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无人车综述:Abstract— We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving c

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2019-03-31 上传 大小:1.24MB
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Self-Driving Cars A Survey(自动驾驶综述)

近几年来,自动驾驶发展非常迅猛,和自动驾驶相关的技术也在不断更新演变,其技术主要分为感知层、信息融合层、决策规划层、以及控制层。未来5到10年,全球互联网产业还有唯一一座未开掘的金矿,毫无疑问是自动驾驶。有预测表明,在2025年与自动驾驶相关的将产生1.9万亿美元的产值。这个庞大的产业与其他行业一样,会经历百舸争流到少数几家竞争的格局。近日,在Arxiv上的一篇最新关于自动驾驶最新综述论文,详细描述了自动驾驶感知和决策层面最新的技术方法,是不可多得参阅材料。

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自动驾驶汽车之深度学习PPT 2018 MIT 6.S094 Deep Learning for Self-Driving Cars

自动驾驶汽车之深度学习 2018 MIT 6.S094 Deep Learning for Self-Driving Cars 自动驾驶汽车之深度学习 2018 MIT 6.S094 Deep Learning for Self-Driving Cars 自动驾驶汽车之深度学习 2018 MIT 6.S094 Deep Learning for Self-Driving Cars

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self-driving cars : A survey

无人车综述:Abstract— We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving c

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Deep Learning for Self-Driving Cars

MIT,Deep Learning for Self-Driving Cars.Lecture 1 : Introduction to Deep Learning for Self-Driving Cars

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On a Formal Model of Safe and Scalable Self-driving Cars (Mobileye 安全定义)

想靠数学模型解决自动驾驶时代的事故问题,Mobileye 的工程师思维引领行业方向。 Mobileye 的逻辑其实很简单,它认为如果我们能用形式化模型来判断事故责任,那么工程师开发起驾驶策略算法就更加轻松,而这些算法是避免自动驾驶汽车造成事故的核心(被动被非自动驾驶汽车卷入事故不可避免)。此外,这样的模型还能让工程师制定出“有效的验证过程”,无需靠麻烦的路测和模拟。

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Deep Learning Localization for Self-driving Cars.pdf

Deep Learning Localization for Self-driving Cars. Rochester Institute of Technology.

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End to End Learning for Self-Driving Cars

无人驾驶技术,

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Waymo详细介绍-Inside Waymo's Secret World for Training Self-Driving Cars

Waymo是一家研发自动驾驶汽车的公司,为Alphabet公司(Google母公司)旗下的子公司。Waymo刚开始是Google于2009年开启的一项自动驾驶汽车计划,之后于2016年12月才由Google独立出来,成为Alphabet公司旗下的子公司。2018年7月,Waymo宣布其自动驾驶车队在公共道路上的路测里程已达800万英里。此篇是Waymo邀请记者参观后的新闻。

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自动驾驶汽车:下一代革命

self-driving cars: The next revolution! 全英文版

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Blockchain-for-the-Transportation-Industry-Article-0318.pdf

ew blockchain-based business models are enabled. The system could create an extremely fast and secure network for electric car charging station availability and payment. Further into the future, it may also prove essential to self-driving cars. Information from a self-driving Porsche on road conditi

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Practical Computer Vision: Extract insightful information from images using TF,K

Computer vision is one of the most widely studied sub-fields of computer science. It has several important applications, such as face detection, image searching, and artistic image conversion. With the popularity of deep learning methods, many recent applications of computer vision are in self-drivi

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Artificial Intelligence Safety and Security

About 10,000 scientists* around the world work on different aspects of creating intelligent machines, with the main goal of making such machines as capable as possible. With amazing progress made in the field of AI over the last decade, it is more important than ever to make sure that the technology

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Genetic Algorithms and Machine Learning for Programmers

Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions (Pragmatic Programmers) by Frances Buontempo English, 2019,243 Pages Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you

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The Future of Machine Intelligence: Perspectives from Leading Practitioners

Title The Future of Machine Intelligence: Perspectives from Leading Practitioners Author(s) David Beyer Publisher: O’Reilly Media Inc. (February 29, 2016) Book Description Advances in both theory and practice are throwing the promise of machine learning into sharp relief. The field has the potenti

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