A New Vehicle Localization Scheme Based on Combined Optical Camera Communication and Photogrammetry

The demand for autonomous vehicles is increasing gradually owing to their enormous potential benefits. However, several challenges, such as vehicle localization, are involved in the development of autonomous vehicles. A simple and secure algorithm for vehicle positioning is proposed herein without massively modifying the existing transportation infrastructure. For vehicle localization, vehicles on the road are classified into two categories: host vehicles (HVs) are the ones used to estimate other vehicles' positions and forwarding vehicles (FVs) are the ones that move in front of the HVs. The FV transmits modulated data from the tail (or back) light, and the camera of the HV receives that signal using optical camera communication (OCC). In addition, the streetlight (SL) data are considered to ensure the position accuracy of the HV. Determining the HV position minimizes the relative position variation between the HV and FV. Using photogrammetry, the distance between FV or SL and the camera of the HV is calculated by measuring the occupied image area on the image sensor. Comparing the change in distance between HV and SLs with the change in distance between HV and FV, the positions of FVs are determined. The performance of the proposed technique is analyzed, and the results indicate a significant improvement in performance. The experimental distance measurement validated the feasibility of the proposed scheme

Quantified vehicles: data, services, ecosystems

Advancing digitalization has shown the potential of so-called Quantified Vehicles for gathering valuable sensor data about the vehicle itself and its environment. Consequently, (vehicle) Data has become an important resource, which can pave the way to (Data-driven) Services. The (Data-driven Service) Ecosystem of actors that collaborate to ultimately generate services, has only shaped up in recent years. This cumulative dissertation summarizes the author's contributions and includes a synopsis as well as 14 peer-reviewed publications, which contribute to answer the three research questions.Die Digitalisierung hat das Potenzial für Quantified Vehicles aufgezeigt, um Sensordaten über das Fahrzeug selbst und seine Umgebung zu sammeln. Folglich sind (Fahrzeug-)Daten zu einer wichtigen Ressource der Automobilindustrie geworden, da sie auch (datengetriebene) Services ermöglichen. Es bilden sich Ökosysteme von Akteuren, die zusammenarbeiten, um letztlich Services zu generieren. Diese kumulative Dissertation fasst die Beiträge des Autors zusammen und enthält eine Synopsis sowie 14 begutachtete Veröffentlichungen, die zur Beantwortung der drei Forschungsfragen beitragen

Real time collision warning system in the context of vehicle-to-vehicle data exchange based on drivings behaviours analysis

Worldwide injuries in vehicle accidents have been on the rise in recent years, mainly due to driver error regardless of technological innovations and advancements for vehicle safety. Consequently, there is a need for a reliable-real time warning system that can alert drivers of a potential collision. Vehicle-to-Vehicle (V2V) is an extensive area of ongoing research and development which has started to revolutionize the driving experience. Driving behaviour is a subject of extensive research which gains special attention due to the relationship between speeding behaviour and crashes as drivers who engage in frequent and extreme speeding behaviour are overinvolved in crashes. National Highway Traffic Safety Administration (NHTSA) set guidelines on how different vehicle automation levels may reduce vehicle crashes and how the use of on-board short-range sensors coupled with V2V technologies can help facilitate communication among vehicles. Based on the previous works, it can be seen that the assessment of drivers’ behaviours using their trajectory data is a fresh and open research field. Most studies related to driving behaviours in terms of acceleration�deceleration are evaluated at the laboratory scale using experimental results from actual vehicles. Towards this end, a five-stage methodology for a new collision warning system in the context of V2V based on driving behaviours has been designed. Real-time V2V hardware for data collection purposes was developed. Driving behaviour was analyzed in different timeframes prior obtained from actual driving behaviour in an urban environment collected from OBD-II adapter and GPS data logger of an instrumented vehicle. By measuring the in-vehicle accelerations, it is possible to categorize the driving behaviour into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. When the vehicle is in a risk situation, the system based on NRF24L01+PA/LNA, GPS, and OBD-II will pass a signal to the driver using a dedicated LCD and LED light signal. The driver can instantly decide to make the vehicle in a safe mood, effectively avoid the happening of vehicle accidents. The proposed solution provides two main functions: (1) the detection of the dangerous vehicles involved in the road, and (2) the display of a message informing the driver if it is safe or unsafe to pass. System performance was evaluated to ensure that it achieved the primary objective of improving road safety in the extreme behaviour of the driver in question either the safest (or the least aggressive) and the most unsafe (or the most aggressive). The proposed methodology has retained some advantages for other literature studies because of the simultaneous use of speed, acceleration, and vehicle location. The V2V based on driving behaviour experiments shows the effectiveness of the selected approach predicts behaviour with an accuracy of over 87% in sixty-four real-time scenarios presented its capability to detect behaviour and provide a warning to nearby drivers. The system failed detection only in few times when the receiving vehicle missed data due to high speed during the test as well as the distances between the moving vehicles, the data was not received correctly since the power transmitted, the frequency range of the signals, the antenna relative positions, and the number of in-range vehicles are of interest for the V2V test scenarios. The latter result supports the conclusion that warnings that efficiently and quickly transmit their information may be better when driver are under stress or time pressure

Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set

Is Europe in the Driver's Seat? The Competitiveness of the European Automotive Embedded Systems Industry

This report is one of a series resulting from a project entitled ¿Competitiveness by Leveraging Emerging Technologies Economically¿ (COMPLETE), carried out by JRC-IPTS. Each of the COMPLETE studies illustrates in its own right that European companies are active on many fronts of emerging and disruptive ICT technologies and are supplying the market with relevant products and services. Nevertheless, the studies also show that the creation and growth of high tech companies is still very complex and difficult in Europe, and too many economic opportunities seem to escape European initiatives and ownership. COMPLETE helps to illustrate some of the difficulties experienced in different segments of the ICT industry and by growing potential global players. This report reflects the findings of a study conducted by Egil Juliussen and Richard Robinson, two senior experts from iSuppli Corporation on the Competitiveness of the European Automotive Embedded Software industry. The report starts by introducing the market, its trends, the technologies, their characteristics and their potential economic impact, before moving to an analysis of the competitiveness of the corresponding European industry. It concludes by suggesting policy options. The research, initially based on internal expertise and literature reviews, was complemented with further desk research, expert interviews, expert workshops and company visits. The results were ultimately reviewed by experts and also in a dedicated workshop. The report concludes that currently ICT innovation in the automotive industry is a key competence in Europe, with very little ICT innovation from outside the EU finding its way into EU automotive companies. A major benefit of a strong automotive ICT industry is the resulting large and valuable employment base. But future maintenance of automotive ICT jobs within the EU will only be possible if the EU continues to have high levels of product innovation.JRC.DDG.J.4-Information Societ

Pasado y presente en el diagnóstico de los motores en los talleres de servicio automotor. Del vacuómetro a los sistemas basados en la nube

In this paper, a comparison between the past and the present in the diagnosis of the engines is made, starting with a brief historical review of the classic diagnosis of engines, common failures in some of its systems, and details of the diagnoses of the alternative engines, as background and contrast with the current modern techniques that are based on the analysis of the information provided by the on-board diagnostic tools and the signals of the sensor of the power train. This work seeks to update and extend the view on the practice of automotive diagnosis. To illustrate the use of those modern procedures, especially for service technicians, some operating parameters of a vehicle's engine are recorded for a test city driving tour, for later graphs and analysis of some operation maps and vehicle dynamics behaviors that can be obtained through the information obtained by the On-Board Diagnostics II system (OBD II).Resumen En el artículo se realizó una comparación entre el pasado y el presente en el diagnóstico de los motores, iniciando con una breve reseña histórica del diagnóstico clásico de motores, fallos comunes en algunos de sus sistemas y detalle de los procedimientos diagnósticos del conjunto móvil del motor. Todo ello, como antecedente y contraposición con las técnicas actuales modernas que se basan en el análisis de la información provista por las herramientas de diagnóstico de bordo y por las señales de los sensores del tren de potencia.  El trabajo realizado busca actualizar y extender la mirada sobre la práctica del diagnóstico automotor, con el propósito de ilustrar la utilización de los procedimientos modernos, en especial para los técnicos de servicio, algunos parámetros de operación del motor de un vehículo son registrados para un recorrido de conducción en ciudad, para posteriormente graficar y analizar algunos mapas de operación y comportamientos de la dinámica de tracción del vehículo que pueden obtenerse a través de la información obtenida por el sistema On Board Diagnostics II system (OBD II)

ITS Safety Ensuring Through Situational Management Methods

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. The paper is devoted to solving the problem of complex organizational and technical systems’ (COTS) safety improvement with the use of methods of situational management. The possibility to manage COTS with the use of decision support system, the intelligent core of which contains the object model of the precedent describing unwanted processes in the COTS, is considered. The object-oriented hierarchy model of the vehicle as a complex dynamical system is developed. To improve reliability and safety of the COTS the database of precedents is established. Precedents in this database are presented according to the developed model by which the analogues search procedure is performed and the best managerial decision is chosen

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified