2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)最新文献_第5页

Decision Making Optimization for Job Offloading in Vehicular Edge Computing Networks 车辆边缘计算网络作业卸载决策优化

2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) Pub Date : 2020-11-18 DOI: 10.23919/AEITAUTOMOTIVE50086.2020.9307383

Christian Grasso, G. Schembra

引用次数: 0

Techniques for improving localization applications running on low-cost IoT devices 改进在低成本物联网设备上运行的本地化应用程序的技术

2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) Pub Date : 2020-11-18 DOI: 10.23919/AEITAUTOMOTIVE50086.2020.9307411

Evelina Forno, Simone Moio, Michael Schenatti, E. Macii, Gianvito Urgese

{"title":"Techniques for improving localization applications running on low-cost IoT devices","authors":"Evelina Forno, Simone Moio, Michael Schenatti, E. Macii, Gianvito Urgese","doi":"10.23919/AEITAUTOMOTIVE50086.2020.9307411","DOIUrl":"https://doi.org/10.23919/AEITAUTOMOTIVE50086.2020.9307411","url":null,"abstract":"Nowadays, localization features are widespread in low-cost and low-power IoT applications such as bike-sharing, off-road vehicle fleet management, and theft prevention of smart devices. For such use cases, since the item to be tracked is inexpensive, older or power-constrained (e.g. battery-powered vehicles), localization features are realized by the installation of low-cost and low-power devices. In this paper, we describe a set of low-computational power techniques, targeting low-cost IoT devices, to process GPS and INS data for accomplishing specific and accurate localization and tracking tasks. The methods here proposed address the calibration of low-cost INS comprised of accelerometer and gyroscope without the aid of external sensors, correction of GPS drift when the target position is static, and the minimization of localization error at device boot. The performances of the proposed methods are then evaluated on several datasets acquired on the field and representing real use-case scenarios.","PeriodicalId":104806,"journal":{"name":"2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133206872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

On the Role of Explainable Machine Learning for Secure Smart Vehicles 可解释机器学习在安全智能车辆中的作用

2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) Pub Date : 2020-11-18 DOI: 10.23919/AEITAUTOMOTIVE50086.2020.9307431

Michele Scalas, G. Giacinto

{"title":"On the Role of Explainable Machine Learning for Secure Smart Vehicles","authors":"Michele Scalas, G. Giacinto","doi":"10.23919/AEITAUTOMOTIVE50086.2020.9307431","DOIUrl":"https://doi.org/10.23919/AEITAUTOMOTIVE50086.2020.9307431","url":null,"abstract":"The concept of mobility is experiencing a serious transformation due to the Mobility-as-a-Service paradigm. Accordingly, vehicles, usually referred to as smart, are seeing their architecture revamped to integrate connection to the outside environment (V2X) and autonomous driving. A significant part of these innovations is enabled by machine learning. However, deploying such systems raises some concerns. First, the complexity of the algorithms often prevents understanding what these models learn, which is relevant in the safety-critical context of mobility. Second, several studies have demonstrated the vulnerability of machine learning-based algorithms to adversarial attacks. For these reasons, research on the explainability of machine learning is raising. In this paper, we then explore the role of interpretable machine learning in the ecosystem of smart vehicles, with the goal of figuring out if and in what terms explanations help to design secure vehicles. We provide an overview of the potential uses of explainable machine learning, along with recent work in the literature that has started to investigate the topic, including from the perspectives of human-agent systems and cyber-physical systems. Our analysis highlights both benefits and criticalities in employing explanations.","PeriodicalId":104806,"journal":{"name":"2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130401027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

User Requirements for Autonomous Vehicles – a Comparative Analysis of Expert and Non-expert-based Approach 自动驾驶汽车的用户需求——基于专家和非专家方法的比较分析

2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) Pub Date : 2020-11-18 DOI: 10.23919/AEITAUTOMOTIVE50086.2020.9307415

Aleksandra Rodak, S. Jamson, M. Kruszewski, Malgorzata Pedzierska

引用次数: 1

A flexible virtual environment for autonomous driving agent-human interaction testing 一种灵活的自动驾驶agent-人机交互测试虚拟环境

2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) Pub Date : 2020-11-18 DOI: 10.23919/AEITAUTOMOTIVE50086.2020.9307438

G. Grasso, Giovanni d’Italia, S. Battiato

{"title":"A flexible virtual environment for autonomous driving agent-human interaction testing","authors":"G. Grasso, Giovanni d’Italia, S. Battiato","doi":"10.23919/AEITAUTOMOTIVE50086.2020.9307438","DOIUrl":"https://doi.org/10.23919/AEITAUTOMOTIVE50086.2020.9307438","url":null,"abstract":"Autonomous driving has, in recent years, gained considerable traction due to commercial interest by car manufacturers, that envision a widespread use of self-driving cars in the next decade. Research has mainly focused on specific topics, that can enable the feasibility of robust artificial autonomous agents for autonomous vehicles (AVs), ranging from artificial vision, to automatic environmental sensing and inference, agent-agent interaction and sensor augmentation and integration. A somewhat reduced effort has been directed towards the evaluation of human-AV interaction and in the direction of defining acceptable rules of engagement when AV deal with human drivers. This aspect of autonomous driving will became extremely relevant in the next years as level 5 AVs will make their appearance on our streets and most of the cars will still be driven by humans. To assess, experimentally, how AVs can successfully interact with human drivers we have constructed a lab setup, which can simulate a large range of situations in which human drivers encounter AVs and interact with them. Based on the Unity Gaming Engine an urban environment has been developed where AVs interact with human driven cars, pedestrians and various agents. This setup allows for human subjects to drive in a virtual reality (VR) environment, and assess their behavior during the interaction with other human drivers and AVs. The goal is to define a set of rules that can be applied in AV design, to make autonomous cars react to human drivers, taking into account the behavioral patterns and variable respect of traffic regulations that human drivers exhibit in real environments. We present an early experimental setup, potentially useful for understanding, in a quantitative and reproducible way, how this approach can contribute in designing AVs that are more suited for large scale deployment.","PeriodicalId":104806,"journal":{"name":"2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130300353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Artificial Intelligence vs Autonomous Cars vs General Data Protection Regulation 人工智能vs自动驾驶汽车vs通用数据保护条例

2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) Pub Date : 2020-11-18 DOI: 10.23919/AEITAUTOMOTIVE50086.2020.9307410

Raffaele Zallone

引用次数: 0

A local trajectory planning and control method for autonomous vehicles based on the RRT algorithm 基于RRT算法的自动驾驶车辆局部轨迹规划与控制方法

2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) Pub Date : 2020-11-18 DOI: 10.23919/AEITAUTOMOTIVE50086.2020.9307439

Stefano Feraco, Sara Luciani, A. Bonfitto, N. Amati, A. Tonoli

引用次数: 20

Trench-Gate MOSFETs in 48V Platform for Mild Hybrid Electric Vehicle Applications 用于轻度混合动力汽车的48V平台沟栅mosfet

2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) Pub Date : 2020-11-18 DOI: 10.23919/AEITAUTOMOTIVE50086.2020.9307436

S. Musumeci, A. Tenconi, M. Pastorelli, F. Scrimizzi, G. Longo, C. Mistretta

引用次数: 0

Four-Wheel Vehicle Driving by using a Spatio-Temporal Characterization of the P300 Brain Potential 四轮车辆驾驶对P300脑电位的时空表征

2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) Pub Date : 2020-11-18 DOI: 10.23919/AEITAUTOMOTIVE50086.2020.9307405

G. Mezzina, D. De Venuto

{"title":"Four-Wheel Vehicle Driving by using a Spatio-Temporal Characterization of the P300 Brain Potential","authors":"G. Mezzina, D. De Venuto","doi":"10.23919/AEITAUTOMOTIVE50086.2020.9307405","DOIUrl":"https://doi.org/10.23919/AEITAUTOMOTIVE50086.2020.9307405","url":null,"abstract":"In this work a P300-based Brain Computer Interface (BCI) for the remote control of a four wheels vehicle, is presented. The proposed BCI exploits the P300 signal, an event-related potential (ERP) typically induced by visual/audio oddball paradigm stimulation protocol. For the driving purpose, in our application a four-choice synchronous BCI has been implemented. The neural interface architecture is made up by (i) the acquisition unit, (ii) the processing unit and (iii) the navigation unit. The former unit collects brain signals by 6 smart wireless electrodes from the parietal-cortex area. The processing unit is composed of a dedicated µPC (Raspberry Pi, RPi) performing stimuli delivery, the machine learning (ML) stage and the real-time classification. Specifically, the processing unit bases its ML stage working on a typical classification problem approach (i.e., feature extraction and classification). In this context, the main contribution of the work lies in the introduction of a P300 spatio-temporal characterization approach (t-RIDE), which allows to analyze all the available choices in a one-vs-all discrimination scenarios. It permits the implementation of very common binary classifiers despite the hyper dimensionality of the classification problem. Finally, the RPi-based navigation unit actuates the received commands and supports the vehicle by using peripheral sensors. As a proof of concept, the BCI operation has been tested on 7 subjects (aged 26 ± 3), using an acrylic prototype car. The experimental results showed that in the online free-drive mode (testing set), the BCI accuracy reached 84.28 ± 0.87% all over 4 choices, on single-trials.","PeriodicalId":104806,"journal":{"name":"2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121866687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Syncing a Smart City within an Evolutionary Dynamical Cooperative Environment 进化动态合作环境下的智慧城市同步

2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) Pub Date : 2020-11-18 DOI: 10.23919/AEITAUTOMOTIVE50086.2020.9307390

Barbara Attanasio, Aurelio La Corte, Marialisa Scatá

{"title":"Syncing a Smart City within an Evolutionary Dynamical Cooperative Environment","authors":"Barbara Attanasio, Aurelio La Corte, Marialisa Scatá","doi":"10.23919/AEITAUTOMOTIVE50086.2020.9307390","DOIUrl":"https://doi.org/10.23919/AEITAUTOMOTIVE50086.2020.9307390","url":null,"abstract":"The cities are evolving in complex systems that compete for space as much as sustainability. In a constantly changing system of networked things and people, the understanding of the way to support the challenges of a smart city is linked to the enabling technologies such as 5G and beyond, automotive Internet of Things (IoT) and Multi Access Edge Computing (MEC). These enable innovative approach to develop sophisticated context and cognitive urban applications, crucial to increase the awareness of the surrounding environment in automotive fields. To this aim, we propose a theoretical approach to investigate a richer structure of the city in terms of depth of knowledge, to unveil hidden urban patterns. This approach is based on two weighted multiplex networks and allows defining and representing cognitive and dynamical interdependence between urban environment and automotive IoT devices (MEC nodes). The evolutionary game theory is applied to study the problem of cooperation of MEC nodes in a multi-service environment, shedding light on the joint impact of its dynamics and the multiplex structure on decreasing the blocking probability of the MEC nodes.","PeriodicalId":104806,"journal":{"name":"2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121401933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1