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

Emerging privacy challenges and approaches in CAV systems

The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

A Recent Connected Vehicle - IoT Automotive Application Based on Communication Technology

Realizing the full potential of vehicle communications depends in large part on the infrastructure of vehicular networks. As more cars are connected to the Internet and one another, new technological advancements are being driven by a multidisciplinary approach. As transportation networks become more complicated, academic, and automotive researchers collaborate to offer their thoughts and answers. They also imagine various applications to enhance mobility and the driving experience. Due to the requirement for low latency, faster throughput, and increased reliability, wireless access technologies and an appropriate (potentially dedicated) infrastructure present substantial hurdles to communication systems. This article provides a comprehensive overview of the wireless access technologies, deployment, and connected car infrastructures that enable vehicular connectivity. The challenges, issues, services, and maintenance of connected vehicles that rely on infrastructure-based vehicular communications are also identified in this paper

Autonomous Vehicles an overview on system, cyber security, risks, issues, and a way forward

This chapter explores the complex realm of autonomous cars, analyzing their fundamental components and operational characteristics. The initial phase of the discussion is elucidating the internal mechanics of these automobiles, encompassing the crucial involvement of sensors, artificial intelligence (AI) identification systems, control mechanisms, and their integration with cloud-based servers within the framework of the Internet of Things (IoT). It delves into practical implementations of autonomous cars, emphasizing their utilization in forecasting traffic patterns and transforming the dynamics of transportation. The text also explores the topic of Robotic Process Automation (RPA), illustrating the impact of autonomous cars on different businesses through the automation of tasks. The primary focus of this investigation lies in the realm of cybersecurity, specifically in the context of autonomous vehicles. A comprehensive analysis will be conducted to explore various risk management solutions aimed at protecting these vehicles from potential threats including ethical, environmental, legal, professional, and social dimensions, offering a comprehensive perspective on their societal implications. A strategic plan for addressing the challenges and proposing strategies for effectively traversing the complex terrain of autonomous car systems, cybersecurity, hazards, and other concerns are some resources for acquiring an understanding of the intricate realm of autonomous cars and their ramifications in contemporary society, supported by a comprehensive compilation of resources for additional investigation. Keywords: RPA, Cyber Security, AV, Risk, Smart Car

Designing and implementing a GPS-based vehicle navigation application for Eclipse Kuksa

Abstract. With the development of the Internet of Things (IoT), connected cars are rapidly becoming an essential milestone in the design of intelligent transportation systems and a key element in smart city design. Connected cars use a three-layer client-connection-cloud architecture, and car sensors are located at the client layer. This architecture provides the driver with a large amount of data about the external environment, which reduces the number of traffic accidents and helps the car drive safely. Driving safety is the most critical design factor for next-generation vehicles. The future vision of the automotive industry is self-driving cars. However, it faces some challenges. Eclipse Kuksa provides solutions to challenges in the field of connected cars. A comprehensive ecosystem includes a complete tool stack for connected vehicles, including a vehicle platform, a cloud platform, and an application development Integrated Development Environment (IDE). Its essential function is to collect, store, and analyze vehicle data and transmit various information in the cloud. This master’s thesis aims to investigate a Global Positioning System (GPS) -based vehicle navigation application on the vehicle and cloud platforms of Eclipse Kuksa, understand how to develop a GPS-based vehicle navigation application using the Eclipse Kuksa software platform, and discuss the advantages and challenges of using Eclipse Kuksa to develop vehicle applications. The research methods are Design Science Research (DSR) and literature review. System development is carried out following the Design Science Research Methodology (DSRM) Process, developed and evaluated on the vehicle navigation application. The application artifact consists of the Eclipse Kuksa vehicle platform and cloud platform. The steps described in this paper can be used to build vehicle applications in Eclipse Kuksa. This paper also explains the benefits and challenges of using Eclipse Kuksa to develop vehicle applications. The main benefit is that open source solutions break the long-term closed development model of the automotive industry and establish a vehicle-to-cloud solution standard to meet the IoT challenges to the automotive industry. Simultaneously the challenge of using Eclipse Kuksa is the complexity of environment construction and the software and hardware compatibility