Agile Calibration Process of Full-Stack Simulation Frameworks for V2X Communications

Computer simulations and real-world car trials are essential to investigate the performance of Vehicle-to-Everything (V2X) networks. However, simulations are imperfect models of the physical reality and can be trusted only when they indicate agreement with the real-world. On the other hand, trials lack reproducibility and are subject to uncertainties and errors. In this paper, we will illustrate a case study where the interrelationship between trials, simulation, and the reality-of-interest is presented. Results are then compared in a holistic fashion. Our study will describe the procedure followed to macroscopically calibrate a full-stack network simulator to conduct high-fidelity full-stack computer simulations.Comment: To appear in IEEE VNC 2017, Torino, I

Exploiting Map Topology Knowledge for Context-predictive Multi-interface Car-to-cloud Communication

While the automotive industry is currently facing a contest among different communication technologies and paradigms about predominance in the connected vehicles sector, the diversity of the various application requirements makes it unlikely that a single technology will be able to fulfill all given demands. Instead, the joint usage of multiple communication technologies seems to be a promising candidate that allows benefiting from characteristical strengths (e.g., using low latency direct communication for safety-related messaging). Consequently, dynamic network interface selection has become a field of scientific interest. In this paper, we present a cross-layer approach for context-aware transmission of vehicular sensor data that exploits mobility control knowledge for scheduling the transmission time with respect to the anticipated channel conditions for the corresponding communication technology. The proposed multi-interface transmission scheme is evaluated in a comprehensive simulation study, where it is able to achieve significant improvements in data rate and reliability

Fine-grained traffic state estimation and visualisation

Tools for visualising the current traffic state are used by local authorities for strategic monitoring of the traffic network and by everyday users for planning their journey. Popular visualisations include those provided by Google Maps and by Inrix. Both employ a traffic lights colour-coding system, where roads on a map are coloured green if traffic is flowing normally and red or black if there is congestion. New sensor technology, especially from wireless sources, is allowing resolution down to lane level. A case study is reported in which a traffic micro-simulation test bed is used to generate high-resolution estimates. An interactive visualisation of the fine-grained traffic state is presented. The visualisation is demonstrated using Google Earth and affords the user a detailed three-dimensional view of the traffic state down to lane level in real time

Use of wireless, ad-hoc networks for proximity warning and collision avoidance in surface mines

Despite the record of progress achieved in the United States with respect to reducing fatal and non-fatal injuries in surface mines, both the number and severity of these injuries remain unacceptable. A large fraction of these injuries in surface mines are caused by collisions involving large haulage equipment such as trucks, dozers, and front-end loaders. There are two main contributing factors for these collisions: (i) the massive size of these vehicles, which causes several blind spots surrounding the vehicle for the driver, and (ii) the sheer momentum of these vehicles, which makes it hard to maneuver these vehicles and often necessitates a long response time to avoid collisions. The objective of this work is to investigate the use of different kinds of wireless networks in a distributed ad-hoc mode for providing timely warning about nearby personnel and vehicles, and to evaluate their performance using tests in an actual surface mine.;The contributions of this work are as follows: (i) A zone-based proximity warning system was developed and tested using low power IEEE 802.15.4 radios for detecting obstacles and vehicles at small distances (\u3c10m), with the information of the exact zone they are in, around the vehicle. (ii) For timely warning about approaching vehicles at relatively larger distances (10-100m), a GPS system was integrated with Wi-Fi (IEEE 802.11a/b/p) radios in an ad-hoc mode, where information about approaching vehicles can be known as soon as they come into range. A communication range test was performed in an actual surface mine setting to characterize the distances at which the warnings can be reliably received using each of the IEEE 802.11 family of radios. Both the proximity warning system and the Wi-Fi-based collision avoidance system were evaluated for feasibility at an operating surface coal mine in the southern United States

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