利用机器学习估算雪地条件下的车辆侧滑角

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Georg Novotny, Yuzhou Liu, Walter Morales-Alvarez, Wilfried Wöber, Cristina Olaverri-Monreal
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引用次数: 0

摘要

恶劣的天气条件,如积雪覆盖的道路,是自动驾驶汽车研究面临的一项挑战。这尤其具有挑战性,因为它可能导致车辆纵轴与实际行驶方向不一致。在本文中,我们扩展了之前在积雪路面上自动驾驶车辆领域的工作,提出了一种新的侧滑角估计方法,该方法将感知与混合人工神经网络相结合,使预测范围超越了现有方法。我们利用卷积神经网络的特征提取能力和门控递归单元的动态时间序列关系学习能力,并将其与运动模型相结合来估计侧滑角。随后,我们利用 3DCoAutoSim 仿真平台对模型进行了评估,在该平台上,我们设计了一个具有降雪、摩擦力和雪地中汽车行驶轨迹的合适仿真环境。结果表明,在预测范围⩾ 2 秒时,我们的方法优于基准模型。这种扩展的预测范围具有实际意义,可为驾驶员和自动驾驶系统提供更多时间做出明智决策,从而提高道路安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vehicle side-slip angle estimation under snowy conditions using machine learning
Adverse weather conditions, such as snow-covered roads, represent a challenge for autonomous vehicle research. This is particularly challenging as it might cause misalignment between the longitudinal axis of the vehicle and the actual direction of travel. In this paper, we extend previous work in the field of autonomous vehicles on snow-covered roads and present a novel approach for side-slip angle estimation that combines perception with a hybrid artificial neural network pushing the prediction horizon beyond existing approaches. We exploited the feature extraction capabilities of convolutional neural networks and the dynamic time series relationship learning capabilities of gated recurrent units and combined them with a motion model to estimate the side-slip angle. Subsequently, we evaluated the model using the 3DCoAutoSim simulation platform, where we designed a suitable simulation environment with snowfall, friction, and car tracks in snow. The results revealed that our approach outperforms the baseline model for prediction horizons ⩾ 2 seconds. This extended prediction horizon has practical implications, by providing drivers and autonomous systems with more time to make informed decisions, thereby enhancing road safety.
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来源期刊
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering 工程技术-工程:综合
CiteScore
9.90
自引率
21.50%
发文量
21
审稿时长
>12 weeks
期刊介绍: Integrated Computer-Aided Engineering (ICAE) was founded in 1993. "Based on the premise that interdisciplinary thinking and synergistic collaboration of disciplines can solve complex problems, open new frontiers, and lead to true innovations and breakthroughs, the cornerstone of industrial competitiveness and advancement of the society" as noted in the inaugural issue of the journal. The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality. Areas covered by the journal include (but are not limited to) artificial intelligence, advanced signal processing, biologically inspired computing, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, internet-based technologies, knowledge discovery and engineering, machine learning, mechatronics, mobile computing, multimedia technologies, networking, neural network computing, object-oriented systems, optimization and search, parallel processing, robotics virtual reality, and visualization techniques.
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