通过持续的环境学习自适应道路检测

Mike Foedisch, A. Takeuchi
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引用次数: 31

摘要

美国国家标准与技术研究院智能系统部多年来一直致力于开发自动驾驶实时系统。道路检测程序是该项目的重要组成部分。在此之前,我们利用神经网络开发了一种基于颜色直方图的自适应道路检测系统。然而,这在初始化步骤中仍然需要人工参与。作为项目的延续,我们扩展了系统,使其能够在没有任何人为干预的情况下适应新的环境。该系统基于道路图像结构不断更新神经网络。为了减少道路与非道路的误分类可能性,我们实现了一种自适应道路特征获取方法。
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Adaptive road detection through continuous environment learning
The Intelligent Systems Division of the National Institute of Standards and Technology has been engaged for several years in developing real-time systems for autonomous driving. A road detection program is an essential part of the project. Previously we developed an adaptive road detection system based on color histograms using a neural network. This, however, still required human involvement during the initialization step. As a continuation of the project, we have expanded the system so that it can adapt to the new environment without any human intervention. This system updates the neural network continuously based on the road image structure. In order to reduce the possibility of misclassifying road and non-road, we have implemented an adaptive road feature acquisition method.
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