Le développement de véhicules autonomes capables de se déplacer sur les routes urbaines peuvent fournir des avantages importants en matière de réduction des accidents, en augmentant le confort et aussi, permettant des réductions de coûts. Les véhicules Intelligents par exemple fondent souvent leurs décisions sur les observations obtenues à partir de différents capteurs tels que les LIDAR, les GPS et les Caméras. En fait, les capteurs de la caméra ont reçu grande attention en raison du fait de qu’ils ne sont pas cher, facile à utiliser et fournissent des données avec de riches informations. Les environnements urbains représentent des scénarios intéressant mais aussi très difficile dans ce contexte, où le tracé de la route peut être très complexe,la présence d’objets tels que des arbres, des vélos, des voitures peuvent générer des observations partielles et aussi ces observations sont souvent bruyants ou même manquant en raison de occlusions complètes. Donc, le processus de perception par nature doit être capable de traiter des incertitudes dans la connaissance du monde autour de la voiture. Tandis que la navigation routière et la conduite autonome en utilisant une connaissance préalable de l’environnement ont démontré avec succès, la compréhension et la navigation des scénarios généraux du environnement urbain avec peu de connaissances reste un problème non résolu. Dans cette thèse, on analyse ce problème de perception pour la conduite dans les milieux urbains basée sur la connaissance de l’environnement pour aussi prendre des décisions dans la navigation autonome. Il est conçu un système de perception robotique, qui permettre aux voitures de se conduire sur les routes, sans la nécessité d’adapter l’infrastructure, sans exiger l’apprentissage précédente de l’environnement, et en tenant en compte la présence d’objets dynamiques tels que les voitures.On propose un nouveau procédé basé sur l’apprentissage par la machine pour extraire le contexte sémantique en utilisant une paire d’images stéréo qui est fusionnée dans une grille d’occupation évidentielle pour modéliser les incertitudes d’un environnement urbain inconnu,en utilisant la théorie de Dempster-Shafer. Pour prendre des décisions dans la planification des chemins, il est appliqué l’approche de tentacule virtuel pour générer les possibles chemins à partir du centre de référence de la voiture et sur cette base, deux nouvelles stratégies sont proposées. Première, une nouvelle stratégie pour sélectionner le chemin correct pour mieux éviter les obstacles et de suivre la tâche locale dans le contexte de la navigation hybride, et seconde, un nouveau contrôle en boucle fermée basé sur l’odométrie visuelle et tentacule virtuel est modélisée pour l’exécution du suivi de chemin. Finalement, un système complet automobile intégrant les modules de perception, de planification et de contrôle sont mis en place et validé expérimentalement dans des situations réelles en utilisant une voiture autonome expérimentale, où les résultats montrent que l’approche développée effectue avec succès une navigation locale fiable basée sur des capteurs de la caméra. / The development of autonomous vehicles capable of getting around on urban roads can provide important benefits in reducing accidents, in increasing life comfort and also in providing cost savings. Intelligent vehicles for example often base their decisions on observations obtained from various sensors such as LIDAR, GPS and Cameras. Actually, camera sensors have been receiving large attention due to they are cheap, easy to employ and provide rich data information. Inner-city environments represent an interesting but also very challenging scenario in this context,where the road layout may be very complex, the presence of objects such as trees, bicycles,cars might generate partial observations and also these observations are often noisy or even missing due to heavy occlusions. Thus, perception process by nature needs to be able to dea lwith uncertainties in the knowledge of the world around the car. While highway navigation and autonomous driving using a prior knowledge of the environment have been demonstrating successfully,understanding and navigating general inner-city scenarios with little prior knowledge remains an unsolved problem. In this thesis, this perception problem is analyzed for driving in the inner-city environments associated with the capacity to perform a safe displacement basedon decision-making process in autonomous navigation. It is designed a perception system that allows robotic-cars to drive autonomously on roads, with out the need to adapt the infrastructure,without requiring previous knowledge of the environment and considering the presenceof dynamic objects such as cars. It is proposed a novel method based on machine learning to extract the semantic context using a pair of stereo images, which is merged in an evidential grid to model the uncertainties of an unknown urban environment, applying the Dempster-Shafer theory. To make decisions in path-planning, it is applied the virtual tentacle approach to generate possible paths starting from ego-referenced car and based on it, two news strategies are proposed. First one, a new strategy to select the correct path to better avoid obstacles and tofollow the local task in the context of hybrid navigation, and second, a new closed loop control based on visual odometry and virtual tentacle is modeled to path-following execution. Finally, a complete automotive system integrating the perception, path-planning and control modules are implemented and experimentally validated in real situations using an experimental autonomous car, where the results show that the developed approach successfully performs a safe local navigation based on camera sensors.

Orientadores: Janito Vaqueiro Ferreira, Alessandro Corrêa Victorino / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-26T17:57:25Z (GMT). No. of bitstreams: 1 Vitor_GiovaniBernardes_D.pdf: 28262004 bytes, checksum: eeccacc4c01faa822412782af2e96121 (MD5) Previous issue date: 2014 / Resumo: O desenvolvimento de veículos autônomos capazes de se locomover em ruas urbanas pode proporcionar importantes benefícios na redução de acidentes, no aumentando da qualidade de vida e também na redução de custos. Veículos inteligentes, por exemplo, frequentemente baseiam suas decisões em observações obtidas a partir de vários sensores tais como LIDAR, GPS e câmeras. Atualmente, sensores de câmera têm recebido grande atenção pelo motivo de que eles são de baixo custo, fáceis de utilizar e fornecem dados com rica informação. Ambientes urbanos representam um interessante mas também desafiador cenário neste contexto, onde o traçado das ruas podem ser muito complexos, a presença de objetos tais como árvores, bicicletas, veículos podem gerar observações parciais e também estas observações são muitas vezes ruidosas ou ainda perdidas devido a completas oclusões. Portanto, o processo de percepção por natureza precisa ser capaz de lidar com a incerteza no conhecimento do mundo em torno do veículo. Nesta tese, este problema de percepção é analisado para a condução nos ambientes urbanos associado com a capacidade de realizar um deslocamento seguro baseado no processo de tomada de decisão em navegação autônoma. Projeta-se um sistema de percepção que permita veículos robóticos a trafegar autonomamente nas ruas, sem a necessidade de adaptar a infraestrutura, sem o conhecimento prévio do ambiente e considerando a presença de objetos dinâmicos tais como veículos. Propõe-se um novo método baseado em aprendizado de máquina para extrair o contexto semântico usando um par de imagens estéreo, a qual é vinculada a uma grade de ocupação evidencial que modela as incertezas de um ambiente urbano desconhecido, aplicando a teoria de Dempster-Shafer. Para a tomada de decisão no planejamento do caminho, aplica-se a abordagem dos tentáculos virtuais para gerar possíveis caminhos a partir do centro de referencia do veículo e com base nisto, duas novas estratégias são propostas. Em primeiro, uma nova estratégia para escolher o caminho correto para melhor evitar obstáculos e seguir a tarefa local no contexto da navegação hibrida e, em segundo, um novo controle de malha fechada baseado na odometria visual e o tentáculo virtual é modelado para execução do seguimento de caminho. Finalmente, um completo sistema automotivo integrando os modelos de percepção, planejamento e controle são implementados e validados experimentalmente em condições reais usando um veículo autônomo experimental, onde os resultados mostram que a abordagem desenvolvida realiza com sucesso uma segura navegação local com base em sensores de câmera / Abstract: The development of autonomous vehicles capable of getting around on urban roads can provide important benefits in reducing accidents, in increasing life comfort and also in providing cost savings. Intelligent vehicles for example often base their decisions on observations obtained from various sensors such as LIDAR, GPS and Cameras. Actually, camera sensors have been receiving large attention due to they are cheap, easy to employ and provide rich data information. Inner-city environments represent an interesting but also very challenging scenario in this context, where the road layout may be very complex, the presence of objects such as trees, bicycles, cars might generate partial observations and also these observations are often noisy or even missing due to heavy occlusions. Thus, perception process by nature needs to be able to deal with uncertainties in the knowledge of the world around the car. While highway navigation and autonomous driving using a prior knowledge of the environment have been demonstrating successfully, understanding and navigating general inner-city scenarios with little prior knowledge remains an unsolved problem. In this thesis, this perception problem is analyzed for driving in the inner-city environments associated with the capacity to perform a safe displacement based on decision-making process in autonomous navigation. It is designed a perception system that allows robotic-cars to drive autonomously on roads, without the need to adapt the infrastructure, without requiring previous knowledge of the environment and considering the presence of dynamic objects such as cars. It is proposed a novel method based on machine learning to extract the semantic context using a pair of stereo images, which is merged in an evidential grid to model the uncertainties of an unknown urban environment, applying the Dempster-Shafer theory. To make decisions in path-planning, it is applied the virtual tentacle approach to generate possible paths starting from ego-referenced car and based on it, two news strategies are proposed. First one, a new strategy to select the correct path to better avoid obstacles and to follow the local task in the context of hybrid navigation, and second, a new closed loop control based on visual odometry and virtual tentacle is modeled to path-following execution. Finally, a complete automotive system integrating the perception, path-planning and control modules are implemented and experimentally validated in real situations using an experimental autonomous car, where the results show that the developed approach successfully performs a safe local navigation based on camera sensors / Doutorado / Mecanica dos Sólidos e Projeto Mecanico / Doutor em Engenharia Mecânica

Fully autonomous vehicles are expected to revolutionize transportation, reduce the cost of ownership, contribute to a cleaner environment, and prevent the majority of traffic accidents and related fatalities. Even though promising approaches for achieving full autonomy exist, developers and manufacturers have to overcome a multitude of challenged before these systems could find widespread adoption. This multiple case study explored the strategies some IT hardware and software developers of self-driving cars use to adapt traditional vehicle design frameworks to address consumer and regulatory requirements in autonomous vehicle designs. The population consisted of autonomous driving technology software and hardware developers who are currently working on fully autonomous driving technologies from or within the United States, regardless of their specialization. The theory of dynamic capabilities was the conceptual framework used for the study. Interviews from 7 autonomous vehicle hard and software engineers, together with 15 archival documents, provided the data points for the study. A thematic analysis was used to code and group results by themes. When looking at the results through the lens of dynamic capability theory, notable themes included regulatory uncertainty, functional safety, rapid iteration, and achieving a competitive advantage. Based on the findings of the study, implications for social change include the need for better regulatory frameworks to provide certainty, consumer education to manage expectations, and universal development standards that could integrate regulatory and design needs into a single approach.

Le véhicule autonome est un véhicule qui se conduira, à terme, sans aucune intervention du conducteur, quelle que soit la situation de conduite. Ce véhicule comprend une nouvelle fonction, nommée fonction AD, pour Autonomous Driving, en charge de la conduite autonome. Cette fonction peut se trouver dans des états différents (Active, Disponible par exemple) selon l'évolution des conditions environnementales. Le changement de ses états est géré par une fonction de Supervision, nommée Supervision AD. Le principal objet de ces travaux consiste à garantir que la fonction AD se trouve constamment dans un état sûr. Ceci revient à s'assurer que la Supervision AD respecte l'ensemble des exigences fonctionnelles et de sûreté qui spécifient son comportement. Ces deux types d'exigences sont émis par deux métiers distincts : l'Architecte Métier Système (AMS) et le pilote Sûreté de Fonctionnement (SdF). Ces deux disciplines d'ingénierie, bien qu'elles contribuent à la conception d'une même fonction, se distinguent en de nombreux points : objectifs, contraintes, planning, outils... Dans notre cas d'étude, ces différences s'illustrent par les exigences considérées : les exigences fonctionnelles sont allouées à la fonction AD globale, tandis que les exigences de sûreté spécifient le comportement de sous-fonctions locales redondantes assurant une continuité de service en cas de défaillance. La mise en cohérence de ces deux perspectives métier au plus tôt dans le cycle de conception et dans un contexte industriel, est la problématique centrale traitée. Les enjeux de SdF soulevés par le véhicule autonome rendent ce problème primordial pour les constructeurs automobiles. Afin de répondre à ces préoccupations, nous avons proposé une démarche outillée et collaborative de conception sûre de la Supervision AD. Cette démarche est intégrée dans les processus normatifs en vigueur (normes ISO 15288 et ISO 26262) ainsi que dans les processus de conception internes chez Renault. Elle est fondée sur la vérification formelle par model checking, la composition parallèle d'automates finis et l'expertise métier. Cette démarche prône l'utilisation d'un même formalisme (l'automate à états finis) par les deux métiers pour mener à bien des activités partageant un objectif de modélisation commun : la vérification d'exigences de comportement en phase amont de conception. Une méthode pour traduire les exigences en propriétés formelles et construire les modèles d'état a été déployée. Il en résulte une consolidation progressive des exigences traitées, initialement rédigées en langage naturel. Les potentielles ambigüités, incohérences et incomplétudes sont exhibées et traitées. / The Autonomous Vehicle is meant to drive itself, without any driver intervention, whatever the driving situation. This vehicle includes a new function, called AD, for Autonomous Driving, function. This function can be in different states (Available, Active for example) according to environmental conditions evolution. This states change is managed by a supervision function, named AD Supervision. The main goal of my works consists in guaranteeing that AD function remains always in a safe state. In other words, the AD Supervision must always respect all the functional and safety requirements that specify its behavior. These two requirements types are produced by two different professions: the System Architect (SA) and the Safety Engineer (SE). These two fields contribute to the design of the same function but distinguish at several aspects: objectives, constraints, planning, tools… In our case study, these differences are illustrated by considered requirements: the functional requirements are allocated to global AD function, while the safety requirements specify the behavior of local redundant sub-functions ensuring a continuous service in case of failure. The consistency of the two perspectives as early as possible in the design phase and in an industrial context, is the central problematic addressed. The safety issues due to Autonomous Vehicle make this topic essential for the automotive manufacturers. To meet these concerns, we proposed a tooled and collaborative approach for safe design of AD Supervision. This approach is integrated in the normative processes (standards ISO 26262 and ISO 15288) as well as in the internal design processes at Renault. It is based on formal verification by model checking, parallel composition of finite sate automata and technical expertise. This approach advocates the utilization of a same formalism (state automata) by the two professions to perform activities sharing a common goal: behavior requirements verification in preliminary design phase. A method to translate requirements into formal properties and to build state models has been deployed. The result is a progressive consolidation of treated requirements, initially expressed in free natural language. The potential ambiguities, inconsistencies and incompleteness are exhibited and treated. Two main contributions are in this way illustrated: highlighting of several formal credible (i.e. validated by expertise) specifications from informal requirements; and precise definition of technical expertise role (milestones, planning). However, this reinforcement – in silos – of the two profession viewpoints does not guarantee that they are mutually consistent. Thus, we proposed a convergence method, relying on expertise and on parallel composition of state automata, for the comparison of local and global views.

The aim of this diploma thesis is the research of building polygons for the testing of autonomous vehicles, from the point of view of road technology and also designing aspects. In the thesis are mentioned 9 most important world test polygons and their description of design parameters. There are described particular stages of automation from foreign organizations which are concerned with research and development in the automotive industry. In addition, there are described basic advanced driver assistance systems and connectivity between vehicles and infrastructure. Conclusion also contains the assessment of existing aerodrome test areas for autonomous vehicles.

Unmanned aerial vehicle (UAV) is an expanding interest in numerous industries for various applications. Increasing development of UAVs is happening worldwide, where various sensor attachments and functions are being added. The multi-function UAV can be used within areas where they have not been managed before. Because of their accessibility, cheap purchase, and easy-to-use, they replace expensive systems such as helicopters- and airplane-surveillance. UAV are also being applied into surveillance, combing object detection to video-surveillance and mobility to finding an object from the air without interfering with vehicles or humans ground. In this thesis, we solve the problem of using UAV on autonomous sites, finding an object and critical situation, support autonomous site operators with an extra safety layer from UAVs camera. After finding an object on such a site, uses GPS-coordinates from the UAV to see and place the detected object on the site onto a gridmap, leaving a coordinate-map to the operator to see where the objects are and see if the critical situation can occur. Directly under the object detection, reporting critical situations can be done because of safety-distance-circle leaving warnings if objects come to close to each other. However, the system itself only supports the operator with extra safety and warnings, leaving the operator with the choice of pressing emergency stop or not. Object detection uses You only look once (YOLO) as main object detection Neural Network (NN), mixed with edge-detection for gaining accuracy during bird-eye-views and motion-detection for supporting finding all object that is moving on-site, even if UAV cannot find all the objects on site. Result proofs that the UAV-surveillance on autonomous site is an excellent way to add extra safety on-site if the operator is out of focus or finding objects on-site before startup since the operator can fly the UAV around the site, leaving an extra-safety-layer of finding humans on-site before startup. Also, moving the UAV to a specific position, where extra safety is needed, informing the operator to limit autonomous vehicles speed around that area because of humans operation on site. The use of single object detection limits the effects but gathered object detection methods lead to a promising result while printing those objects onto a global positions system (GPS) map has proposed a new field to study. It leaves the operator with a viewable interface outside of object detection libraries.

This thesis describes the mechanical design of a prototype vehicle developed for a company located in California. The project was based on an earlier vehicle located at KTH, Transport Labs, and investigated if the existing concept for the vehicle would work as a concept for an autonomous prototype, with focus on component layout and increased forces. The design of the vehicle is based on a concept with a carbon fiber bottom plate, two separate suspension modules with electric hub motors and steer by wire. In addition a steering interface, seats and a roll cage is added to the base. Quadrant symmetric design and four wheel steering/drive makes the vehicle move equally good forward and reverse. The steering is controlled by individual rotating actuators mounted at each wheel, meaning that the vehicle, apart from acquiring a low turning radius also can angle the wheel in the same direction and drive with so called crab steer where the car is moving sideways without rotating itself. The brake system contains a regular manual hydraulic brake system in parallel with an autonomous brake system. The project was started by generating a list of requirements. This was then considered when doing the design in CAD (Solid Edge). The design was validated with ADAMS (MBS) and ANSYS Workbench (FEA). The majority of the project was carried out in Sweden at KTH where the driveline of the vehicle was designed and assembled. The driveline was then transported to California where the vehicle was finalized and tested. The test carried out indicated that the concept was working as a prototype but that some of the components needed to be upgraded. All tests needed was not carried out which led to that the maximum speed of the vehicle was limited to 40 km/h Further durability-, and high load tests will be carried out in order to, with suitable safety, raise the maximum speed. The maximum steering angle of each wheel acquired was 23 degrees that, with four wheel steering, means an effective steering angle of 46 degrees. The cars minimum turning radius was around 5 meters. / Detta examensarbete beskriver den mekaniska konstruktionen av ett prototypfordon för ett företag beläget i Kalifornien. Projektet utgick från ett befintligt fordon på KTH, Transport Labs och undersökte hur vida det befintliga konceptet för det fordonet fungerade för en autonom prototyp, med särskilt hänseende till komponentplacering och ökade krafter. Fordonet är konstruerad runt en bottenplatta av kolfiber, två separata hjulupphängningar med elektriska navmotorer och så kallad ”steer by wire” samt kompletteras med ett förargränssnitt, säten och rullbur. Kvadrant symmetriska design och fyrhjuls styrning/drivning gör att fordonet för sig lika bra framåt som bakåt. Styrningen sköts av en individuell roterande motor fäst vid varje hjul vilket innebär att fordonet, utöver att få en låg svängradie, även kan vinkla alla hjul åt samma håll och uppnå så kallad krabbstyrning där bilen rör sig i sidled utan att själv rotera. Bromssystemet består av ett vanligt manuellt hydrauliskt bromssystem parallell kopplat med ett autonomt aktiverat bromssystem. Projektet inleddes med generering av en kravspecifikation. Denna låg sedan som grund för konstruktionen som genomfördes i Solid Edge (CAD). Konstruktionen validerades med hjälp av ADAMS (MBS) och ANSYS Workbench (FEM). Största delen av projektet genomfördes i Sverige på KTH där drivlinan av fordonets konstruerades och monterads. Denna flögs sedan till Kalifornien där fordonet färdigställdes och testades på plats. De genomförda testerna tydde på att konceptet fungerade bra som prototyp men att vissa komponenter behövde uppgraderas. Full testning han inte genomföras vilket ledde till att den maximala hastigheten begränsades till 40 km/h. Vidare uthållighets- och höglasttester kommer genomföras för att, på ett säkert sätt, kunna öka den maximala tillåtna hastigheten. Den maximala styrvinkeln för varje hjul uppgick till 23 grader vilket, med fyrhjulningsstyrning, innebär en effektiv styrvinkel på 46 grader. Bilens minimi svängradie uppgick till cirka 5 meter.

Indiana University-Purdue University Indianapolis (IUPUI) / Autonomous Emergency Braking (AEB) system uses vehicle’s on-board sensors such as radar, LIDAR, camera, infrared, etc. to detect the potential collisions, alert the driver and make safety braking decision to avoid a potential collision. Its limitation is that it requires clear line-of-sight to detect what is in front of the vehicle. Whereas, in current V2V (vehicle-to-vehicle communication) systems, vehicles communicate with each other over a wireless network and share information about their states. Thus the safety of a V2V system is limited to the vehicles with communication capabilities. Our idea is to integrate the complementary capabilities of V2V and AEB systems together to overcome the limitations of V2V and AEB systems. In a V2V-AEB system, vehicles exchange data about the objects information detected by their onboard sensors along with their locations, speeds, and movements. The object information detected by a vehicle and the information received through the V2V network is processed by the AEB system of the subject vehicle. If there is an imminent crash, the AEB system alerts the driver or applies the brake automatically in critical conditions to prevent the collision. To make V2V-AEB system advance, we have developed an intelligent heart Monitoring system and integrated it with the V2V-AEB system of the vehicle. The advancement of wearable and implantable sensors enables them to communicate driver’s health conditions with PC’s and handheld devices. Part of this thesis work concentrates on monitoring the driver’s heart status in real time by using fitness tracker. In the case of a critical health condition such as the cardiac arrest of a driver, the system informs the vehicle to take an appropriate operation decision and broadcast emergency messages over the V2V network. Thus making other vehicles and emergency services aware of the emergency condition, which can help a driver to get immediate medical attention and prevent accident casualties. To ensure that the effectiveness of the V2V-AEB system is not reduced by a time delay, it is necessary to study the effect of delay thoroughly and to handle them properly. One common practice to control the delayed vehicle trajectory information is to extrapolate trajectory to the current time. We have put forward a dynamic system that can help to reduce the effect of delay in different environments without extrapolating trajectory of the pedestrian. This method dynamically controls the AEB start braking time according to the estimated delay time in the scenario. This thesis also addresses the problem of communication overload caused by V2V-AEB system. If there are n vehicles in a V2V network and each vehicle detects m objects, the message density in the V2V network will be n*m. Processing these many messages by the receiving vehicle will take considerable computation power and cause a delay in making the braking decision. To prevent flooding of messages in V2V-AEB system, some approaches are suggested to reduce the number of messages in the V2V network that include not sending information of objects that do not cause a potential collision and grouping the object information in messages.

No description available.

Kraven på säkerhet och effektivitet ökar ständigt inom fordonsindustrin. För att uppfylla dessa strävar fordonstillverkare efter att uppnå en högre grad av autonomi, detta innebär dock att många problem måste lösas. Denna rapport behandlar ett av dessa, autonoma fordons möjlighet att identifiera utryckningsfordon. Målet är att presentera ett förslag på vilken teknik som anses mest lämpad för autonoma fordon att kommunicera med utryckningsfordon. Arbetet grundade sig i en förstudie där standarden ITS G5, IEEE 802.11g, ZigBee samt mobilnät analyserades utifrån deras tekniska specifikationer. Utifrån analysen presenterades tre situationer där de olika teknikernas användning ansågs begränsade, i tunnlar, i tät trafik samt på långa avstånd vid höga hastigheter. Dessa situationer ställde krav på teknikerna inom bland annat svarstid, räckvidd, överföringsförmåga samt möjlighet till direktkommunikation mellan fordonen. Utifrån dessa krav ställdes en jämförelsematris upp där de olika teknikernas prestanda jämfördes. Resultatet visar att ITS G5 och ZigBee har bäst prestanda på egen hand medan en kombination av mobilnät och ITS G5 uppnår högst prestanda. / The demand for safety and effectivity continuously increases within the automotive industry. One way to meet these demands is to achieve a higher level of autonomy, but to achieve the highest levels of autonomy there is a few problems to be solved along the way. This report treats one of these, an autonomous vehicle’s ability to identify emergency vehicles. The report, based on a pilot study, analyses the ITS G5 standard, IEEE 802.11g, Zigbee and mobile networks based on their technological specifications. From the analysis three situations are identified where the technologies are considered limited. These limitations are, but not limited to, reach, latency, data rates and ability to communicate vehicle to vehicle (V2V). The four technologies are then compared by these limitations in a matrix. The result shows that ITS G5 and ZigBee has the best performance by its own but the combination of mobile networks and ITS G5 shows the highest possible performance.