Advanced driving-assistance systems (ADAS) have recently received an increasing attention from the car industry. ADAS development represents a considerable effort to obtain efficient and reliable systems. Driving several million kilometers is necessary to validate a system. Thus, there are strong needs to ease the development and validation processes. In this context, computer simulation techniques are a candidate solution to this problem since running digital scenarios saves time, money and human resources. Up until now, several simulators have been developed. In the first part of this paper we present one of these simulators i.e. Pro-SiVICTM, as a tool that is able to simulate and validate different sensors, control and path planning algorithms for ADAS. Pro-SiVICTM connected with other tools is able to simulate/emulate vehicle components and road environment elements (e.g. perception sensors, wheel-road contacts, chassis characteristics, road side objects including their sensor responses, etc.). However, mainly in ADAS, sound assessment is a difficult task; therefore our paper approaches the related methodology in focusing the effort on defining the assessment criterion. Based on some ADAS developments, assessment examples are given

Despite recent progresses in smart manufacturing, existing architectures often fail to meet the comprehensive demands of modern manufacturing environments, such as flexibility, scalability, and seamless human-machine interaction. The COGNitive Industries for smart MANufacturing (COGNIMAN) architecture addresses these gaps by integrating advanced technologies with a focus on human-centric design. This article introduces COGNIMAN architecture as an essential pillar for Industry 5.0, enhancing operational efficiency through continuous monitoring, real-time data analysis, and predictive insights powered by machine learning. Its modular design optimizes resource utilization, reduces waste, and improves energy efficiency, aligning with eco-conscious manufacturing trends. By integrating sensors, robotics, digital twins, simulation, and AI, COGNIMAN offers a flexible, resilient, and sustainable platform adaptable to various manufacturing processes. The novelty of COGNIMAN architecture lies in its comprehensive approach, addressing key requirements such as flexibility, interoperability, maintainability, and adaptability. It fosters inclusive and safer working environments while supporting sustainable manufacturing practices. A case study in glass fiber manufacturing demonstrates COGNIMAN’s architecture applicability across different fields, enhancing the global competitiveness of European technology and manufacturing sectors.

This work addresses the issue of ocean monitoring and clean-up of polluted zones, as well as the notion of trajectory planning and fault tolerance for semi-autonomous unmanned vehicles. A hybrid approach for unmanned aerial vehicles (UAVs) is introduced to monitor the ocean region and cooperate with swarm of unmanned surface vehicles (USVs) to clean dirty zones. The paper proposes two solutions that apply to trajectory planning from the base of life to the dirty zone for swarm USVs. The first solution is performed by a modified Genetic Algorithm (GA), and the second uses a modified Ant Algorithm (AA). The proposed solutions were both implemented in the simulation with different scenarios for the dirty zone. This approach detects and reduces the pollution level in ocean zones while taking into account the problem of fault tolerance related to unmanned cleaning vehicles.