CAPELLO A.

The use of .csv files is very widespread, because of the simplicity of its tabular format and the support by popular editing tools. We propose a novel workflow for enhancing integration of such files with MongoDB storage, and investigate its applicability over a representative sample from the data. world collection. Compared to mongoimport...

Research in the Internet of Things (IoT) have paved the way to a new generation of applications and services that collect huge quantities of data from the field and do a significant part of the processing on the edge. This requires availability of efficient and effective methodologies and tools for a workflow spanning from the edge to the...

Formalization of driving scenarios is key to define the operational design domain (ODD) of Automated Driving Functions (ADF). Training machine learning (ML) requires huge datasets, that are costly to produce. We propose a toolchain to generate driving scenario video-clip datasets based on the state-of-the-art CarLA driving simulator engine....

Explainability is a key requirement for users to effectively understand, trust, and manage artificial intelligence applications, especially those concerning safety. We present the design of a framework aimed at supporting a quantitative explanation of the behavioural planning performed in automated driving (AD) highway simulations by a...

As deep learning models have become increasingly complex, it is critical to understand their decision-making, particularly in safety-relevant applications. In order to support a quantitative interpretation of an autonomous agent trained through Deep Reinforcement Learning (DRL) in the highway-env simulation environment, we propose a framework...

This article explores the development of a Deep Reinforcement Learning (DRL) -based agent able to perform both path planning and trajectory execution, processing sensor perception information and directly controlling the steering wheel and the acceleration, like a normal driver. As a preliminary investigation, we limit our research to low-speed...

Serious Games (SGs) are versatile tools that entertain while addressing serious issues through digital or analog gameplay. However, ensuring continuous supervision during gameplay can be challenging. To overcome this, we propose a flexible scoring system that automates procedure evaluation, empowering learners and promoting independent skill...

Binarization is an extreme quantization technique that is attracting research in the Internet of Things (IoT) field, as it radically reduces the memory footprint of deep neural networks without a correspondingly significant accuracy drop. To support the effective deployment of Binarized Neural Networks (BNNs), we propose CBin-NN, a library of...

This study provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). In Unity, this study established two case studies: garage parking and navigating an obstacle-dense area. Our analysis involves training a path-planning agent with real-time-only...

Availability of realistic driver models, also able to represent various driving styles, is key to add traffic in serious games on automotive driving. We propose a new architecture for behavioural planning of vehicles, that decide their motion taking high-level decisions, such as "keep lane", "overtake" and "go to rightmost lane". This is...

As the quality of perception systems available for automated driving (AD) increases, we investigate the development of an AD agent based on Reinforcement Learning which exploits underlying systems for longitudinal and lateral control. The goal is addressed by designing high-level actions, trying to imitate the commands of a real driver. The...