Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles

The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles. This paper proposes a federated learning (FL) based dynamic map fusion framework to achieve high map quality despite unknown numbers of objects in fields of view (FoVs), various sensing and model uncertainties, and missing data labels for online learning. The novelty of this work is threefold: (1) developing a three-stage fusion scheme to predict the number of objects effectively and to fuse multiple local maps with fidelity scores; (2) developing an FL algorithm which fine-tunes feature models (i.e., representation learning networks for feature extraction) distributively by aggregating model parameters; (3) developing a knowledge distillation method to generate FL training labels when data labels are unavailable. The proposed framework is implemented in the Car Learning to Act (CARLA) simulation platform. Extensive experimental results are provided to verify the superior performance and robustness of the developed map fusion and FL schemes.Comment: 12 pages, 5 figures, to appear in 2021 IEEE International Conference on Robotics and Automation (ICRA

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception

Recent advancements in Vehicle-to-Everything communication technology have enabled autonomous vehicles to share sensory information to obtain better perception performance. With the rapid growth of autonomous vehicles and intelligent infrastructure, the V2X perception systems will soon be deployed at scale, which raises a safety-critical question: \textit{how can we evaluate and improve its performance under challenging traffic scenarios before the real-world deployment?} Collecting diverse large-scale real-world test scenes seems to be the most straightforward solution, but it is expensive and time-consuming, and the collections can only cover limited scenarios. To this end, we propose the first open adversarial scene generator V2XP-ASG that can produce realistic, challenging scenes for modern LiDAR-based multi-agent perception systems. V2XP-ASG learns to construct an adversarial collaboration graph and simultaneously perturb multiple agents' poses in an adversarial and plausible manner. The experiments demonstrate that V2XP-ASG can effectively identify challenging scenes for a large range of V2X perception systems. Meanwhile, by training on the limited number of generated challenging scenes, the accuracy of V2X perception systems can be further improved by 12.3\% on challenging and 4\% on normal scenes. Our code will be released at https://github.com/XHwind/V2XP-ASG.Comment: ICRA 2023, see https://github.com/XHwind/V2XP-AS

Shared Situational Awareness with V2X Communication and Set-membership Estimation

The ability to perceive and comprehend a traffic situation and to estimate the state of the vehicles and road-users in the surrounding of the ego-vehicle is known as situational awareness. Situational awareness for a heavy-duty autonomous vehicle is a critical part of the automation platform and depends on the ego-vehicle's field-of-view. But when it comes to the urban scenario, the field-of-view of the ego-vehicle is likely to be affected by occlusion and blind spots caused by infrastructure, moving vehicles, and parked vehicles. This paper proposes a framework to improve situational awareness using set-membership estimation and Vehicle-to-Everything (V2X) communication. This framework provides safety guarantees and can adapt to dynamically changing scenarios, and is integrated into an existing complex autonomous platform. A detailed description of the framework implementation and real-time results are illustrated in this paper

Open Platforms for Connected Vehicles

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

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

ADOPT: A system for Alerting Drivers to Occluded Pedestrian Traffic

Recent statistics reveal an alarming increase in accidents involving pedestrians (especially children) crossing the street. A common philosophy of existing pedestrian detection approaches is that this task should be undertaken by the moving cars themselves. In sharp departure from this philosophy, we propose to enlist the help of cars parked along the sidewalk to detect and protect crossing pedestrians. In support of this goal, we propose ADOPT: a system for Alerting Drivers to Occluded Pedestrian Traffic. ADOPT lays the theoretical foundations of a system that uses parked cars to: (1) detect the presence of a group of crossing pedestrians - a crossing cohort; (2) predict the time the last member of the cohort takes to clear the street; (3) send alert messages to those approaching cars that may reach the crossing area while pedestrians are still in the street; and, (4) show how approaching cars can adjust their speed, given several simultaneous crossing locations. Importantly, in ADOPT all communications occur over very short distances and at very low power. Our extensive simulations using SUMO-generated pedestrian and car traffic have shown the effectiveness of ADOPT in detecting and protecting crossing pedestrians