{"title":"1/10th scale autonomous vehicle based on convolutional neural network","authors":"Avishkar Seth, Alice James, S. Mukhopadhyay","doi":"10.21307/ijssis-2020-021","DOIUrl":null,"url":null,"abstract":"Abstract A vehicle capable of using sensors to detect and control its driving actions is called an autonomous vehicle. The development of autonomous vehicles caters to many application areas in the technological advancement of society. This research paper shows a demonstration and implementation of an autonomous vehicle based on a convolutional neural network. The vehicle uses a 1/10th scale RC car as its primary base for the system control with the camera as its primary input. For the computing platform, a Raspberry Pi 4 microprocessor board is used. To enhance the capabilities, the ultrasonic sensor has been implemented in the system as well. The unique aspect of this project is the system design, the CAD modeling, and the track built used to train and test the self-driving capability of the car. The CNN model and the software algorithm also are exclusive to this research project. This research has potential in a variety of application areas in education and also for robotics and autonomous car enthusiasts.","PeriodicalId":45623,"journal":{"name":"International Journal on Smart Sensing and Intelligent Systems","volume":"13 1","pages":"1 - 17"},"PeriodicalIF":0.5000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal on Smart Sensing and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21307/ijssis-2020-021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 6
Abstract
Abstract A vehicle capable of using sensors to detect and control its driving actions is called an autonomous vehicle. The development of autonomous vehicles caters to many application areas in the technological advancement of society. This research paper shows a demonstration and implementation of an autonomous vehicle based on a convolutional neural network. The vehicle uses a 1/10th scale RC car as its primary base for the system control with the camera as its primary input. For the computing platform, a Raspberry Pi 4 microprocessor board is used. To enhance the capabilities, the ultrasonic sensor has been implemented in the system as well. The unique aspect of this project is the system design, the CAD modeling, and the track built used to train and test the self-driving capability of the car. The CNN model and the software algorithm also are exclusive to this research project. This research has potential in a variety of application areas in education and also for robotics and autonomous car enthusiasts.
能够使用传感器检测和控制其驾驶行为的车辆称为自动驾驶车辆。自动驾驶汽车的发展迎合了社会技术进步的诸多应用领域。本文展示了一种基于卷积神经网络的自动驾驶汽车的演示和实现。车辆使用1/10比例的RC汽车作为其主要基础的系统控制与相机作为其主要输入。计算平台采用Raspberry Pi 4微处理器板。为了提高系统的性能,还在系统中加入了超声波传感器。这个项目的独特之处在于系统设计、CAD建模和用于训练和测试汽车自动驾驶能力的轨道。CNN模型和软件算法也是本研究项目所独有的。这项研究在教育、机器人和自动驾驶汽车爱好者的各种应用领域都有潜力。
期刊介绍:
nternational Journal on Smart Sensing and Intelligent Systems (S2IS) is a rapid and high-quality international forum wherein academics, researchers and practitioners may publish their high-quality, original, and state-of-the-art papers describing theoretical aspects, system architectures, analysis and design techniques, and implementation experiences in intelligent sensing technologies. The journal publishes articles reporting substantive results on a wide range of smart sensing approaches applied to variety of domain problems, including but not limited to: Ambient Intelligence and Smart Environment Analysis, Evaluation, and Test of Smart Sensors Intelligent Management of Sensors Fundamentals of Smart Sensing Principles and Mechanisms Materials and its Applications for Smart Sensors Smart Sensing Applications, Hardware, Software, Systems, and Technologies Smart Sensors in Multidisciplinary Domains and Problems Smart Sensors in Science and Engineering Smart Sensors in Social Science and Humanity