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教学视频
综合
2. 定位
3. SLAM
书籍
1.综合
2. Planning
3.高精度地图和定位
《视觉SLAM十四讲》高精度地图和定位需要的基础知识
4. 车联网
车联网白皮书(C-V2X白皮书)
车联网白皮书(2018年)
车联网网络安全白皮书(2017年)
车联网白皮书(2017年)
C-V2X业务需求演进
车辆高精度定位白皮书
C-V2X产业化路径和时间表研究白皮书
LTE-V2X安全技术白皮书
MEC与C-V2X融合 应用场景
C-V2X白皮书
主要介绍了V2X的架构演进和一些产业研究,对CV2X的技术方案了解非常有帮忙。
资料下载见文末
自动驾驶技术栈[1]
这里主要对自动驾驶技术做了硬件和软件2个大类的划分,图片如果不清晰可以查看思维导图原图链接
开源项目
开源项目也是学习的重要方面
1.全栈
2. 仿真
数据集
驾驶数据集
2. 交通标志数据集
论文
论文下载
文末有论文打包下载地址!!!请点击文末链接下载。同时论文下载强烈推荐,感谢这个网站的作者。
removing barriers in the way of science
2. 自动驾驶综述
Self-Driving Cars: A Survey
Towards Fully Autonomous Driving: Systems and Algorithms
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
A Survey of Deep Learning Techniques for Autonomous Driving
3. 定位
下面总结了目前主流的定位方法,以及其优缺点,参考"A Survey of Autonomous Driving: Common Practices and Emerging Technologies"需要的自取
1.state-of-art定位综述
A survey of the state-of-the-art localization techniques and their potentials for autonomous vehicle applications
2.SLAM方法在自动驾驶领域应用综述
Simultaneous localization and mapping: A survey of current trends in autonomous driving
3.斯坦福DARPA比赛开山之作,主要是关于SLAM方法
Map-Based Precision Vehicle Localization in Urban Environments Robust Vehicle Localization in Urban Environments Using Probabilistic Maps
4.百度GNSS和点云定位融合方案
Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes
4. 感知
计算机视觉在自动驾驶应用综述
Computer Vision for Autonomous Vehicles:Problems, Datasets and State-of-the-Art
2. 物体识别综述
Object Detection With Deep Learning: A Review
50 Years of object recognition: Directions forward
Deep Learning for Generic Object Detection: A Survey
Object Detection in 20 Years: A Survey - 2019
3. 道路和车道识别
Recent progress in road and lane detection: a survey
4. 传感器融合
Multisensor data fusion: A review of the state-of-the-art
A Review of Data Fusion Techniques
A COMPREHENSIVE REVIEW OF THE MULTI-SENSOR DATA FUSION ARCHITECTURES
A Survey of Multisensor Fusion Techniques, Architectures and Methodologies
5. 多目标跟踪
SIMPLE ONLINE AND REALTIME TRACKING
SIMPLE ONLINE AND REALTIME TRACKING WITH A DEEP ASSOCIATION METRIC
Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review
Multiple Object Tracking: A Literature Review
DEEP LEARNING IN VIDEO MULTI-OBJECT TRACKING: A SURVEY
Deep Learning for Visual Tracking: A Comprehensive Survey
Learning to Divide and Conquer for Online Multi-Target Tracking
An Experimental Survey on Correlation Filter-based Tracking
5.预测
A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving
Human Motion Trajectory Prediction: A Survey
Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review
A survey on motion prediction and risk assessment for intelligent vehicles
6. 规划控制
综述论文
2. 百度EMplanner论文
Baidu Apollo EM Motion Planner
7. End-to-End
端到端自动驾驶
End to End Learning for Self-Driving Cars - 2016 NVIDIA
8.V2X
v2x测试综述
A Survey of Vehicle to Everything (V2X) Testing
9. DARPA
DARPA城市挑战赛是无人驾驶技术的鼻祖,下面是参赛的队伍发表的论文集
Autonomous Driving in Urban Environments:Boss and the Urban Challenge
Motion Planning in Urban Environments
Junior: Stanford in The Urban Challenge
Odin: Team VictorTango’s entry in the DUC
A Perception-Driven Autonomous Urban Vehicle
Little Ben: The Ben Franklin Racing Team’s Entry in the 2007 DARPA Urban Challenge
Team Cornell’s Skynet: Robust Perception and Planning in anUrban Environment
A Practical Approach to Robotic Design for the DARPA Urban Challenge
Team AnnieWAY’s Autonomous System for the DARPA Urban Challenge 2007
Driving with Tentacles: Integral Structures for Sensingand Motion
Caroline: An Autonomously Driving Vehicle for Urban Environments
The MIT–Cornell Collision and Why It Happened
A Perspective on Emerging Automotive Safety Applications,Derived from Lessons Learned through Participation in the DARPA Grand Challenges
TerraMax: Team Oshkosh Urban Robot
参考
博客
1.资料合集
2.高精度地图
资料分享
1. 论文下载地址
论文分享在百度网盘,有需要的同学可以下载学习,提取码:gbd1
文件分享pan.baidu.com
2. 车联网白皮书
中国信通院白皮书打包下载
中国信通院-研究成果-权威发布-白皮书www.caict.ac.cn
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参考
^自动驾驶技术栈整理 https://zhuanlan.zhihu.com/p/113319371