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自动驾驶领域入门经典资料推荐

#自动驾驶领域入门经典资料推荐| 来源: 网络整理| 查看: 265

1、经典书籍1.1、高精地图经典书籍

《自主定位与地图构建:SLAM算法原理及应用》(高精地图经典书籍)豆瓣评分:8.1分

《地图融合的理论与实践》豆瓣评分:7.6分

1.2、SLAM经典书籍

《SLAM:地图创建、导航和定位》,豆瓣评分 8.1 分

《SLAM:自动驾驶机器人的综合问题》,豆瓣评分 7.7 分

1.3、决策控制经典书籍

《自动驾驶决策控制:算法、模型和应用》,豆瓣评分 7.8 分

1.4 感知经典书籍

《智能汽车:实时感知与自动驾驶》,豆瓣评分 7.8 分

1.5 综合图书

《自动驾驶技术:从原理到实践》,豆瓣评分 8.2 分

《自动驾驶:技术、系统和应用》,豆瓣评分 8.1 分

2、公开课

英国剑桥大学公开课:《自动驾驶:技术、经济和法律视角》,地址:https://www.futurelearn.com/courses/autonomous-vehicles

加州大学圣地亚哥分校公开课:《自动驾驶》,地址:https://www.edx.org/course/autonomous-driving-uc-san-diegox-sdauto-1

自动驾驶公开课:麻省理工学院公开课,地址:https://ocw.mit.edu/courses/mechanical-engineering/2-159-autonomous-vehicle-engineering-fall-2019/

自动驾驶讲座:清华大学智能车论坛,地址:https://z.tsinghua.edu.cn/

3、论文

《Autonomous Driving: A Survey》,作者:Chang-Lin Chen、Yong-Hua Song、Hua Ma,发布时间:2020年。

《Deep Learning in Autonomous Driving: A Survey》,作者:Xu Tan、Jiacheng Yang、Yong Liu、Ye Yuan,发布时间:2018年。

《Overview of Deep Learning in Autonomous Driving: Challenges and Perspectives》,作者:Shaocheng Liu、Yi Zhao、Ying Tan、Zhen Cao、Yi Fang、Xiangyang Ji、Ming Liu,发布时间:2019年。

《End-to-End Learning for Self-Driving Cars》,作者:Marvin Zhang、Alex Kendall、Vijay Badrinarayanan、Roberto Cipolla,发布时间:2016年

《Learning a Driving Simulator》,作者:Pieter Abbeel、Timothy Hunter、John Schulman、Levi Thatcher,发布时间:2016年。

《Imagined Worlds: Generative Models for Video Prediction and Beyond》,作者:Muneeb Ali、Alexey Dosovitskiy、Vladlen Koltun,发布时间:2017年

《深度学习在自动驾驶中的应用研究综述》,作者:董静刚、饶新宇、李宗鹏,发布时间:2020年。

《自动驾驶领域深度学习方法综述》,作者:任宇、胡跃洪,发布时间:2020年。

《深度学习在自动驾驶中的研究进展》,作者:陈东升、李翔宇、李宗鹏,发布时间:2020年。

4、头部企业研究4.1 特斯拉

“Autopilot: Vision-Based Estimation and Control for Autonomous Vehicle Applications”

“Autopilot: End-to-End Learning for Self-Driving Cars”,“Autopilot: Autonomous Driving Using Real-Time Computer Vision”

4.2 谷歌

“Learning a Driving Simulator”

“End-to-End Learning for Self-Driving Cars”

“Deep Reinforcement Learning for Autonomous Driving”

“Deep Learning for Self-Driving Cars”

4.3 百度

“Deep Learning for Autonomous Driving”

“Graph-Based Deep Reinforcement Learning for Autonomous Driving”

“Multi-Objective Reinforcement Learning for Autonomous Driving”

“Scalable Robotics Simulation Platforms for Autonomous Driving”

4.4 蔚来

“The Design and Evaluation of Autonomous Driving Strategies for Multi-Modal Transportation Networks”,“Deep Learning for Autonomous Vehicle Navigation”

“Improving Autonomous Driving Safety with Comprehensive Awareness System”

“Real-Time Autonomous Driving with Spatiotemporal Attention Network”

4.5 小鹏

“An Adaptive Autonomous Vehicle System with Multi-Sensor Fusion and Parallel Structure”

“A Resilient Autonomous Driving System in Unstructured Environments”

“Robust Autonomous Driving with Multi-Objective Optimization”

“Decentralized Autonomous Vehicle Navigation with Mobile Robots”

“Autonomous Driving with Deep Reinforcement Learning”

4.6 滴滴

“Real-Time End-to-End Autonomous Driving with Recurrent Neural Networks”

“Robust Autonomous Driving with Planning-based Reinforcement Learning”

“Deep Multi-Modal Perception for Autonomous Driving”

“Adaptive Autonomous Vehicle Control through Deep Reinforcement Learning”

“Learning to Drive with Syntax-Guided Imitation Learning”

4.7 阿里巴巴

《End-to-End Autonomous Driving with Deep Reinforcement Learning》

《Multi-Agent Reinforcement Learning for Autonomous Driving》

《High-Level Path Planning with Deep Reinforcement Learning》

《Fast Accurate Autonomous Driving Planning with Deep Reinforcement Learning》

《Autonomous Driving and Decision Making with Deep Reinforcement Learning》

5、 开源系统5.1 apollo

http://apollocar.com/

5.2 autoware

https://www.autoware.org

5.3 carla

http://carla.org/

6、国际顶级会议

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

International Conference on Robotics and Automation (ICRA)

IEEE Intelligent Transportation Systems Conference (ITSC)

International Conference on Automated Planning and Scheduling (ICAPS)

International Conference on Intelligent Transportation Systems (ITSC)



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