. After that, we review the recent literature by identifying three different areas, i.e.: (i) MEC providing additional resources to vehicles (e.g., for task offloading); (ii) MEC enabling innovative vehicular applications (e.g., platooning), and (iii) vehicular networks providing additional resources to MEC systems. Finally, we discuss open challenges and future research directions, addressing the possible interplays between MEC systems and vehicular networks.
5G led to a digital revolution for networks by leveraging virtualisation techniques to manage software-based network functions through provided standard interfaces, which have matured recently for cloud infrastructure that is widely employed across domains and sectors. This undiscovered potential to adequately respond to concurrent and specialised traffic demands is promising for a wide spectrum of industries. Moreover, it exposes the networking ecosystem to prospects beyond the traditional value chain. However, the configuration, deployment and operation of a 5G network are challenging. Thus, different scientific and research entities have built their own open, evolvable and updateable testbed infrastructure that can be used for experimentation purposes. Such testbeds enable different stakeholders to integrate new systems or features exploiting new technologies, assess the performance of innovative services, and customise operation policies to find optimal setups from a cost-effective perspective. Furthermore, federations of infrastructure allow for wider and more complex experiments to be performed in distributed domains. However, numerous technical and procedural obstacles exist during the building of 5G network testbeds. In addition, some technical barriers persist despite the testing of alternatives and ongoing efforts within open-source systems and commercial equipment portfolios. All these limitations and challenges are relevant for experimenters and stakeholders as they attempt to determine the scope of 5G set expectations.
