Off-road terrain classification

IF 2.4 3区 工程技术 Q3 ENGINEERING, ENVIRONMENTAL Journal of Terramechanics Pub Date : 2023-01-01 DOI:10.1016/j.jterra.2022.11.003
Lafras Fritz, Herman A. Hamersma, Theunis R. Botha
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越野地形分类
道路交通事故给全球经济造成了负担。这种影响可以通过开发更安全的车辆来减少。高级驾驶辅助系统(ADAS)旨在减少事故发生的频率和严重程度。adas的设计目的是在定义明确的环境中运行,例如第一世界的城市地区。然而,93%的致命事故发生在发展中国家;通常没有适当维护道路的地区。在这些具有挑战性的环境中,ADAS经常无法按预期运行。地形分类可以提高ADAS的性能。对道路地形分类的研究较多,但对非道路地形分类的研究较少。本研究使用卷积神经网络(CNN),基于ISO8608:2016标准的道路粗糙度,对几种越野地形进行了分类。使用前向和下向摄像头创建了不同地形上已知道路粗糙度的图像数据库。建立了两种不同的分类模型:一种是全新的分类模型,另一种是在预训练模型上使用迁移学习。地形数据是在几个公路和越野轨道上捕获的。结果表明,在车辆行驶到特定路段之前,利用相机进行越野地形分类可以达到较高的精度。
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来源期刊
Journal of Terramechanics
Journal of Terramechanics 工程技术-工程:环境
CiteScore
5.90
自引率
8.30%
发文量
33
审稿时长
15.3 weeks
期刊介绍: The Journal of Terramechanics is primarily devoted to scientific articles concerned with research, design, and equipment utilization in the field of terramechanics. The Journal of Terramechanics is the leading international journal serving the multidisciplinary global off-road vehicle and soil working machinery industries, and related user community, governmental agencies and universities. The Journal of Terramechanics provides a forum for those involved in research, development, design, innovation, testing, application and utilization of off-road vehicles and soil working machinery, and their sub-systems and components. The Journal presents a cross-section of technical papers, reviews, comments and discussions, and serves as a medium for recording recent progress in the field.
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