VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Geometry-based localization for GPS outage in vehicular cyber physical systems

Vehicular localization has witnessed significant attention due to the growing number of location-based services in vehicular cyber physical systems (VCPS). In vehicular localization, GPS outage is a challenging issue considering the growing urbanization including high rise buildings, multilevel flyovers and bridges. GPS-free and GPS-assisted cooperative localization techniques have been suggested in the literature for GPS outage. Due to the cost of infrastructure in GPS-free techniques, and the absence of location aware neighbors in cooperative techniques, efficient and scalable localization is a challenging task in VCPS. In this context, this paper proposes a geometry-based localization for GPS outage in VCPS (GeoLV). It is a GPS-assisted localization which reduces location-aware neighbor constraint of cooperative localization. GeoLV utilizes mathematical geometry to estimate vehicle location focusing on vehicular dynamics and road trajectory. The static and dynamic relocations are performed to reduce the impact of GPS outage on location-based services. A case study based comparative performance evaluation has been carried out to assess the efficiency and scalability of GeoLV. It is evident from the results that GeoLV handles both shorter and longer GPS outage problem better than the state-of-the-art techniques in VCPS

Architecture, Protocols, and Algorithms for Location-Aware Services in Beyond 5G Networks

The automotive and railway industries are rapidly transforming with a strong drive towards automation and digitalization, with the goal of increased convenience, safety, efficiency, and sustainability. Since assisted and fully automated automotive and train transport services increasingly rely on vehicle-to-everything communications, and high-accuracy real-time positioning, it is necessary to continuously maintain high-accuracy localization, even in occlusion scenes such as tunnels, urban canyons, or areas covered by dense foliage. In this paper, we review the 5G positioning framework of the 3rd Generation Partnership Project in terms of methods and architecture and propose enhancements to meet the stringent requirements imposed by the transport industry. In particular, we highlight the benefit of fusing cellular and sensor measurements and discuss required architecture and protocol support for achieving this at the network side. We also propose a positioning framework to fuse cellular network measurements with measurements by onboard sensors. We illustrate the viability of the proposed fusion-based positioning approach using a numerical example.Comment: 7 pages, 5 figures, accepted for publication in IEEE Communications Standards Magazin

FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of Vehicles

Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes network resources. To fill these gaps, this article proposes a vehicle cooperative positioning (CP) system based on federated learning (FedVCP), which makes full use of the potential of social Internet of Things (IoT) and collaborative edge computing (CEC) to provide high-precision positioning correction while ensuring user privacy. To the best of our knowledge, this article is the first attempt to solve the privacy of CP from a perspective of federated learning. In addition, we take the advantages of local cooperation through vehicle-to-vehicle (V2V) communications in data augmentation. For individual differences in vehicle positioning, we utilize transfer learning to eliminate the impact of such differences. Extensive experiments on real data demonstrate that our proposed model is superior to the baseline method in terms of effectiveness and convergence speed

5G evaluation platform for connected and autonomous vehicles

Connected vehicles are the next frontier in massive mobile communications. The field of vehicular communications has undergone a significant transformation and is interested in getting more vehicles connected to exchange essential information between vehicles and road infrastructure in order to improve traffic efficiency and safety. The introduction of the millimeter-wave (mmWave) region in 5G New Radio (NR), together with the latest release of 3rd Generation Partnership Project (3GPP) Release 16 (Rel. 16) to achieve higher data rates, autonomous vehicles are expected to push the limits of the cellular network by exploiting novel technologies, such as beamforming and massive MultipleInput Multiple-Output (MIMO). This potentially enables several Vehicle-to-Everything (V2X) use cases for cooperative automated driving and enhanced information services. The project proposes an approach of beam-based interference assessment for Vehicle-toVehicle (V2V) communications at mmWave. The perceived interference level is evaluated for a given beamset covering the full azimuthal range. This information provides useful insights on the quality of communications and the potential re-use rate of scheduled resources. In addition, the performance of 5G V2X physical-layer is evaluated by means of scheduling implementation