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Titlebook: Computational Intelligence in Automotive Applications; Danil Prokhorov Book 20081st edition Springer-Verlag Berlin Heidelberg 2008 Automot

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發(fā)表于 2025-3-26 22:39:42 | 只看該作者
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發(fā)表于 2025-3-27 01:39:27 | 只看該作者
Understanding Driving Activity Using Ensemble Methods,iver assistance systems, specifically, Driver Workload Management systems. Such systems integrate, prioritize, and manage information from the roadway, vehicle, cockpit, driver, infotainment devices, and then deliver it through a multimodal user interface. This could include incoming cell phone call
33#
發(fā)表于 2025-3-27 06:31:30 | 只看該作者
Computer Vision and Machine Learning for Enhancing Pedestrian Safety,o minimize accidents and save many lives. Such a system would detect the pedestrian, predict the possibility of collision, and then warn the driver or engage automatic braking or other safety devices. This chapter describes the framework and issues involved in developing a pedestrian protection syst
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發(fā)表于 2025-3-27 09:28:14 | 只看該作者
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發(fā)表于 2025-3-27 16:08:57 | 只看該作者
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發(fā)表于 2025-3-27 19:48:02 | 只看該作者
Neural Networks in Automotive Applications,e main technology. Automotive engineers and researchers are certainly familiar with the buzzword, and some have even tried neural networks for their specific applications as models, virtual sensors, or controllers (see, e.g., [1] for a collection of relevant papers). In fact, a quick search reveals
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發(fā)表于 2025-3-28 01:07:42 | 只看該作者
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發(fā)表于 2025-3-28 05:25:55 | 只看該作者
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發(fā)表于 2025-3-28 11:51:45 | 只看該作者
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