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What will be the mode of the data that today's connected cars accumulate has become a critical issue for the entire automotive sector and its related supply and service industries. If quanta of highly usable data remain largely with car manufacturers, they will become gatekeepers and the entire aftermarket will either fall into complete dependence on them or lose the ability to innovate and compete. Aftermarket leaders and EU institutions are working to ensure that this data is shared from manufacturers to other supply and service providers. However, the most sensitive data is of a personal nature and its widespread sharing in the name of open competition may conflict with the right to privacy and the protection of drivers' personal data. Protecting both competition and data at the same time can be awfully expensive, with negative impacts on consumption and available mobility. This paper seeks to explain this straitjacket of three not entirely consistent requirements and to show the possibilities for emerging legislation within its framework. It shows that there are not only convincing arguments but also strong lobbies behind each of the demands, which makes finding a compromise solution even more difficult. At the end of the analysis, a solution is proposed which, taking into account all the constraints, appears to be the least detrimental to the preservation of all protected rights and legitimate interests. [ABSTRACT FROM AUTHOR]

Connected cars are becoming commonplace, creating vast volumes of data that may contain or reveal information about drivers. It is imperative to understand drivers' perspectives on such data being collected and used by car manufacturers. Applying the Human-Data Interaction (HDI) framework - which centres the user and their experience - to this context, we conducted semi-structured interviews with 15 drivers. Interview transcripts revealed issues with understanding of car data (Legibility) and drivers’ sense of control over automotive data (Agency), across different circumstances (Negotiability). Our findings suggest that car manufacturers should enable learning, access, and control over car data via the mobile app in a coordinated fashion, as the privacy preferences of drivers are often based on perceived benefit or threat resulting from data collection. The ability to set data-sharing preferences in a time- and location- sensitive manner can help drivers navigate data sharing consent based on circumstances. These findings have implications for the consent procedures in modern cars as well as for the development of data-sharing programmes aimed at creation of climate-smart cities.

Options thinking is a powerful approach for managing uncertainty and change in digital environments. It is recognised as a structured process for identifying, developing, and realising options into novel products and services. At Volvo Cars, we have learned it can also become a powerful instrument for innovation renewal, although it can be difficult to apply because it challenges existing firm practices. We elaborate this tension by presenting digital options strategizing as a process of applying options thinking to negotiate capability gaps and configure innovation resources. Our clinical study reveals that this facilitates innovation renewal through emergent processes, practice-oriented design, and opportunity-driven management. [ABSTRACT FROM AUTHOR]

Connected Cars (CCs) and vehicle-to-everything (V2X) use cases require stringent reliability for safety and non-safety uses. With increasing network softwarisation, it has become easier to use multiple, redundant connectivity options instead of relying on a single network connectivity. But where should these redundant connections be managed? Is it at a network provider’s core network - i.e., supply side managed (SSM) - or at the CC - i.e., demand side managed (DSM)? In our work, we investigate the use of SSM and DSM for CCs on four separate days and across 800 kilometers of major / minor roads in South East England. For Day 1, we captured performance indicators, and determined hypothetical multi-operator configurations for four U.K. providers and a global Universal SIM. For Day 2, 3 & 4, we built and deployed a test-bed to actually implement network switching and understand performance (incl. for TCP & UDP) either on the road or in a stationary location. Based on our results, we make three contributions. First, we show that DSM can deliver superior performance for CCs more than any individual network (up to 28 percentage points in a hypothetical scenario), or SSM which had up to 4.8x longer page load times. Second, unlike other smartphone-only studies, our system-level study demonstrates that improvements of at least 12% can be obtained in a practical DSM field implementation for a CC. Third, we confirm that the advantage of DSM in a field implementation is higher for UDP traffic (23% better latency) compared to TCP (13%). [ABSTRACT FROM AUTHOR]

This paper proposes a decision-making framework for Connected Autonomous Vehicle interactions. It provides and justifies algorithms for strategic selection of control references for cruising, platooning and overtaking. The algorithm is based on the trade-off between energy consumption and time. The consequent cooperation opportunities originating from agent heterogeneity are captured by a game-theoretic cooperative-competitive solution concept to provide a computationally feasible, self-enforced, cooperative traffic management framework. [ABSTRACT FROM AUTHOR]

We conduct an examination of the preferences of drivers and passengers alike for in-vehicle interactions with a multistudy approach consisting of (1) a targeted literature survey of applications and user interfaces designed to support interactions with in-vehicle controls and systems based on gesture and voice input; (2) a large-scale survey (N = 160 participants) to understand drivers and passengers' preferences for driving and traveling by car; and (3) an end-user elicitation study (N = 40 drivers and passengers) to collect preferences for gesture and voice input commands for in-vehicle interaction. We analyze and discuss the gesture and voice commands proposed by our participants and describe their characteristics, such as production times for gesture input and the vocabulary size of voice commands. [ABSTRACT FROM AUTHOR]

The automotive industry has been emerging with a very fast rate and still, it is growing day by day. Internet of Things (IoT) is one of the reasons behind these changes in the automobile business. The prospects and potential of this innovation are extraordinary. The main enabling factor which provide promising paradigm is the integration of several technologies and communication solution. In this paper, a device is built which can generate an immediate response from the hospital if the vehicle get into the accident and driver is helpless to call the Ambulance. The aim of this work is to build a device which can increase the safety of driver and passenger. IoT technology helps us to achieve this target of building the device successfully. This project will reduce the lead time between accident occurred and information received by hospital about the accident. [ABSTRACT FROM AUTHOR]

Connected cars are equipped with a rich set of sensors, such as GPS, accelerometer, video cameras, and pollution detectors. The information generated by these sensors can be used to offer a wide range of on-demand services, such as congestion notification, parking lots, and video surveillance. These services need a reliable and low latency unicast communication scheme in order to efficiently deliver the information requested by drivers. In this paper, a local traffic aware unicast routing scheme is proposed. To overcome network fragmentation, the proposed scheme relies on base stations and virtual base stations to transmit information from source car to the destination car using backhaul link. In this model, as base stations are sparsely deployed in the junction areas, a car moving in the junction area acts as a virtual base station node to support the routing process in the absence of a base station. Moreover, it avoids the impact of unreliable channel on information delivery. In the proposed scheme, each base station and virtual base station uses the short status messages (beacons) exchanged by the cars to form a local database of car locations. The stored information is used to find a base station or virtual base station that offers a minimum delay path to the destination car. The simulation results show that the proposed scheme outperforms the existing routing schemes in terms of end-to-end delay and packet delivery ratio. The proposed scheme is also validated by a connected car prototype built in an indoor laboratory environment. [ABSTRACT FROM AUTHOR]

The development of automated driving is actively progressing, and connected cars are also under development. Connected cars are the technology of connecting vehicles to networks so that connected vehicles can enhance their services. Safety services are among the main services expected in connected car society. Cooperative perception belongs to safety services and improves safety by visualizing blind spots. This visualization is achieved by sharing sensor data via wireless communications. Therefore, the number of visualized blind spots highly depends upon the performance of wireless communications. In this paper, we analyzed the required sensor data rate to be shared for the cooperative perception in order to realize safe and reliable automated driving in an intersection scenario. The required sensor data rate was calculated by the combination of recognition and crossing decisions of an automated driving vehicle to adopt realistic assumptions. In this calculation, CVFH was used to derive tight requirements, and the minimum required braking aims to alleviate the traffic congestion around the intersection. At the end of the paper, we compare the required sensor data rate with the outage data rate realized by conventional and millimeter-wave communications, and show that millimeter-wave communications can support safe crossing at a realistic velocity.

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 article, I take up a highly visible theme in discourses, experimentation, and manufacture of connected cars and autonomous vehicles: disability. I analyze the leading ways in which this new kind of technology is imagined for particular users with disability, as in the highly publicized case of Google's pilot driverless vehicle promoted as a boon for blind people and those with vision impairments. Then, I try to stand this kind of framing of connected-cars-as-good-for-disability on its head, and discuss the implications for questions of emerging social technology, equality, diversity, and design. Reflecting on this analysis, I look at what disability tells us about connected cars, and, indeed, how we might rethink communication and technology. [ABSTRACT FROM AUTHOR]

Upcoming disruptive technologies around autonomous driving of connected cars have not yet been matched with appropriate security by design principles and lack approaches to incorporate proactive preventative measures in the wake of increased cyber-threats against such systems. In this paper, we introduce proactive anomaly detection to a use-case of hijacked connected cars to improve cyber-resilience. First, we manifest the opportunity of behavioral profiling for connected cars from recent literature covering related underpinning technologies. Then, we design and utilize a new data set file for connected cars influenced by the automatic dependent surveillance–broadcast surveillance technology used in the aerospace industry to facilitate data collection and sharing. Finally, we simulate the analysis of travel routes in real time to predict anomalies using predictive modeling. Simulations show the applicability of a Bayesian estimation technique, namely, Kalman filter. With the analysis of future state predictions based on the previous behavior, cyber-threats can be addressed with a vastly increased time window for a reaction when encountering anomalies. We discuss that detecting real-time deviations for malicious intent with the predictive profiling and behavioral algorithms can be superior in effectiveness than the retrospective comparison of known-good/known-bad behavior. When quicker action can be taken while connected cars encounter cyberattacks, more effective engagement or interception of command and control will be achieved. [ABSTRACT FROM PUBLISHER]

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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

As cartographers, we have long dreamt of creating the perfect map – one that precisely recreates the territory it seeks to represent. But, as many in the field will attest, at the heart of that endeavor has lay a great paradox: the closer to reality a map becomes, the less useful it tends to be, especially in today´s complex and crowded world. Maps have historically been abstractions of reality by necessity as people need not know every inch of the territory to get from A to B. And besides, there are limits to how much information the human brain can handle. But what if map data is not only designed for direct consumption by people but also to be read by machines capable of comprehending vast quantities of information? When we build maps that need to be understood only by software then the paradox falls away: one can indeed have a map which is close to 1:1 scale and at the same time useful to a machine such as a self-driving vehicle. HERE is working on such maps of the future through a combination of modern technology and traditional mapmaking expertise. At the forefront of its mapmaking is a global, highly trained team of geographic analysts, supported by mappers from the community, which contribute edits to the map via HERE’s Map Creator program. Beyond that, HERE is also deploying the industry’s most modern mapmaking technology too in the form of LIDAR data capture vehicles, which help HERE build very detailed 3D maps. It is these high definition maps – which HERE calls the HD Map – that will be the foundation for autonomous driving. It is these maps that also move us closer to the notion of the ‘perfect map’.

The automotive industry is undergoing a technological revolution with the development of Connected Cars. The empowerment of in-vehicle Ubiquitous Computing (UbiComp) does not only shape drivers' behaviour but is also shaped by the drivers' psychology. Drawing on academic literature, this paper will show that acceptance and success of UbiComp in the context of the Connected Car is dependent on social norms and behaviours. Major components such as security, privacy and the loss of autonomy need to be taken into account when modelling a Connected Car. In conclusion, the ongoing changes and trends towards connected cars can only be successful if Original Equipment Manufacturers (OEM) address socio-technological questions to smoothen a social adaptation to the driver and its environment. [ABSTRACT FROM AUTHOR]

As cartographers, we have long dreamt of creating the perfect map - one that precisely recreates the territory it seeks to represent. But, as many in the field will attest, at the heart of that endeavor has lay a great paradox: the closer to reality a map becomes, the less useful it tends to be, especially in today's complex and crowded world. Maps have historically been abstractions of reality by necessity as people need not know every inch of the territory to get from A to B. And besides, there are limits to how much information the human brain can handle. But what if map data is not only designed for direct consumption by people but also to be read by machines capable of comprehending vast quantities of information? When we build maps that need to be understood only by software then the paradox falls away: one can indeed have a map which is close to 1:1 scale and at the same time useful to a machine such as a self-driving vehicle. HERE is working on such maps of the future through a combination of modern technology and traditional mapmaking expertise. At the forefront of its mapmaking is a global, highly trained team of geographic analysts, supported by mappers from the community, which contribute edits to the map via HERE's Map Creator program. Beyond that, HERE is also deploying the industry's most modern mapmaking technology too in the form of LIDAR data capture vehicles, which help HERE build very detailed 3D maps. It is these high definition maps - which HERE calls the HD Map - that will be the foundation for autonomous driving. It is these maps that also move us closer to the notion of the 'perfect map'. [ABSTRACT FROM AUTHOR]