A Systematic Mapping of Autonomous Vehicle Prototypes: Trends and Opportunities

IF 14 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-11 DOI:10.1109/TIV.2024.3387394

Ericson Silva;Felipe Soares;Wenderson Júnio de Souza;Henrique Freitas

{"title":"A Systematic Mapping of Autonomous Vehicle Prototypes: Trends and Opportunities","authors":"Ericson Silva;Felipe Soares;Wenderson Júnio de Souza;Henrique Freitas","doi":"10.1109/TIV.2024.3387394","DOIUrl":null,"url":null,"abstract":"Autonomous driving vehicles (ADVs) are the next significant evolution in transportation systems. Using machine learning techniques supported by strong processing units and sensors, the driver would be removed and the vehicle would drive itself, bringing countless advantages to the commuting system. There are too many players developing their own ADV prototypes all over the world. For this reason, this paper aims to use a systematic mapping study (SMS) to identify trends and patterns in ADV development. From a set of academic databases, a total of 68 papers were selected from a total of 548 after applying inclusion and exclusion criteria and snowballing techniques. The results showed the technical characteristics of the platform required to develop real-size and mini ADV prototypes. In addition, the paper explains the detailed software and hardware architectures and their main aspects and requirements. Finally, the paper discusses the necessary set of sensors which are required and their characteristics and main suppliers. It was possible to map the publications distribution based on venues, year, authors' affiliation, and most prolific countries. This work can guide researchers to design their ADV prototype or identify development bottlenecks that could lead to narrow research and innovation possibilities in autonomous driving vehicle development.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 11","pages":"6777-6802"},"PeriodicalIF":14.0000,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Intelligent Vehicles","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10496830/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

Autonomous driving vehicles (ADVs) are the next significant evolution in transportation systems. Using machine learning techniques supported by strong processing units and sensors, the driver would be removed and the vehicle would drive itself, bringing countless advantages to the commuting system. There are too many players developing their own ADV prototypes all over the world. For this reason, this paper aims to use a systematic mapping study (SMS) to identify trends and patterns in ADV development. From a set of academic databases, a total of 68 papers were selected from a total of 548 after applying inclusion and exclusion criteria and snowballing techniques. The results showed the technical characteristics of the platform required to develop real-size and mini ADV prototypes. In addition, the paper explains the detailed software and hardware architectures and their main aspects and requirements. Finally, the paper discusses the necessary set of sensors which are required and their characteristics and main suppliers. It was possible to map the publications distribution based on venues, year, authors' affiliation, and most prolific countries. This work can guide researchers to design their ADV prototype or identify development bottlenecks that could lead to narrow research and innovation possibilities in autonomous driving vehicle development.

查看原文本刊更多论文

自动驾驶汽车原型的系统映射：趋势和机遇

自动驾驶汽车（ADVs）是交通运输系统的下一个重大发展。使用由强大的处理单元和传感器支持的机器学习技术，驾驶员将被移除，车辆将自动驾驶，为通勤系统带来无数优势。世界上有太多玩家在开发自己的ADV原型。因此，本文旨在使用系统映射研究（SMS）来确定ADV发展的趋势和模式。从一套学术数据库中，运用纳入、排除标准和滚雪球技术，从548篇论文中筛选出68篇。结果显示了开发真实尺寸和迷你ADV原型所需平台的技术特征。此外，本文还详细阐述了系统的软硬件体系结构及其主要方面和要求。最后，讨论了所需要的一组传感器及其特点和主要供应商。可以根据地点、年份、作者所属单位和最多产的国家绘制出版物分布地图。这项工作可以指导研究人员设计他们的ADV原型，或者确定可能导致自动驾驶汽车开发中研究和创新可能性缩小的开发瓶颈。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Intelligent Vehicles Mathematics-Control and Optimization

CiteScore

12.10

自引率

13.40%

发文量

177

期刊介绍： The IEEE Transactions on Intelligent Vehicles (T-IV) is a premier platform for publishing peer-reviewed articles that present innovative research concepts, application results, significant theoretical findings, and application case studies in the field of intelligent vehicles. With a particular emphasis on automated vehicles within roadway environments, T-IV aims to raise awareness of pressing research and application challenges. Our focus is on providing critical information to the intelligent vehicle community, serving as a dissemination vehicle for IEEE ITS Society members and others interested in learning about the state-of-the-art developments and progress in research and applications related to intelligent vehicles. Join us in advancing knowledge and innovation in this dynamic field.