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

Improving Roadside Unit deployment in vehicular networks by exploiting genetic algorithms

Vehicular networks make use of the Roadside Units (RSUs) to enhance the communication capabilities of the vehicles in order to forward control messages and/or to provide Internet access to vehicles, drivers and passengers. Unfortunately, within vehicular networks, the wireless signal propagation is mostly affected by buildings and other obstacles (e.g., urban fixtures), in particular when considering the IEEE 802.11p standard. Therefore, a crowded RSU deployment may be required to ensure vehicular communications within urban environments. Furthermore, some applications, notably those applications related to safety, require a fast and reliable warning data transmission to the emergency services and traffic authorities. However, communication is not always possible in vehicular environments due to the lack of connectivity even employing multiple hops. To overcome the signal propagation problem and delayed warning notification time issues, an effective, smart, cost-effective and all-purpose RSU deployment policy should be put into place. In this paper, we propose the genetic algorithm for roadside unit deployment (GARSUD) system, which uses a genetic algorithm that is capable of automatically providing an RSU deployment suitable for any given road map layout. Our simulation results show that GARSUD is able to reduce the warning notification time (the time required to inform emergency authorities in traffic danger situations) and to improve vehicular communication capabilities within different density scenarios and complexity layouts

An Overview of Vehicle-to-Infrastructure Communication Technology

As a part of solutions to reduce problems associated with transportation in cities, technologies can have noticeable impacts. Due to efficiency and low costs, innovative transportation technologies can reshape and improve human’s transportation. This research aims to explore Vehicle-to-Infrastructure communication technology (V2I) and its benefits to safety, mobility, and environment. In addition, it explores the planning aspect of deploying V2I technology and its opportunities, challenges and concerns, and implication to communities. The research will also look at several case studies including pilot projects that have been taking place in the United States and studies that have been done to have a better understanding of the current situation of V2I technology and its future needs. Advisor: Rodrigo Cantarer

Vehicular Networks with Infrastructure: Modeling, Simulation and Testbed

This thesis focuses on Vehicular Networks with Infrastructure. In the examined scenarios, vehicular nodes (e.g., cars, buses) can communicate with infrastructure roadside units (RSUs) providing continuous or intermittent coverage of an urban road topology. Different aspects related to the design of new applications for Vehicular Networks are investigated through modeling, simulation and testing on real field. In particular, the thesis: i) provides a feasible multi-hop routing solution for maintaining connectivity among RSUs, forming the wireless mesh infrastructure, and moving vehicles; ii) explains how to combine the UHF and the traditional 5-GHz bands to design and implement a new high-capacity high-efficiency Content Downloading using disjoint control and service channels; iii) studies new RSUs deployment strategies for Content Dissemination and Downloading in urban and suburban scenarios with different vehicles mobility models and traffic densities; iv) defines an optimization problem to minimize the average travel delay perceived by the drivers, spreading different traffic flows over the surface roads in a urban scenario; v) exploits the concept of Nash equilibrium in the game-theory approach to efficiently guide electric vehicles drivers' towards the charging stations. Moreover, the thesis emphasizes the importance of using realistic mobility models, as well as reasonable signal propagation models for vehicular networks. Simplistic assumptions drive to trivial mathematical analysis and shorter simulations, but they frequently produce misleading results. Thus, testing the proposed solutions in the real field and collecting measurements is a good way to double-check the correctness of our studie

Evaluating On-demand Pseudonym Acquisition Policies in Vehicular Communication Systems

Standardization and harmonization efforts have reached a consensus towards using a special-purpose Vehicular Public-Key Infrastructure (VPKI) in upcoming Vehicular Communication (VC) systems. However, there are still several technical challenges with no conclusive answers; one such an important yet open challenge is the acquisition of shortterm credentials, pseudonym: how should each vehicle interact with the VPKI, e.g., how frequently and for how long? Should each vehicle itself determine the pseudonym lifetime? Answering these questions is far from trivial. Each choice can affect both the user privacy and the system performance and possibly, as a result, its security. In this paper, we make a novel systematic effort to address this multifaceted question. We craft three generally applicable policies and experimentally evaluate the VPKI system performance, leveraging two large-scale mobility datasets. We consider the most promising, in terms of efficiency, pseudonym acquisition policies; we find that within this class of policies, the most promising policy in terms of privacy protection can be supported with moderate overhead. Moreover, in all cases, this work is the first to provide tangible evidence that the state-of-the-art VPKI can serve sizable areas or domain with modest computing resources.Comment: 6 pages, 7 figures, IoV-VoI'1

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

Proposition of Augmenting V2X Roadside Unit to Enhance Cooperative Awareness of Heterogeneously Connected Road Users

Intelligent transportation and autonomous mobility solutions rely on cooperative awareness developed by exchanging proximity and mobility data among road users. To maintain pervasive awareness on roads, all vehicles and vulnerable road users must be identified, either cooperatively, where road users equipped with wireless capabilities of Vehicle-to-Everything (V2X) radios can communicate with one another, or passively, where users without V2X capabilities are detected by means other than V2X communications. This necessitates the establishment of a communications channel among all V2X-enabled road users, regardless of whether their underlying V2X technology is compatible or not. At the same time, for cooperative awareness to realize its full potential, non-V2X-enabled road users must also be communicated with where possible or, leastwise, be identified passively. However, the question is whether current V2X technologies can provide such a welcoming heterogeneous road environment for all parties, including varying V2X-enabled and non-V2X-enabled road users? This paper investigates the roles of a propositional concept named Augmenting V2X Roadside Unit (A-RSU) in enabling heterogeneous vehicular networks to support and benefit from pervasive cooperative awareness. To this end, this paper explores the efficacy of A-RSU in establishing pervasive cooperative awareness and investigates the capabilities of the available communication networks using secondary data. The primary findings suggest that A-RSU is a viable solution for accommodating all types of road users regardless of their V2X capabilities.Comment: 13 page

Determining the Interruption of Services While Performing V2I Communication Using the SPMD Prototype

The use of Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), Vehicle to Roadside Unit (V2R) and Vehicle to Other (V2X) communications are increasingly applied over existing and upcoming transportation means by the United States Department of Transportation (USDOT) and other federal agencies. From previous statistical data, these technologies would primarily avoid or mitigate vehicle crashes and would provide more safety, mobility and various other benefits on the roads (“Traffic Safety Facts 2012,” 2013; “Traffic Safety Facts 2013” 2014). During the communication processes between vehicles, infrastructures and roadside units’ various sensitive data such as positions and speed of the vehicles, are transmitted which are currently highly vulnerable. These facts are generated from this research experiment results performed on the provided data sets from the University of Michigan Transportation Research Institute (UMTRI). An interference to the vehicular communications is possible by intentional or unintentional malicious users or other elements which puts drivers at greater risk with the upcoming vehicular technology. Moreover, different agencies and private companies are utilizing collected data from the USDOT to improve the operational volume of roads and services while avoiding accidents. They are also trying to provide other third-party Internet-based services to the consumers based on the live streaming information. This research paper gives a detailed description of all aspects of the vehicular communications protocol (i.e. DSRC, CA, 802.11p protocol, smart infrastructure, etc.). This research paper will provide details of all identified security features (i.e. encryption methods, certificate management, physical securities, data management lifecycles, etc.) that have been applied to these mechanisms to protect the safety of drivers (Cronin, 2013). The USDOT has currently approved the implementation of a 5.9 GHz band, along with the 802.11p standard wireless protocol for dedicated short-range communications used in vehicular communication (Shankland, 2014). This research paper will also provide details of current standards and regulations which will be in effect for the upcoming vehicular technologies in the future in the US along with the susceptibilities to the interruptions of services. Finally, this research will utilize the actual data sets compiled using the actual safety pilot model deployment (SPMD) provided by the UMRTI researchers. The analysis of these results will validate that this protocol is susceptible to interference during communications. This will be shown by plotting the latitudinal and longitudinal coordinates and thus demonstrating the occurrence of gaps within communication (i.e. interference to the vehicular communication) in the existing SPMD prototype data sets

UNPREDICTABLE VEHICLES TRAJECTORY MAP RELYING ON INTERNATIONAL DRIVERS

The brilliant mobility of vehicles also makes routing far complicated once we lack reliable way to infer the long run location of vehicles. However, when thinking about a genuine deployment, the idea of full understanding from the trajectories of vehicles appears impractical because it raises several privacy concerns. The FPF strategy demands partial mobility information, i.e., the power of vehicles inside the urban cells and also the migration ratios between all pairs of urban cells. FPF doesn't consider anyone information. In addition, processing the trajectories of vehicles needs a large computing effort, and gathering similarly info is way from trivial. The brilliant mobility of vehicles also makes routing far complicated once we lack reliable way to infer the long run location of vehicles. Within this work we advise a deployment formula according to migration ratios between urban cells without counting on the person vehicles trajectories. Among several optimization targets, we maximize the amount of distinct vehicles contacting the infrastructure, a fascinating metric whenever we plan to collect and disseminate small traffic bulletins. However, the amount of distant vehicles increases extremely fast once we escape from the chosen urban cell. During hurry hrs the main roads get congested and also the motorists use secondary roads as a substitute for getting away the congestions. The aim of FPF would be to select individual’s urban cells presenting the greatest quantity of uncovered vehicles. FPF might be expressed being an Integer Straight line Programming Formulation. Our goal would be to evaluate the outcome from the mobility info on the deployment performance. We validated our programs by applying the Integer Straight line Programming Formulation. Such result shows that previous understanding from the trajectories from the vehicles isn’t mandatory for achieving a detailed-to-optimal deployment performance whenever we plan to disseminate small traffic bulletins