Abstract Data spaces are collaborative environments where data is exchanged across organizations with agreed-upon rules, and they are emerging as crucial infrastructures for secure and efficient data sharing in the digital economy. This paper provides a comprehensive analysis of various prominent European data space architectures, specifically IDS-RAM, Gaia-X, FIWARE, and IHAN, using a structured methodology to evaluate their characteristics, strengths, and limitations. In addition, recommendations for enhancing security protocols and privacy protection mechanisms are proposed, which are essential for building trust in data sharing environments. Finally, advancements in current frameworks are identified, along with a set of proposed extension requirements designed to enhance the capabilities of data spaces. These suggestions focus on the need for standardized governance, improved interoperability, and mechanisms to ensure data sovereignty and ethical use. By addressing these areas, the paper aims to support the development of more robust data-sharing infrastructures that can adapt to the evolving landscape of data management, with a strong focus on maintaining the highest standards of security and privacy protection.

Reliable and robust navigation of autonomous mobile robots in indoor environments faces significant challenges due to the absence of GPS, visual degradation, repetitive structures, illumination variations, and low texture. These factors adversely affect localization systems. Current robots often use a uniform navigation approach, regardless of the varying localization uncertainties within different indoor environments. In this paper, we propose a holistic, active vision-based path planning method that produces efficient trajectories, aiming to minimize localization error and enhance navigation performance. Specifically, we utilize a 3D model of an indoor environment to derive an Artificial Potential Field (APF) with its associated localizability scores that encapsulate both visual features’ richness and fiducial markers’ placement. APF is employed to direct a Kinematically Constrained Bi-directional Rapidly Exploring Random Tree (KB-RRT) planner towards the calculation of optimal paths, prioritizing high localization areas. Subsequently, we use an online weight-adaptive MPC-based approach that, apart from robust path planning and obstacle avoidance, guides the robot towards areas with the most robust visual features in order to further refine the localization error. The proposed framework has been extensively tested in both simulation and real-world experiments with a mobile robot in a visually challenging indoor environment.

Autonomous vehicle navigation in complex and unpredictable outdoor environments requires extensive and detailed understanding of the surrounding area and compliance with the traffic rules. In this paper, we attempt to imitate human driver behavior towards autonomous navigation that is suitable for diverse, challenging environments, whether urban, semi-structured or rural-like. Our approach starts with a novel method that we propose for extracting free space area using RGB and LiDAR data, in combination with a rough topometric map for route planning. Local goals are extracted in the final drivable region, and the vehicle draws a local path in the free space that is approximately in line with the overall path via a lattice planner. Our method is evaluated both in the publicly available KITTI urban dataset and a custom-made dataset of a semi-structured environment. In both cases, the results highlight the potential of our approach for further advancements in autonomous navigation and the development of safer and more human-like behaviors in driverless vehicles compared to the existing trajectory prediction state-of-the-art methods that make use of a topometric map.

During the last decades, the research endeavours on autonomous driving found great resonance in Advanced Driver-Assistance Solutions that equipped the contemporary civilian vehicles and significantly boosted their driver-less mobility. The existing applications are mostly focused on urban scenarios where signs, road lanes and markers are well defined and ordered favouring the motion of the vehicles whilst, less attention has been paid to the semi-structured and rural environments where traffic infrastructure is scarce. The paper at hand introduces a holistic framework for autonomous vehicles navigation in semi-structured environments. Semantic cues fused with geometrical information of LiDAR data are used for road detection and tracking. OpenStreetMaps are employed as a rough route planner, the waypoints of which are rectified via a probability distribution function over the visible area of vehicle’s vicinity. Thus, vehicle’s localization is obtained by Normal Distribution Transform (NDT) SLAM, where the covariance of egomotion estimation is obtained by processing short-term 3D maps, fused with GPS measurements by means of an extended Kalman filter. Local planning and execution of vehicle’s motion is applied on local cost maps formulated by the union of 2D laser readings and the detected road boundaries fitted through Bézier curves. The complete framework has been evaluated with the aid of a real Autonomous Guided Vehicle in a constrained semi-structured urban area, exhibiting robust navigation performance.