Infrastructure Wi-Fi for connected autonomous vehicle positioning : a review of the state-of-the-art

In order to realize intelligent vehicular transport networks and self driving cars, connected autonomous vehicles (CAVs) are required to be able to estimate their position to the nearest centimeter. Traditional positioning in CAVs is realized by using a global navigation satellite system (GNSS) such as global positioning system (GPS) or by fusing weighted location parameters from a GNSS with an inertial navigation systems (INSs). In urban environments where Wi-Fi coverage is ubiquitous and GNSS signals experience signal blockage, multipath or non line-of-sight (NLOS) propagation, enterprise or carrier-grade Wi-Fi networks can be opportunistically used for localization or “fused” with GNSS to improve the localization accuracy and precision. While GNSS-free localization systems are in the literature, a survey of vehicle localization from the perspective of a Wi-Fi anchor/infrastructure is limited. Consequently, this review seeks to investigate recent technological advances relating to positioning techniques between an ego vehicle and a vehicular network infrastructure. Also discussed in this paper is an analysis of the location accuracy, complexity and applicability of surveyed literature with respect to intelligent transportation system requirements for CAVs. It is envisaged that hybrid vehicular localization systems will enable pervasive localization services for CAVs as they travel through urban canyons, dense foliage or multi-story car parks

Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking

In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on a vehicle, and the target state should be represented in a global (absolute) coordinate frame. Then it is important to consider the uncertain location of the vehicle on which the sensor is mounted for MTT. In this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states. Measurements collected by different sensors mounted on multiple vehicles with varying location uncertainty are incorporated sequentially based on the arrival of new sensor measurements. In doing so, targets observed from a sensor mounted on a well-localized vehicle reduce the state uncertainty of other poorly localized vehicles, provided that a common non-empty subset of targets is observed. A low complexity filter is obtained by approximations of the joint sensor-feature state density minimizing the Kullback-Leibler divergence (KLD). Results from synthetic as well as experimental measurement data, collected in a vehicle driving scenario, demonstrate the performance benefits of joint vehicle-target state tracking.Comment: 13 pages, 7 figure

Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles

Advancements in LiDAR technology have led to more cost-effective production while simultaneously improving precision and resolution. As a result, LiDAR has become integral to vehicle localization, achieving centimeter-level accuracy through techniques like Normal Distributions Transform (NDT) and other advanced 3D registration algorithms. Nonetheless, these approaches are reliant on high-definition 3D point cloud maps, the creation of which involves significant expenditure. When such maps are unavailable or lack sufficient features for 3D registration algorithms, localization accuracy diminishes, posing a risk to road safety. To address this, we proposed to use LiDAR-equipped roadside unit and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the connected autonomous vehicle's position and help the vehicle when its self-localization is not accurate enough. Our simulation results indicate that this method outperforms traditional NDT scan matching-based approaches in terms of localization accuracy.Comment: Accepted by 2023 International Conference on Intelligent Computing and its Emerging Application

Towards the simulation of cooperative perception applications by leveraging distributed sensing infrastructures

With the rapid development of Automated Vehicles (AV), the boundaries of their function alities are being pushed and new challenges are being imposed. In increasingly complex and dynamic environments, it is fundamental to rely on more powerful onboard sensors and usually AI. However, there are limitations to this approach. As AVs are increasingly being integrated in several industries, expectations regarding their cooperation ability is growing, and vehicle-centric approaches to sensing and reasoning, become hard to integrate. The proposed approach is to extend perception to the environment, i.e. outside of the vehicle, by making it smarter, via the deployment of wireless sensors and actuators. This will vastly improve the perception capabilities in dynamic and unpredictable scenarios and often in a cheaper way, relying mostly in the use of lower cost sensors and embedded devices, which rely on their scale deployment instead of centralized sensing abilities. Consequently, to support the development and deployment of such cooperation actions in a seamless way, we require the usage of co-simulation frameworks, that can encompass multiple perspectives of control and communications for the AVs, the wireless sensors and actuators and other actors in the environment. In this work, we rely on ROS2 and micro-ROS as the underlying technologies for integrating several simulation tools, to construct a framework, capable of supporting the development, test and validation of such smart, cooperative environments. This endeavor was undertaken by building upon an existing simulation framework known as AuNa. We extended its capabilities to facilitate the simulation of cooperative scenarios by incorporat ing external sensors placed within the environment rather than just relying on vehicle-based sensors. Moreover, we devised a cooperative perception approach within this framework, showcasing its substantial potential and effectiveness. This will enable the demonstration of multiple cooperation scenarios and also ease the deployment phase by relying on the same software architecture.Com o rápido desenvolvimento dos Veículos Autónomos (AV), os limites das suas funcional idades estão a ser alcançados e novos desafios estão a surgir. Em ambientes complexos e dinâmicos, é fundamental a utilização de sensores de alta capacidade e, na maioria dos casos, inteligência artificial. Mas existem limitações nesta abordagem. Como os AVs estão a ser integrados em várias indústrias, as expectativas quanto à sua capacidade de cooperação estão a aumentar, e as abordagens de perceção e raciocínio centradas no veículo, tornam-se difíceis de integrar. A abordagem proposta consiste em extender a perceção para o ambiente, isto é, fora do veículo, tornando-a inteligente, através do uso de sensores e atuadores wireless. Isto irá melhorar as capacidades de perceção em cenários dinâmicos e imprevisíveis, reduzindo o custo, pois a abordagem será baseada no uso de sensores low-cost e sistemas embebidos, que dependem da sua implementação em grande escala em vez da capacidade de perceção centralizada. Consequentemente, para apoiar o desenvolvimento e implementação destas ações em cooperação, é necessária a utilização de frameworks de co-simulação, que abranjam múltiplas perspetivas de controlo e comunicação para os AVs, sensores e atuadores wireless, e outros atores no ambiente. Neste trabalho será utilizado ROS2 e micro-ROS como as tecnologias subjacentes para a integração das ferramentas de simulação, de modo a construir uma framework capaz de apoiar o desenvolvimento, teste e validação de ambientes inteligentes e cooperativos. Esta tarefa foi realizada com base numa framework de simulação denominada AuNa. Foram expandidas as suas capacidades para facilitar a simulação de cenários cooperativos através da incorporação de sensores externos colocados no ambiente, em vez de depender apenas de sensores montados nos veículos. Além disso, concebemos uma abordagem de perceção cooperativa usando a framework, demonstrando o seu potencial e eficácia. Isto irá permitir a demonstração de múltiplos cenários de cooperação e também facilitar a fase de implementação, utilizando a mesma arquitetura de software

Infraestrutura de beira de estrada para apoio a sistemas cooperativos e inteligentes de transportes

The growing need of mobility along with the evolution of the automotive industry and the massification of the personal vehicle amplifies some of the road-related problems such as safety and traffic congestion. To mitigate such issues, the evolution towards cooperative communicating technologies and autonomous systems is considered a solution to overcome the human physical limitations and the limited perception horizon of on-board sensors. Short-range vehicular communications such as Vehicle-to-Vehicle or Vehicle-to-Infrastructure (ETSI ITS-G5) in conjunction with long-range cellular communications (LTE,5G) and standardized messages, emerge as viable solutions to amplify the benefits that standalone technologies can bring to the road environment, by covering a wide array of applications and use cases. In compliance with the standardization work from European Telecommunications Standards Institute (ETSI), this dissertation describes the implementation of the collective perception service in a real road infrastructure to assist the maneuvers of autonomous vehicles and provide information to a central road operator. This work is focused on building standardized collective perception messages (CPM) by retrieving information from traffic classification radars (installed in the PASMO project) for local dissemination using ETSI ITS-G5 radio technology and creating a redundant communication channel between the road infrastructure and a central traffic control centre, located at the Instituto de Telecomunicações - Aveiro, taking advantage of cellular, point-to-point radio links and optical fiber communications. The output of the messages are shown to the user by a mobile application. The service is further improved by building an algorithm for optimizing the message dissemination to improve channel efficiency in more demanding scenarios. The results of the experimental tests showed that the time delay between the production event of the collective perception message and the reception by other ITS stations is within the boundaries defined by ETSI standards. Moreover, the algorithm for message dissemination also shows to increase radio channel efficiency by limiting the number of objects disseminated by CPM messages. The collective perception service developed and the road infrastructure are therefore, a valuable asset to provide useful information for improving road safety and fostering the deployment of intelligent cooperative transportation systems.A crescente necessidade de mobilidade em paralelo com a evolução da indústria automóvel e com a massificação do uso de meios de transportes pessoais, têm vindo a amplificar alguns problemas dos transportes rodoviários, tais como a segurança e o congestionamento do tráfego. Para mitigar estas questões, a evolução das tecnologias de comunicação cooperativas e dos sistemas autónomos é vista como uma potencial solução para ultrapassar limitações dos condutores e do horizonte de perceção dos sensores veículares. Comunicações de curto alcance, tais como Veículo-a-Veículo ou Veículo-a-Infrastrutura (ETSI ITS-G5), em conjunto com comunicações móveis de longo alcance (LTE,5G) e mensagens padrão, emergem como soluções viáveis para amplificar todos os beneficios que tecnologias independentes podem trazer para o ambiente rodoviário, cobrindo um grande leque de aplicações e casos de uso da estrada. Em conformidade com o trabalho de padronização da European Telecommunications Standards Institute, esta dissertação descreve a implementação do serviço de perceção coletiva, numa infrastrutura rodoviária real, para suporte a manobras de veículos autónomos e para fornecer informações aos operadores de estradas. Este trabalho foca-se na construção de mensagens de perceção coletiva a partir de informação gerada por radares de classificação de tráfego (instalados no âmbito do projeto PASMO) para disseminação local usando a tecnologia rádio ETSI ITS-G5 e criando um canal de comunicação redundante entre a infraestrutura rodóviaria e um centro de controlo de tráfego localizado no Instituto de Telecomunicações - Aveiro, usando para isso: redes móveis, ligações rádio ponto a ponto e fibra ótica. O conteúdo destas messagens é mostrado ao utilizador através de uma aplicação móvel. O serviço é ainda melhorado, tendo-se para tal desenvolvido um algoritmo de otimização de disseminação das mensagens, tendo em vista melhorar a eficiência do canal de transmissão em cenários mais exigentes. Os resultados dos testes experimentais efetuados revelaram que o tempo de atraso entre o evento de produção de uma mensagem de perceção coletiva e a receção por outra estação ITS, usando comunicações ITS-G5, se encontra dentro dos limites definidos pelos padrões da ETSI. Além disso, o algoritmo para disseminação de mensagens também mostrou aumentar a eficiência do canal de rádio, limitando o número de objetos disseminados pelas mesmas. Assim, o serviço de perceção coletiva desenvolvido poderá ser uma ferramenta valiosa, contribuindo para o aumento da segurança rodóviaria e para a disseminação da utilização dos sistemas cooperativos de transporte inteligente.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Open Platforms for Connected Vehicles

L'abstract è presente nell'allegato / the abstract is in the attachmen

Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines

In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. Therefore, we generalize the concept of ISAC inspired by human synesthesia to establish a unified framework of intelligent multi-modal sensing-communication integration and provide a comprehensive review under such a framework in this paper. The so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such intelligent integration and details its paradigm for the first time. We commence by justifying the necessity of the new paradigm. Subsequently, we offer a definition of SoM and zoom into the detailed paradigm, which is summarized as three operation modes. To facilitate SoM research, we overview the prerequisite of SoM research, i.e., mixed multi-modal (MMM) datasets. Then, we introduce the mapping relationships between multi-modal sensing and communications. Afterward, we cover the technological review on SoM-enhance-based and SoM-concert-based applications. To corroborate the superiority of SoM, we also present simulation results related to dual-function waveform and predictive beamforming design. Finally, we propose some potential directions to inspire future research efforts.Comment: This paper has been accepted by IEEE Communications Surveys & Tutorial

Object-aware multi-criteria decision-making approach using the heuristic data-driven theory for intelligent transportation systems.

Sharing up-to-date information about the surrounding measured by On-Board Units (OBUs) and Roadside Units (RSUs) is crucial in accomplishing traffic efficiency and pedestrians safety towards Intelligent Transportation Systems (ITS). Transferring measured data demands >10Gbit/s transfer rate and >1GHz bandwidth though the data is lost due to unusual data transfer size and impaired line of sight (LOS) propagation. Most existing models concentrated on resource optimization instead of measured data optimization. Subsequently, RSU-LiDARs have become increasingly popular in addressing object detection, mapping and resource optimization issues of Edge-based Software-Defined Vehicular Orchestration (ESDVO). In this regard, we design a two-step data-driven optimization approach called Object-aware Multi-criteria Decision-Making (OMDM) approach. First, the surroundings-measured data by RSUs and OBUs is processed by cropping object-enabled frames using YoLo and FRCNN at RSU. The cropped data likely share over the environment based on the RSU Computation-Communication method. Second, selecting the potential vehicle/device is treated as an NP-hard problem that shares information over the network for effective path trajectory and stores the cosine data at the fog server for end-user accessibility. In addition, we use a nonlinear programming multi-tenancy heuristic method to improve resource utilization rates based on device preference predictions (Like detection accuracy and bounding box tracking) which elaborately concentrate in future work. The simulation results agree with the targeted effectiveness of our approach, i.e., mAP (>71%) with processing delay (< 3.5 x 106bits/slot), and transfer delay (< 3Sms). Our simulation results indicate that our approach is highly effective