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Machine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. Reacting to such situations requires accurate classification for uncommon events, which in turn depends on the selection of large, diverse, and high-quality training data. In fact, the data available at a vehicle (e.g., photos of road signs) may be affected by errors or have different levels of resolution and freshness. To tackle this challenge, we propose an active learning framework that, leveraging the information collected through onboard sensors as well as received from other vehicles, effectively deals with scarce and noisy data. Given the information received from neighboring vehicles, our solution: (i) selects which vehicles can reliably generate high-quality training data, and (ii) obtains a reliable subset of data to add to the training set by trading off between two essential features, i.e., quality and diversity. The results, obtained with different real-world datasets, demonstrate that our framework significantly outperforms state-of-the-art solutions, providing high classification accuracy with a limited bandwidth requirement for the data exchange between vehicles. Horizon 2020 Framework Programme;European Commission Scopus

Intelligent Transport Systems (ITS) have emerged as an integral part of smart cities, providing increased ease of mobility as well as efficiency and safety in vehicular traffic. Given its wide array of applications, ITS has also become a multidisciplinary field of work where vehicular communications, traffic control, ADAS (Advance Driver Assistance System) sensors, and vehicle dynamics have all to be accounted for. The study of such diverse aspects makes the evaluation of new ITS approaches, algorithms, and protocols not a small feat. For this reason, the availability of an effective, scalable, and comprehensive tool for the investigation and virtual validation of new ITS solutions is paramount. In this work, we present a simulation framework, called CoMoVe (Communication, Mobility, Vehicle dynamics), that effectively addresses the above need, as it enables the virtual validation of innovative solutions for vehicles that are both connected and equipped with ADAS sensors. Our framework encapsulates the important attributes of vehicle communication, road traffic, and dynamics into a single environment, by combining the strengths of different simulators. CoMoVe finds its use to evaluate the impact of vehicle connectivity, while imposing causality on vehicle dynamics and mobility. Such an assessment can greatly facilitate the development of control systems, algorithms, and protocols for real-world ITS.

This paper has been presented at IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops This demonstration shows how the 5G-TRANSFORMER platform, and more specifically the vertical slicer, is capable of arbitrating vertical services. In this context, arbitration refers to handling the various services of a given vertical customer according to their SLA requirements, service priorities, and resource budget available to the vertical. In this demo, a low priority video service of the automotive vertical is terminated when a high-priority intersection collision avoidance service needs to be instantiated and there are not enough resources allowing all services to be run in parallel. All these services are deployed with the help of the 5G-TRANSFORMER platform in a multi-PoP scenario, with PoPs in Barcelona (Spain), Turin (Italy), and Pisa (Italy), featuring a high variety of transport and computing technologies. Work supported in part by EU Commission H2020 5G-TRANSFORMER project (Grant No. 761536), H2020 5Growth project (Grant No. 856709), Spanish MINECO grant TEC2017-88373-R (5G-REFINE) and Generalitat de Catalunya grant 2017 SGR 1195.

Performance evaluation is integral to the vast majority of research on Vehicle-to-Vehicle (V2V) technology enabled connected cars. To validate ideas and concepts, researchers have been continuously striving towards the higher accuracy of simulation-based performance evaluation. However, many state-of-the-art network simulators lack comprehensive physical (PHY) layer models. More often, simplified representations of vehicular channel characteristics are used to achieve a trade-off between accuracy and performance. Vehicular channel modeling is a highly complex task because of its unique properties, for example, higher carrier frequency, rapid fluctuations in vehicular channels due to moving scatterers, and propagation in horizontal plane instead of a vertical plane with diffraction and reflection. Efficiently incorporating vehicular channel details into a single network simulator is infeasible; instead, a chain of simulation tools are used together. In this paper, we proposed a two-stage simulation framework which combines several layers of simulation tools into two distinct stages. During the first stage, a Geometry-based vehicular propagation model is used to characterize received signal strength among transmitter-receiver pairs. For this purpose, metropolitan area-wide 2.5D building geometry data and vehicular mobility traces are employed to represent the real-world environment. Subsequently, the output from the first stage is collected and fed as an input to the network simulator. Through extensive simulation-based studies, we analyze the difference between the proposed framework and standard propagation models implemented in the network simulator and their impact on the network-level performance metrics such as packet loss rate (PLR), throughput, latency, and jitter.

Internet of Things (IoT) is fast emerging and becoming an almost basic necessity in general life. The concepts of using technology in our daily life is not new, but with the advancements in technology, the impact of technology in daily activities of a person can be seen in almost all the aspects of life. Today, all aspects of our daily life, be it health of a person, his location, movement, etc. can be monitored and analyzed using information captured from various connected devices. This paper discusses one such use case, which can be implemented by the automobile industry, using technological advancements in the areas of IoT and Analytics. ‘Connected Car’ is a terminology, often associated with cars and other passenger vehicles, which are capable of internet connectivity and sharing of various kinds of data with backend applications. The data being shared can be about the location and speed of the car, status of various parts/lubricants of the car, and if the car needs urgent service or not. Once data are transmitted to the backend services, various workflows can be created to take necessary actions, e.g. scheduling a service with the car service provider, or if large numbers of care are in the same location, then the traffic management system can take necessary action. ’Connected cars’ can also communicate with each other, and can send alerts to each other in certain scenarios like possible crash etc. This paper talks about how the concept of ‘connected cars’ can be used to perform ‘predictive car maintenance’. It also discusses how certain technology components, i.e., Eclipse Mosquito and Eclipse Paho can be used to implement a predictive connected car use case.

In this paper, we present an enhanced Collision Avoidance (eCA) service that leverages vehicle connectivity through a cellular network to avoid vehicle collisions and increase road safety at intersections. The eCA service is assumed to be deployed at the edge of the network, thus curbing the latency incurred by the communication process. The core of the eCA service is composed of a Collision Avoidance Algorithm (CAA), and a Collision Avoidance Strategy (CAS). The former predicts the vehicle's future trajectory through the positional information advertised by periodic beacons and detects if two vehicles are on a collision course. The latter decides which of the vehicles potentially involved in a collision should yield. The vehicles are then notified of both the impending danger and of the actions needed to avoid it. We have simulated our solution using SUMO (Simulation of Urban MObility) and ns-3 (network simulator 3) with the LENA (LTE-EPC Network simulAtor) framework on a Manhattan-grid road topology, and observed its good performance in terms of avoided collisions percentage as a function of vehicle speed and different vehicles densities. This work was supported by the EU Commission through the H2020 5G-TRANSFORMER project (Grant No. 761536) and the H2020 5GROWTH project (Grant No. 856709), by the CARS Interdepartmental Lab at Politecnico di Torino, by the Spanish MINECO Grant TEC2017- 88373-R (5G-REFINE), and by the Generalitat de Catalunya Grant 2017 SGR 1195.

Content caching on the edge of 5G networks is an emerging and critical feature to quench the thirst for content of future connected cars. However, the tight packaging of 5G cells, the finite storage capacity at the edge, and the need for content availability while driving motivate the need to develop smart edge caching strategies adapted to the mobility characteristics of connected cars. In this paper, we propose a scheme called RICH (RoadsIde CacHe), which optimally caches content at edge nodes where connected vehicles require it most. In particular, our scheme is designed to ensure in-order delivery of content chunks to end users. Unlike blind popularity decisions, the probabilistic caching used by RICH accounts for the user mobility information that the system can realistically acquire. Furthermore, we provide a complete system architecture and define the protocols through which the different system entities can interact. We assess the performance of our approach against state-of-the-art solutions, under realistic mobility datasets and system scenarios. Our RICH edge caching scheme improves significantly the content availability at the caches and reduces the required backhaul bandwidth, with beneficial effects for both the end users and the network operators.

Cooperative systems are based on the periodical exchange of standardized information, thanks to which vehicles can advertise their presence, position and the direction they are moving to, and execute sophisticated C-ITS applications that can detect potentially dangerous situations and properly react. The technological pillar, which must enable a Vehicular ad Hoc Network (VANET), is now being debated: the candidates are the traditional WiFi-based approach and the upcoming cellular one. The application effectiveness, however, depends not only on the technology, but also on how fast it is adopted and becomes widespread, i.e., the so-called technology Penetration Rate (PR). In this paper, simulation is used to evaluate the Intersection Collision Avoidance (ICA) application for both candidate technologies, and evaluated as a function of the technology PR. This work was partially supported by FCA through the DiVe project, by the C.A.R.S. center at Politecnico di Torino, and by the H2020 5G-TRANSFORMER project (Project ID 761536)

Significant strides are being made in research towards security and privacy concerns associated with connected cars. Being a relatively novel research domain, it is crucial to examine the state of the art to formulate future research strategies. The objective of the paper is to aggregate the research progress made in identifying security and privacy concerns in connected cars, and the concomitant solutions. Using systematic mapping study, we reviewed the state of the art to address the research objective. Results indicate that in the last couple of years, there has been an exponential increase in investigation into this research topic. Compromised vehicular communication has received more attention than any other concern, and the popularity has only increased in recent years. A secure system design and infrastructure is the most common solution for all the identified concerns in this paper, but securing communication is the most popular one. Real-world applicability of the devised solutions is debatable, as most researchers have preferred simulations to empirical evaluation. Overall, insecure system design or infrastructure warrants more attention from solution point of view. In this context, the principles of Prevention by Design and Privacy by Design can be among the most effective strategies.

Self-driving and connected vehicles, communicating with one another (V2V technology) and with the road infrastructure (V2I technology), are a subject of extensive research nowadays and are expected to revolutionize the automotive industry in the near future. The major goal of the authors' work is to design a microscopic traffic simulation model for such vehicles, including a robust protocol for exchanging information. The question arises as to whether such communication system may efficiently improve travel quality while reducing the risk of collisions. For the purpose of their research the authors created and developed a simulation software. Their tool visualizes traffic flow for custom but simplified road maps. The transport infrastructure includes multiple junctions, optionally equipped with traffic lights, and roads with varying number of travel lanes. Each vehicle is assigned a fixed route leading to a randomly chosen destination point. Any decisions made by autonomous cars (regarding acceleration or turning maneuvers) are preceded by communication stages (retrieving necessary data, negotiations). In the paper we present fundamental concepts, assumptions and design of our model and simulation software, the authors also discuss potential issues relevant to their approach. As for the future work, the authors plan to implement their model in a large-scale agent-based traffic simulation software, Traffic Simulation Framework, so that further examination will be carried out for realistic road networks taken from the OpenStreetMap project. The authors also plan to apply machine learning techniques, so that self-driving vehicles, as well as traffic light controllers, will be able to learn how to develop the best strategy and by this way improve traffic safety and efficiency in atypical cases.

The road environment can be seen as a social situation and road user safety can be viewed as not just skills- based and rule-governed. Driver need to coordinate with each other to share the infrastructure. Lights, horn and speed information are the most frequently used tools to exchange information, limiting both the range and the bandwidth of the connectivity, which may lead to misunderstanding, irresponsibility and social isolation. With everywhere available connectivity and the broad penetration of social network services, the relationship between drivers on the road may gain more transparency, enabling social information to pass through the steel shell of the cars and giving opportunities to reduce anonymity and strengthen empathy. In this study, we utilized the “Four Side” communication model to describe different dimensions of information exchanging between drivers, which would be enhanced by the latest Vehicle to Vehicle communication technology. Under this model, three prototypes on a driving simulator were developed for further exploration to get the insights of social interaction between drivers by digital augmentation.

Internet-of-things technologies enable service providers such as insurance companies to collect vast amounts of privacy-sensitive data on car drivers. This paper studies whether and how privacy concerns of car owners can be compensated by offering monetary benefits. We study the case of usage based car insurance services for which the insurance fee is adapted to measured mileage and driving behaviour. A conjoint experiment shows that consumers prefer their current insurance products to usage based car insurance. However, when offered a minor financial compensation, they are willing to give up their privacy to car insurers. Consumers find privacy of behaviour and action more valuable than privacy of location and space. The study is a first to compare different forms of privacy in the acceptance of connected car services. Hereby, we contribute to more fine-grained understanding of privacy concerns in the acceptance of digital services, which will become more important in the upcoming Internet-of-things era.

Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is sound.