LAZZARONI L.

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...

Recognition of driving scenarios is getting ever more relevant in research, especially for assessing performance of advanced driving assistance systems (ADAS) and automated driving functions. However, the complexity of traffic situations makes this task challenging. In order to improve the detection rate achieved through state-of-the-art deep...

Voice assistants are spreading in various environments, such as houses and cars, bringing the possibility of controlling heterogeneous Internet of Things devices with simple voice commands. However, massive use of the cloud connection for speech processing requires an efficient and robust Internet connection and raises concerns in terms of...

This research explores an emerging approach, the adversarial policy learning paradigm, that aims to increase safety and robustness in deep reinforcement learning models for automated driving. We propose an iterative procedure to train an adversarial agent acting in a highway-simulated environment to attack a victim agent that is to be improved....

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...

Online deep reinforcement learning training poses challenges due to its length and instability, despite the development of learning algorithms targeted to overcome these issues. Offline learning has emerged as a potential solution, but it reintroduces the issue of dataset production, which is resource-consuming and challenging even in...

Availability of efficient development tools for data‐rich IoT applications is becoming ever more important. Such tools should support cross‐platform deployment and seamless and effective applicability in a variety of domains. In this view, we assessed the versatility of an edge‐to‐cloud system featuring Measurify, a framework for managing smart...

Detection of driving scenarios is getting ever more importance for assessment and control of automated driving functions. This paper investigates the performance of two versions of a high-end 3D convolutional network for scenario classification. The first one uses fully 3D kernels, the second one separates, in each constituting block, the 2D...

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...

The diffusion of Internet of Things (IoT) technologies has paved the way to new applications and services. In this context, developers need tools for efficient design and implementation. This paper proposes Edgine (Edge engine), a cross-platform open-source edge computing system. The system is the edge computing extension of Measurify, a cloud...

This paper proposes the use of a new data toolchain for serious games analytics. The toolchain relies on the open source Measurify Internet of Things (IoT) framework, and particularly takes advantage of its edge computing extension (namely, Edgine), which can be seamlessly deployed cross-platform on embedded devices and PCs as well. The Edgine...

Structural health monitoring (SHM) is key in civil engineering because of the importance and the aging of the infrastructure. We argue that applying leading-edge, data-driven methods of large-scale complex industrial systems may be beneficial, particularly for accuracy and responsiveness. A fundamental step concerns the identification of 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...

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...

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...

The relationship between decision-making and emotions has been extensively studied in both theoretical and empirical research. Game Theory-based paradigms utilizing socio-economic and trust-based contexts have been established to elicit specific emotional responses in autistic individuals. Serious games, incorporating cohesive storylines and...

Measurement-oriented non-relational databases often have a fixed structure schema to better manage and guarantee integrity of their data. However, this leads to a redundancy of field values into the database or does not allow storing most of the existing measurement files. We propose a solution to massively load various format.csv datasets...

There is a growing body of research in the literature that investigates the relationship between emotions and decision-making in socio-economic contexts. Previous research has used Serious Games (SGs) based on game theory paradigms with socio-economic contexts to explore this relationship in controlled settings, but it is unclear whether such...

Driving scenarios detection is an important aspect of the development of automated driving functions (ADF). Given the lack of publicly available datasets with driving scenario labels, we designed a toolchain for generating synthetic video datasets of driving scenarios, based on the OpenSCENARIO format, a well-established, public and...

This article investigates the feasibility of implementing a reinforcement learning agent able to plan the trajectory of a simple automated vehicle 2D model in a motorway simulation. The goal is to use it to implement a non-player vehicle in serious games for driving. The agent extends a Deep Q Learning agent developed by Eduard Leurent in...