This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges Index Terms-distributed neural networks, human-centred artificial intelligence, cyber-physical systems, ubiquitous and pervasive computing, edge artificial intelligence
This work is part of the project "A-SWARM" (project number 03SX485D), funded by the German Federal Ministry of Economy and Industry (BMWI) within the funding program Maritime Forschungsstrategie 2025. The company Eesy Innovation collaborated during the experimental and test phase of this project by testing the proposed tool in this paper in different scenarios to check the accuracy of the system in multiple environments.
Robust functionality of autonomous driving vehicles relies on their ability to detect obstables and various scenarios on the road. This can be only achieved by applying robust, fast and efficient AI-based signal processing to radar data. In this work we present an empirical investigation on the question, whether one can apply artificial neural networks (ANNs) directly to frequency modulated continuous wave (FMCW) radar raw data. We show that preproceessing is not necessary if one has enough raw data.In our experiment we have data of 153 648 frames collected with a 60 GHz FMCW radar. We compare systematically the options of preprocessing the data using variational autoencoder, applying traditional preprocessing or omit data-preprocessing and apply ANN directly to raw data. We show that the last option results in 28% faster signal processing and highest accuracy. This is a promising result, since it enables edge computing and direct signal processing at the sensor level.
Autonomous driving is a highly complex task, which involves the use of numerous sensors and various algorithms. Testing of algorithms is difficult and therefore mostly done in simulations. Radar technology will play a key part due to various advantages. In this paper we present a solution to one aspect of autonomous driving, which is the development of a detection algorithm on a moving platform, which is capable of tracking and sending the commands to follow a preceding object, by means of sensor data from a low power 60 GHz Frequency Modulated Continuous Wave (FMCW) radar. The moving platform is based on a miniaturized autonomous vehicle that is used for data gathering as well as algorithm evaluation.To the best of the author's knowledge, this is the first time that processing of radar data via Deep Convolutional Neural Networks (DCNN) for navigation purposes is performed in real time on the edge device operating in a real world environment and not simulative.
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