自动驾驶学习资料合集
开源项目开源项目也是学习的重要方面1.全栈Apollo - 百度的自动驾驶项目，集成了无人驾驶的各个模块，很值得推荐autoware - 名古屋大学的自动驾驶项目，最早的自动驾驶开源项目之一2. 仿真Udacity- 优达学城的自动驾驶仿真平台Carla- Intel和丰田合作的自动驾驶项目AirSim- 微软的仿真平台，还可以用于无人机lgsvl- LG的自动驾驶仿真平台数据集驾驶数据集KITTI 目前最知名的自动驾驶数据集之一，一些创业公司都会拿里面的数据进行排名比赛。Cityscapes 目标是理解街景的语义，主要是针对城市街景做语义解析。Mapillary 是一个由位于瑞典马尔默的Mapillary AB开发，用来分享含有地理标记照片的服务。其创建者想要利用众包的方式来把整个世界（不仅是街道）以照片的形式存储。comma.ai's Driving Dataset 目的是低成本的自动驾驶方案，目前是通过手机改装来做自动驾驶，开源的数据主要是行车记录仪的数据。Udacity's Driving Dataset 优达学城的自动驾驶数据集，优达学城真的是业界良心，希望国内也多点靠谱的网课。Washington DC's Lidar Data 看起来像是亚马逊的数据？Apolloscape 百度的自动驾驶数据集，有很多复杂场景的道路，同意用数据要同意很长一段声明。BDDV Berkeley的大规模自动驾驶视频数据集。Oxford RobotCar 对牛津的一部分连续的道路进行了上百次数据采集，收集到了多种天气、行人和交通情况下的数据，也有建筑和道路施工时的数据。1000小时以上。nuscenes aptiv提供的数据集，带标注，宣称是目前最大的数据集之一，资源在Amazon S3，目前被墙，后面看是否做个镜像。Waymo open dataset waymo在CVPR2020上提供的自动驾驶数据集，数据量和场景都非常完整。2. 交通标志数据集KUL Belgium Traffic Sign Dataset 比利时的一个交通标志数据集。German Traffic Sign 德国交通标注数据集 。STSD 超过20 000张带有20％标签的图像，包含3488个交通标志。LISA 超过6610帧上的7855条标注。Tsinghua-Tencent 100K 腾讯和清华合作的数据集，100000张图片，包含30000个交通标志实例。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 science2. 自动驾驶综述Self-Driving Cars: A SurveyTowards Fully Autonomous Driving: Systems and AlgorithmsA Survey of Autonomous Driving: Common Practices and Emerging TechnologiesA Survey of Deep Learning Techniques for Autonomous Driving3. 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 applications2.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 driving3.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 Maps4.百度GNSS和点云定位融合方案Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes4. 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-Art2. 物体识别综述Object Detection With Deep Learning: A Review50 Years of object recognition: Directions forwardDeep Learning for Generic Object Detection: A SurveyObject Detection in 20 Years: A Survey - 20193. 道路和车道识别Recent progress in road and lane detection: a survey4. 传感器融合Multisensor data fusion: A review of the state-of-the-artA Review of Data Fusion TechniquesA COMPREHENSIVE REVIEW OF THE MULTI-SENSOR DATA FUSION ARCHITECTURESA Survey of Multisensor Fusion Techniques, Architectures and Methodologies5. 多目标跟踪SIMPLE ONLINE AND REALTIME TRACKINGSIMPLE ONLINE AND REALTIME TRACKING WITH A DEEP ASSOCIATION METRICDeep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A ReviewMultiple Object Tracking: A Literature ReviewDEEP LEARNING IN VIDEO MULTI-OBJECT TRACKING: A SURVEYDeep Learning for Visual Tracking: A Comprehensive SurveyLearning to Divide and Conquer for Online Multi-Target TrackingAn Experimental Survey on Correlation Filter-based Tracking5.预测A Review of Tracking, Prediction and Decision Making Methods for Autonomous DrivingHuman Motion Trajectory Prediction: A SurveyDeep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A ReviewA survey on motion prediction and risk assessment for intelligent vehicles6. 规划控制综述论文A Survey of Motion Planning and ControlTechniques for Self-driving Urban VehiclesA Review of Motion Planning Techniques for Automated Vehicles2. 百度EMplanner论文Baidu Apollo EM Motion Planner7. End-to-End端到端自动驾驶End to End Learning for Self-Driving Cars - 2016 NVIDIA8.V2Xv2x测试综述A Survey of Vehicle to Everything (V2X) Testing9. DARPADARPA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 ChallengeMotion Planning in Urban EnvironmentsJunior: Stanford in The Urban ChallengeOdin: Team VictorTango’s entry in the DUCA Perception-Driven Autonomous Urban VehicleLittle Ben: The Ben Franklin Racing Team’s Entry in the 2007 DARPA Urban ChallengeTeam Cornell’s Skynet: Robust Perception and Planning in anUrban EnvironmentA Practical Approach to Robotic Design for the DARPA Urban ChallengeTeam AnnieWAY’s Autonomous System for the DARPA Urban Challenge 2007Driving with Tentacles: Integral Structures for Sensingand MotionCaroline: An Autonomously Driving Vehicle for Urban EnvironmentsThe MIT–Cornell Collision and Why It HappenedA Perspective on Emerging Automotive Safety Applications,Derived from Lessons Learned through Participation in the DARPA Grand ChallengesTerraMax: Team Oshkosh Urban Robot参考博客1.资料合集apollo代码分析如何开始无人驾驶学习？感知物体识别论文集强化学习资料合集2.高精度地图apollo介绍之map模块（二）高精度地图制作高精度地图制作(二)高精度地图制作（三）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.com2. 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
如果觉得本文对你有帮助，欢迎点赞、分享、关注3连 O(∩_∩)O~~参考^自动驾驶技术栈整理 https://zhuanlan.zhihu.com/p/113319371发布于 2019-03-06