784 research outputs found
On the needs and requirements arising from connected and automated driving
Future 5G systems have set a goal to support mission-critical Vehicle-to-Everything (V2X) communications and they contribute to an important step towards connected and automated driving. To achieve this goal, the communication technologies should be designed based on a solid understanding of the new V2X applications and the related requirements and challenges. In this regard, we provide a description of the main V2X application categories and their representative use cases selected based on an analysis of the future needs of cooperative and automated driving. We also present a methodology on how to derive the network related requirements from the automotive specific requirements. The methodology can be used to analyze the key requirements of both existing and future V2X use cases
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
NLOS Dies Twice: Challenges and Solutions of V2X for Cooperative Perception
Multi-agent multi-lidar sensor fusion between connected vehicles for
cooperative perception has recently been recognized as the best technique for
minimizing the blind zone of individual vehicular perception systems and
further enhancing the overall safety of autonomous driving systems. This
technique relies heavily on the reliability and availability of
vehicle-to-everything (V2X) communication. In practical sensor fusion
application scenarios, the non-line-of-sight (NLOS) issue causes blind zones
for not only the perception system but also V2X direct communication. To
counteract underlying communication issues, we introduce an abstract perception
matrix matching method for quick sensor fusion matching procedures and
mobility-height hybrid relay determination procedures, proactively improving
the efficiency and performance of V2X communication to serve the upper layer
application fusion requirements. To demonstrate the effectiveness of our
solution, we design a new simulation framework to consider autonomous driving,
sensor fusion and V2X communication in general, paving the way for end-to-end
performance evaluation and further solution derivation.Comment: Submission to IEEE Vehicular Technology Magazin
Open Platforms for Connected Vehicles
L'abstract è presente nell'allegato / the abstract is in the attachmen
Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
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
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