ShapeFuture will drive innovation in fundamental Electronic Components and Systems (ECS) that are essential for robust, powerful, fail-operational and integrated perception, cognition, AI-enabled decision making, resilient automation and computing, as well as communications, for highly automated vehicles. The overarching vision of ShapeFuture is to bring ECS Innovation to the heart of Europe’s Mobility Transformation, thereby elevating sovereignty by perfecting programmable ECS solutions for intelligent, safe, connected, and highly automated vehicles. In this paper, we detail not only the vision and mission of the ShapeFuture project, but we also showcase the results achieved during the first year.

Recently, smart system integration was identified as a key competence for optimizing machines and robots. However, when one wants to ’tune’ the entire production process a step further is necessary. We should evaluate performance indicators (e.g. energy and material consumption) over the whole machine life cycle in order to align the production with circular economy principles. To reach that target MBSE (model-based system engineering) should be covered by advanced digital twin approaches which allow continuous monitoring of machine performance, predict the failures and maintenance. Moreover, artificial intelligence and machine learning must be used to process big data sets gathered from the production lines. This paper identifies a common set of technologies and building blocks suitable to solve above mentioned problems for a large variety of industrial domains (semiconductor production, health-care robotics, CNC1machining, high-speed packaging and others). It presents the first results of the large-scale IMOCO4.E2project and shows the pathways for application of the technology on specific machines (so-called pilots). The authors believe the ideas presented could be inspiring also in other domains.

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3.

Deep neural networks (DNNs) have achieved state-of-the-art results in a broad range of tasks, in particular the ones dealing with the perceptual data. However, full-scale application of DNNs in safety-critical areas is hindered by their black box-like nature, which makes their inner workings nontransparent. As a response to the black box problem, the field of explainable artificial intelligence (XAI) has recently emerged and is currently rapidly growing. The present survey is concerned with perturbation-based XAI methods, which allow to explore DNN models by perturbing their input and observing changes in the output. We present an overview of the most recent research focusing on the differences and similarities in the applications of perturbation-based methods to different data types, from extensively studied perturbations of images to the just emerging research on perturbations of video, natural language, software code, and reinforcement learning entities.

Autonomous driving has the potential to disruptively change the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI controlled vehicle demonstrators) achieved until year 2.

Emerging mechatronic applications aim to work at limit performance and reliability while their size and operational space is getting restricted more and more. To reach those targets, often fast integration of customized components is necessary, either electronic systems, SW modules, sensors or actuators. Such diverse set of components needs special tool-chains and methods for fast customization and optimization respecting MBSE (model based system engineering) principles. Large-scale I-MECH project is a natural, fully industry driven initiative trying to follow those demands. The purpose of this paper is to describe its core scientific content, report initial milestones and show a variety of application where I-MECH components, so called building blocks, are being applied.

Train integrity monitoring is one of the key aspects to enable cheaper, safer and more reliable modern railway signaling system. The proposed Train Integrity Monitoring System is based on a WSN, which consist of the WSN Nodes (deployed on each wagon), the Coordinator and the Serial Gateway (both deployed on a locomotive). Each WSN Node measures accelerometer and GPS data, which are further send to the Coordinator, which holds the accelerometer and GPS reference measurements against which the measurements from each node are compared to detect the train integrity. In addition, the Coordinator is also measuring the value of RSSI from all nodes, thereby the decision on whether the train is complete or not is made based on three distinct measurements. If train integrity is lost, an alert message from the Coordinator is send to the Serial Gateway node, which is connected to the PC with a dedicated GUI. The proposed WSN is tested both in a WSN TestBed as well as on a real-life train. Experimental results show that the proposed method is feasible and can be used for train integrity monitoring.