Localization of nodes is an important aspect in a vehicular ad-hoc network (VANET). Research has been done on various localization methods. Some are more apt for a specific purpose than others. To begin with, we give an overview of a vehicular ad-hoc network, localization methods, and how they can be classified. The distance bounding and verifiable trilateration methods are explained further with their corresponding algorithms and steps used for localization. Distance bounding is a range-based distance estimation algorithm. Verifiable trilateration is a popular geometric method of localization. A dedicated automated highway infrastructure can use distance bounding and/or trilateration to localize an automated vehicle on the highway. We describe a highway infrastructure for our analysis and test how well each of the methods performs, according to a security measure defined as spoofing probability. The spoofing probability is, simply put, the probability that a given point on the highway will be successfully spoofed by an attacker that is located at any random position along the highway. Spoofing probability depends on different quantities depending on the method of localization used. We compare the distance bounding and trilateration methods to a novel method using friendly jamming for localization. Friendly jamming works by creating an interference around the region whenever communication takes place between a vehicle and a verifier (belonging to the highway infrastructure, which is involved in the localization process using a given algorithm and localization method). In case of friendly jamming, the spoofing probability depends both on the position and velocity of the attacker and those of the target vehicle (which the attacker aims to spoof). This makes the spoofing probability much less for friendly jamming. On the other hand, the distance bounding and trilateration methods have spoofing probabilities depending only on their position. The results are summarized at the end of the last chapter to give an idea about how the three localization methods, i.e. distance bounding, verifiable trilateration, and friendly jamming, compare against each other for a dedicated automated highway infrastructure. We observe that the spoofing probability of the friendly jamming infrastructure is less than 2% while the spoofing probabilities of distance bounding and trilateration are 25% and 11%, respectively. This means that the friendly jamming method is more secure for the corresponding automated transportation system (ATS) infrastructure than distance bounding and trilateration. However, one drawback of friendly jamming is that it has a high standard deviation because the range of positions that are most vulnerable is high. Even though the spoofing probability is much less, the friendly jamming method is vulnerable to an attack over a large range of distances along the highway. This can be overcome by defining a more robust infrastructure and using the infrastructure's resources judiciously. This can be the future scope of our research. Infrastructures that use the radio resources in a cost effective manner to reduce the vulnerability of the friendly jamming method are a promising choice for the localization of vehicles on an ATS highway.

A transformative revolution in transportation is coming with the advent of Au-tonomous Vehicles (AVs), which are expected to increase mobility, reduce trafficcongestion, and save fuel. Although AVs present significant advantages, they alsopose substantial challenges, particularly when it comes to security and safety. Theaim of this study is to map out the existing knowledge in order to facilitate furtherresearch and development, which will hasten the rollout of secure and reliable au-tonomous vehicles. This, in turn, will enable a sustainable and efficient future fortransportation. Research on AV safety and security is reviewed in this thesis in acomprehensive systematic literature review. The search process identified a total of283 studies published between 2019 and 2022, out of which 24 studies were selectedthrough a multi-stage process according to our predefined protocol. Based on re-search topics in selected studies, our findings have a significant impact on the fieldof Artificial Intelligence and automated vehicles. Based on our findings, we canprovide a summary of current knowledge regarding the safety, security, and stabilityimplications of autonomous vehicles. Simulations, real-life experiments, and physi-cal tests were all used in the selected articles for evaluation. Aside from the excellentresults, we identified many limitations of the articles, including the limitations of thedata sets, the analysis of unusual events, and the verification practices.

Adaptive ramp metering (ARM) is a widely popular intelligent transportation system (ITS) tool that boasts the ability to reduce congestion and streamline traffic flow during peak hour periods while maintaining a lower implementation cost than traditional methods such as freeway widening. This thesis explores the effectiveness of ARM implementation on an 18 mile segment of the Interstate 80 (I-80) corridor in the Bay Area residing in northern California. Smaller segments of this particular segment were analyzed to determine the effective length of ARM on efficiency at various lengths originating from a known bottleneck location. Efficiency values were also compared against a control segment of the Interstate 280 (I-280) in San Jose to provide a test site experiencing similar traffic congestion but without any ARM implementation. An Empirical Bayes analysis was conducted to provide the foundation of a safety evaluation of the ramp metering implementation and determine a counterfactual estimate of expected collisions had ARM implementation not occurred. It was found that the installation of the ramp meters did allow for some marginal increases in efficiency but may not be entirely associated with ARM implementation due to a variety of external factors as well as showing inconsistent behavior between analyzed segments. Regarding safety, the predictive model estimates 32.8 collisions to occur along a 0.5 mile segment within a three-year timeframe if ARM were not installed, which implies substantial improvements in safety conditions. However additional efficiency and safety data within the “after” period may be necessary to provide a more robust and conclusive evaluation as the ARM system is still relatively new.

A door-to-door trip may involve multiple traffic modes. For example, travelers may drive to a subway station and make a transfer to rail transit; alternatively, people may also start their trips by walking/cycling to a bus/subway station and then take transit in most of the trip. A successful eco-route planning thus should be able to cover multiple traffic modes and offer intermodal routing suggestions. Developing such a system requires to address extensive concerns. The dissertation is a building block of the multi-modal energy-efficient routing system which is being developed and tested in the simulation environment before real applications. Four submodules have been developed in the dissertation as partial fulfillment of the simulation-based system: energy consumption modeling, subway system development, on-road vehicles dynamic eco-routing, and information effect on route choice behavior. Other submodules such as pedestrian/bicycle modeling will be studied in the future. Towards the research goal, the dissertation first develops fuel consumption models for on-road vehicles. Given that gasoline light duty vehicles (LDVs) and electric vehicles were modeled in previous studies, the research effort mainly focuses on heavy duty vehicles (HDVs). Specifically, heavy duty diesel trucks (HDDTs) as well as diesel and hybrid-electric transit buses are modeled. The models are developed based on the Virginia Tech Comprehensive Power-based Fuel consumption Modeling (VT-CPFM) framework. The results demonstrate that the model estimates are highly consistent with field observations as well as the estimates of the Comprehensive Modal Emissions Model (CMEM) and MOtor Vehicle Emissions Simulator (MOVES). It is also found that the optimum fuel economy cruise speed ranges between 32 and 52 km/h for the tested trucks and between 39 and 47 km/h for the tested buses on grades varying from 0% to 8%, which is significantly lower than LDVs (60-80 km/h). The dissertation then models electric train dynamics and energy consumption in support of subway simulation system development and trip energy estimation. The dynamics model varies throttle and brake level with running speed rather than assuming constants as was done by previous studies, and the energy consumption model considers instantaneous energy regeneration. Both models can be easily calibrated using non-engine data and implemented in simulation systems and eco-transit applications. The results of the dynamics modeling demonstrate that the proposed model can adequately capture instantaneous acceleration/deceleration behavior and thus produce realistic train trajectories. The results of the energy consumption modeling demonstrate that the model produces the estimates consistent with the National Transit Database (NTD) results, and is applicable for project-level analysis given its ability in capturing the energy consumption differences associated with train, route and operational characteristics. The most suitable simulation testbed for system development is then identified. The dissertation investigates four state-of-the-art microsimulation models (INTEGRATION, VISSIM, AIMSUM, PARAMICS). Given that the car-following model within a micro-simulator controls longitudinal vehicle motion and thus determines the resulting vehicle trajectories, the research effort mainly focuses on the performance of the built-in car-following models from the energy and environmental perspective. The vehicle specific power (VSP) distributions resulting from each of the car-following models are compared to the field observations. The results demonstrate that the Rakha-Pasumarthy-Adjerid (RPA) model (implemented in the INTEGRATION software) outperforms the Gipps (AIMSUM), Fritzsche (PARAMICS) and Wiedemann (VISSIM) models in generating accurate VSP distributions and fuel consumption and emission estimates. This demonstrates the advantage of the INTEGRATION model over the other three simulation models for energy and environmental analysis. A new eco-routing model, comprehensively considering microscopic characteristics, is then developed, followed by a numerical experiment to test the benefit of the model. With the resulting eco-routing model, an on-road vehicle dynamic eco-routing system is constructed for in-vehicle navigation applications, and tested for different congestion levels. The results of the study demonstrate that the proposed eco-routing model is able to generate reasonable routing suggestions based on real-time information while at the same time differentiate eco-routes between vehicle models. It is also found that the proposed dynamic eco-routing system achieves lower network-wide energy consumption levels compared to the traditional eco-routing and travel time routing at all congestion levels. The results also demonstrate that the conventional fuel savings relative to the travel time routing decrease with the increasing congestion level; however, the electric power savings do not monotonically vary with congestion level. Furthermore, the energy savings relative to the traditional eco-routing are also not monotonically related to congestion level. In addition, network configuration is demonstrated to significantly affect eco-routing benefits. The dissertation finally investigates the potential to influence driver behavior by studying the impact of information on route choice behavior based on a real world experiment. The results of the experiment demonstrate that the effectiveness of information in routing rationality depends upon the traveler's age, preferences, route characteristics, and information type. Specifically, information effect is less evident for elder travelers. Also, the provided information may not be contributing if travelers value other considerations or one route significantly outperforms the others. The results also demonstrate that, when travelers have limited experiences, strict information is more effective than variability information, and that the faster less reliable route is more attractive than the slower more reliable route; yet the difference becomes insignificant with experiences accumulation. The results of the study will be used to enhance system design through considering route choice incentives. / Ph. D.

No Brasil existe a perspectiva de crescimento expressivo do volume de dados a ser processado pelas prestadoras de serviços de rastreamento em decorrência do aumento natural do uso de sistemas de rastreamento e também para atender a Resolução 330 de 2009 e Deliberação 135 de 30/01/2013 do Conselho Nacional de Trânsito (CONTRAN). Este crescimento gera a necessidade da incorporação de ferramentas analíticas nos sistemas de gerenciamento do rastreamento e monitoramento de veículos e na gestão de risco, para aumentar a sua eficiência e atender o crescimento do mercado. O objetivo desta dissertação é de propor uma metodologia que permita caracterizar o comportamento de movimentação de um veículo, com a finalidade de auxiliar o processo de tomada de decisão no gerenciamento e monitoramento de veículos. A caracterização do comportamento de movimentação do veículo foi feita pela geração de um modelo analítico do comportamento de movimentação, coletando os dados pretéritos da posição espacial e temporal. Este modelo baseia-se na movimentação e considera os aspectos comportamentais espaciais e temporais de forma independente. A caracterização do comportamento gera informações para identificar o comportamento espacial e temporal do veículo monitorado para um determinado nível de confiabilidade. / In Brazil there is the prospect of growth in the volume of data to be processed by the tracking service providers due to the natural increase of the use of tracking systems and also to meet the Resolution 330 of 2009 and Resolution 135 of 01.30.2013 of the National Traffic Council (CONTRAN), due to this growth the need of incorporation of analytical tools in systems management tracking and monitoring of vehicles and risk management are created, to increase their efficiency and meet market growth. This study objective is to propose a methodology to characterize the moving vehicle behavior, in order to assist the process of decision making in management and vehicle tagging. The vehicle handling behavior will be characterized by generating an analytical model of the vehicle movement, collecting bygone data of spatial position and time. This model will consist of a motion model taking into account that the spatial and temporal aspects of behavior are taken independently. The behavior characterization generates reports able to identify the spatial and temporal behavior of the monitored vehicle for a given level of reliability.

The automotive domain is changing. On the way to more convenient, safe, and efficient vehicles, the role of electronic controllers and particularly software has increased significantly for many years, and vehicles have become software-intensive systems. Furthermore, vehicles are connected to the Internet to enable Advanced Driver Assistance Systems and enhanced In-Vehicle Infotainment functionalities. This widens the automotive software and system landscape beyond the physical vehicle boundaries to presently include as well external backend servers in the cloud. Moreover, the connectivity facilitates new kinds of distributed functionalities, making the vehicle a part of an Intelligent Transportation System (ITS) and thus an important example for a future Internet of Things (IoT). Manufacturers, however, are confronted with the challenging task of integrating these ever-increasing range of functionalities with heterogeneous or even contradictory requirements into a homogenous overall system. This requires new software platforms and architectural approaches. In this regard, the connectivity to fixed side backend systems not only introduces additional challenges, but also enables new approaches for addressing them. The vehicle-to-backend approaches currently emerging are dominated by proprietary solutions, which is in clear contradiction to the requirements of ITS scenarios which call for interoperability within the broad scope of vehicles and manufacturers. Therefore, this research aims at the development and propagation of a new concept of a universal distributed Automotive Service Delivery Platform (ASDP), as enabler for future automotive functionalities, not limited to ITS applications. Since Machine-to-Machine communication (M2M) is considered as a primary building block for the IoT, emergent standards such as the oneM2M service platform are selected as the initial architectural hypothesis for the realisation of an ASDP. Accordingly, this project describes a oneM2M-based ASDP as a reference configuration of the oneM2M service platform for automotive environments. In the research, the general applicability of the oneM2M service platform for the proposed ASDP is shown. However, the research also identifies shortcomings of the current oneM2M platform with respect to the capabilities needed for efficient communication and data exchange policies. It is pointed out that, for example, distributed traffic efficiency or vehicle maintenance functionalities are not efficiently treated by the standard. This may also have negative privacy impacts. Following this analysis, this research proposes novel enhancements to the oneM2M service platform, such as application-data-dependent criteria for data exchange and policy aggregation. The feasibility and advancements of the newly proposed approach are evaluated by means of proof-of-concept implementation and experiments with selected automotive scenarios. The results show the benefits of the proposed enhancements for a oneM2M-based ASDP, without neglecting to indicate their advantages for other domains of the oneM2M landscape where they could be applied as well.

No Brasil existe a perspectiva de crescimento expressivo do volume de dados a ser processado pelas prestadoras de serviços de rastreamento em decorrência do aumento natural do uso de sistemas de rastreamento e também para atender a Resolução 330 de 2009 e Deliberação 135 de 30/01/2013 do Conselho Nacional de Trânsito (CONTRAN). Este crescimento gera a necessidade da incorporação de ferramentas analíticas nos sistemas de gerenciamento do rastreamento e monitoramento de veículos e na gestão de risco, para aumentar a sua eficiência e atender o crescimento do mercado. O objetivo desta dissertação é de propor uma metodologia que permita caracterizar o comportamento de movimentação de um veículo, com a finalidade de auxiliar o processo de tomada de decisão no gerenciamento e monitoramento de veículos. A caracterização do comportamento de movimentação do veículo foi feita pela geração de um modelo analítico do comportamento de movimentação, coletando os dados pretéritos da posição espacial e temporal. Este modelo baseia-se na movimentação e considera os aspectos comportamentais espaciais e temporais de forma independente. A caracterização do comportamento gera informações para identificar o comportamento espacial e temporal do veículo monitorado para um determinado nível de confiabilidade. / In Brazil there is the prospect of growth in the volume of data to be processed by the tracking service providers due to the natural increase of the use of tracking systems and also to meet the Resolution 330 of 2009 and Resolution 135 of 01.30.2013 of the National Traffic Council (CONTRAN), due to this growth the need of incorporation of analytical tools in systems management tracking and monitoring of vehicles and risk management are created, to increase their efficiency and meet market growth. This study objective is to propose a methodology to characterize the moving vehicle behavior, in order to assist the process of decision making in management and vehicle tagging. The vehicle handling behavior will be characterized by generating an analytical model of the vehicle movement, collecting bygone data of spatial position and time. This model will consist of a motion model taking into account that the spatial and temporal aspects of behavior are taken independently. The behavior characterization generates reports able to identify the spatial and temporal behavior of the monitored vehicle for a given level of reliability.

The popularity of the wireless networks is so great that we will soon reach the point where most of the devices work based on that, but new challenges in wireless channel access will be created with these increasingly widespread wireless communications. Multi-channel CSMA protocols have been designed to enhance the throughput of the next generation wireless networks compared to single-channel protocols. However, their performance analysis still needs careful considerations. In this thesis, a set of techniques are proposed to model and analyse the CSMA protocols in terms of channel sensing and channel access. In that respect, the performance analysis of un-slotted multi-channel CSMA protocols is studied through considering the hidden terminals. In the modelling phase, important parameters such as shadowing and path loss impairments are being considered. Following that, due to the high importance of spectrum sensing in CSMA protocols, the Double-Threshold Energy Detector (DTED) is thoroughly investigated in this thesis. An iterative algorithm is also proposed to determine optimum values of detection parameters in a sensing-throughput problem formulation. Vehicle-to-Roadside (V2R) communication, as a part of Intelligent Transportation System (ITS), over multi-channel wireless networks is also modelled and analysed in this thesis. In this respect, through proposing a novel mathematical model, the connectivity level which an arbitrary vehicle experiences during its packet transmission with a RSU is also investigated.

The aim of this thesis is to propose methods for obtaining the additional vehicle parameters from the real-world traffic situations images and including existing information of the license plate and localization of the vehicle in the scene. The task is to use an existing installation of the camera systems based on data obtained from these devices to propose a new method of extraction of other vehicle's parameters. Solutions can be divided into two groups: 1. Methods for obtaining the features and methods of data evaluation which will lead to a vehicle's type identification based on a single image of the vehicle. 2. Methods for obtaining data of the vehicle's shape based on image sequence of passing vehicle.

Ces travaux de thèse sont réalisés au sein de l’équipe STI du laboratoire LITIS, en collaboration avec le centre de robotique CAOR de l’école des mines de Paris et l’INRIA Rocquencourt dont ils ont utilisé la plateforme du prototype LARA composée de véhicules instrumentés. L’objectif est de contribuer à la localisation des véhicules intelligents équipés de récepteurs GPS (Global Positionning System), de systèmes de vision et du matériel de communication permettant la coopération entre ces véhicules. En milieu urbain, les performances du GPS sont fortement dégradées. La réception du signal GPS souffre de masquages ou de mauvaises configurations géométriques des satellites. De plus, la qualité du signal peut être corrompue à cause du phénomène de multi-trajets lié à la réflexion du signal sur les bâtiments, tunnels... Alors la robustesse, la précision et la disponibilité de l’estimation de la position peut décroître significativement. D’où la nécessité d’une source d’information complémentaire pour compenser les faiblesses du récepteur GPS. L’originalité de nos travaux consiste à utiliser les données exploitées par notre système de vision. Le système de vision utilisé est basé sur une caméra (monovision). Il permet la détection robuste des obstacles sur la route, ainsi que la détection de la pluie. Le calcul de la distance de l’obstacle à notre véhicule est réalisé à l’aide du modèle sténopé et l’hypothèse de la route plane. Les véhicules utilisant des systèmes de communication sans fil basé sur la norme 802.11g+ coopèrent entre eux en échangeant leurs coordonnées GPS si elles sont disponibles. Cette coopération permet de connaître la position des véhicules qui nous entourent. Le système de communication est aussi utilisé pour l’alerte météorologique V2I ou V2V en utilisant la détection de la pluie réalisée en collaboration avec Valeo. Pour réaliser le positionnement relatif fiable, nous avons mis en oeuvre un algorithme de suivi basé sur le filtrage particulaire. Cette méthode permet de fusionner les données en utilisant les techniques probabilistes lors des différentes étapes du filtre. Finalement, une validation expérimentale en temps réel sur les véhicules du prototype LARA a été réalisée sur différents scénarios. / This thesis work realised at the STI team of the LITIS Laboratory, in collaboration with the Center of Robotics CAOR at the Ecole des Mines of Paris and the INRIA Rocquencourt, and tested on the prototype LARA. The aim is to better positionning of intelligent vehicles equipped with GPS, vision systems and communication devices used for cooperation between vehicles. In urban areas, The usage of GPS is not always ideal because of the poorness of the satellite coverage. Sometimes, the GPS signal may be also corrupted by multipath reflections due to tunnels, high buildings, electronic interferences etc. So, in order to accurate the vehicle positioning in the navigation application, the GPS data will be enhanced with vision data using communication between vehicles. The vision system is based on a monocular real-time vision-based vehicle detection. We can calculate the distance between vehicles using the pinhole model. We developped a rain detection system using the same camera. The inter-vehicle cooperation is made possible thanks to the revolution in the wireless mobile ad hoc network. Localization information can be exchanged between the vehicles through a wireless communication devices. The creation of the system will adopt the Monte Carlo Method or what we call a particle filter for the treatment of the GPS data and vision data. An experimental study of this system is performed on our fleet of experimental communicating vehicles LARA.