Key technologies for safe and autonomous drones

Drones/UAVs are able to perform air operations that are very difficult to be performed by manned aircrafts. In addition, drones' usage brings significant economic savings and environmental benefits, while reducing risks to human life. In this paper, we present key technologies that enable development of drone systems. The technologies are identified based on the usages of drones (driven by COMP4DRONES project use cases). These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. Also, we present the contributions of the COMP4DRONES project to improve existing technologies. These contributions aim to ease drones’ customization, and enable their safe operation.This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826610. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Austria, Belgium, Czech Republic, France, Italy, Latvia, Netherlands. The total project budget is 28,590,748.75 EUR (excluding ESIF partners), while the requested grant is 7,983,731.61 EUR to ECSEL JU, and 8,874,523.84 EUR of National and ESIF Funding. The project has been started on 1st October 2019

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Envisioning a Safety Island to Enable HPC Devices in Safety-Critical Domains

HPC (High Performance Computing) devices increasingly become the only alternative to deliver the performance needed in safety-critical autonomous systems (e.g., autonomous cars, unmanned planes) due to deploying large and powerful multicores along with accelerators such as GPUs. However, the support that those HPC devices offer to realize safety-critical systems on top is heterogeneous. Safety islands have been devised to be coupled to HPC devices and complement them to meet the safety requirements of an increased set of applications, yet the variety of concepts and realizations is large. This paper presents our own concept of a safety island with two goals in mind: (1) offering a wide set of features to enable the broadest set of safety applications for each HPC device, and (2) being realized with open source components based on RISC-V ISA to ease its use and adoption. In particular, we present our safety island concept, the key features we foresee it should include, and its potential application beyond safety.Comment: White pape

SAFEXPLAIN: Safe and Explainable Critical Embedded Systems Based on AI

Deep Learning (DL) techniques are at the heart of most future advanced software functions in Critical Autonomous AI-based Systems (CAIS), where they also represent a major competitive factor. Hence, the economic success of CAIS industries (e.g., automotive, space, railway) depends on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost. However, there is a fundamental gap between Functional Safety (FUSA) requirements on CAIS and the nature of DL solutions. This gap stems from the development process of DL libraries and affects high-level safety concepts such as (1) explainability and traceability, (2) suitability for varying safety requirements, (3) FUSA-compliant implementations, and (4) real-time constraints. As a matter of fact, the data-dependent and stochastic nature of DL algorithms clashes with current FUSA practice, which instead builds on deterministic, verifiable, and pass/fail test-based software. The SAFEXPLAIN project tackles these challenges and targets by providing a flexible approach to allow the certification - hence adoption - of DL-based solutions in CAIS building on: (1) DL solutions that provide end-to-end traceability, with specific approaches to explain whether predictions can be trusted and strategies to reach (and prove) correct operation, in accordance to certification standards; (2) alternative and increasingly sophisticated design safety patterns for DL with varying criticality and fault tolerance requirements; (3) DL library implementations that adhere to safety requirements; and (4) computing platform configurations, to regain determinism, and probabilistic timing analyses, to handle the remaining non-determinism.The research leading to these results has received funding from the Horizon Europe Programme under the SAFEXPLAIN Project (www.safexplain.eu), grant agreement num. 101069595. BSC authors have also been supported by the Spanish Ministry of Science and Innovation under grant PID2019- 107255GBC21/AEI/10.13039/501100011033.Peer Reviewed"Article signat per 22 autors/es: Jaume Abella, Jon Perez, Cristofer Englund, Bahram Zonooz, Gabriele Giordana, Carlo Donzella, Francisco J. Cazorla, Enrico Mezzetti, Isabel Serra, Axel Brando, Irune Agirre, Fernando Eizaguirre, Thanh Hai Bui, Elahe Arani, Fahad Sarfraz, Ajay Balasubramaniam, Ahmed BadarIlaria Bloise, Lorenzo Feruglio, Ilaria Cinelli, Davide Brighenti, Davide Cunial"Postprint (author's final draft

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

Sem resumo disponível.publishe

Design and management of image processing pipelines within CPS : Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints.Peer reviewe

Smart Manufacturing

This book is a collection of 11 articles that are published in the corresponding Machines Special Issue “Smart Manufacturing”. It represents the quality, breadth and depth of the most updated study in smart manufacturing (SM); in particular, digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the Internet to expand system capabilities, (3) supporting data-driven decision-making activities at various domains and levels of businesses, or (4) reconfiguring systems to adapt to changes and uncertainties. System smartness can be evaluated by one or a combination of performance metrics such as degree of automation, cost-effectiveness, leanness, robustness, flexibility, adaptability, sustainability, and resilience. This book features, firstly, the concepts digital triad (DT-II) and Internet of digital triad things (IoDTT), proposed to deal with the complexity, dynamics, and scalability of complex systems simultaneously. This book also features a comprehensive survey of the applications of digital technologies in space instruments; a systematic literature search method is used to investigate the impact of product design and innovation on the development of space instruments. In addition, the survey provides important information and critical considerations for using cutting edge digital technologies in designing and manufacturing space instruments