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雖然已經(jīng)從事自動駕駛領域多年，但一直忙于項目調(diào)研與落地,，近期才有時間將自動駕駛領域方向的一些綜述,、經(jīng)典論文進行匯總，供大家學習,。主要涉及目標檢測、語義分割,、全景/實例分割,、目標跟蹤、Transformer,、關鍵點檢測,、深度估計、3D檢測,、多模態(tài)融合,、車道線檢測、多傳感器數(shù)據(jù)融合,、SLAM與高精地圖等方向,； 1目標檢測綜述主要涉及通用目標檢測任務、檢測任務中的數(shù)據(jù)不均衡問題,、偽裝目標檢測,、自動駕駛領域檢測任務,、anchor-based、anchor-free,、one-stage,、two-stage方案等； 1.A Survey of Deep Learning for Low-Shot Object Detection 2.A Survey of Deep Learning-based Object Detection 3.Camouflaged Object Detection and Tracking：A Survey 4.Deep Learning for Generic Object Detection：A Survey 5.Imbalance Problems in Object Detection：A survey 6.Object Detection in 20 Years：A Survey 7.Object Detection in Autonomous Vehicles：Status and Open Challenges 8.Recent Advances in Deep Learning for Object Detection 2目標檢測數(shù)據(jù)增強與不均衡問題主要涉及目標檢測任務中的數(shù)據(jù)增強,、小目標檢測,、小樣本學習、autoargument等工作,； 1.A survey on Image Data Augmentation for Deep Learning 2.Augmentation for small object detection 3.Bag of Freebies for Training Object Detection Neural Networks 4.Generalizing from a Few Examples：A Survey on Few-Shot 5.Learning Data Augmentation Strategies for Object Detection 3分割綜述主要對實時圖像分割,、視頻分割、實例分割,、弱監(jiān)督/無監(jiān)督分割,、點云分割等方案展開討論； 1.A Review of Point Cloud Semantic Segmentation 2.A SURVEY ON DEEP LEARNING METHODS FOR SEMANTIC IMAGE SEGMENTATION IN REAL-TIME 3.A SURVEY ON DEEP LEARNING METHODS FOR SEMANTIC 4.A Survey on Deep Learning Technique for Video Segmentation 5.A Survey on Instance Segmentation State of the art 6.A Survey on Label-efficient Deep Segmentation-Bridging the Gap between Weak Supervision and Dense Prediction 7.A Technical Survey and Evaluation of Traditional Point Cloud Clustering for LiDAR Panoptic Segmentation 8.Evolution of Image Segmentation using Deep Convolutional Neural Network A Survey 9.On Efficient Real-Time Semantic Segmentation 10.Unsupervised Domain Adaptation for Semantic Image Segmentation-a Comprehensive Survey 4多任務學習對檢測+分割+關鍵點+車道線聯(lián)合任務訓練方法進行了匯總,； 1.Cascade R-CNN 2.Deep Multi-Task Learning for Joint Localization, Perception, and Prediction 3.Mask R-CNN 4.Mask Scoring R-CNN 5.Multi-Task Multi-Sensor Fusion for 3D Object Detection 6.MultiTask-CenterNet 7.OmniDet 8.YOLOP 9.YOLO-Pose 5目標跟蹤對單目標和多目標跟蹤,、濾波和端到端方法進行了匯總； 1.Camouflaged Object Detection and Tracking：A Survey 2.Deep Learning for UAV-based Object Detection and Tracking：A Survey 3.Deep Learning on Monocular Object Pose Detection and Tracking：A Comprehensive Overview 4.Detection, Recognition, and Tracking：A Survey 5.Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation：A Survey 6.Recent Advances in Embedding Methods for Multi-Object Tracking：A Survey 7.Single Object Tracking：A Survey of Methods, Datasets, and Evaluation Metrics 8.Visual Object Tracking with Discriminative Filters and Siamese Networks：A Survey and Outlook 6深度估計針對單目,、雙目深度估計方法進行了匯總,，對戶外常見問題與精度損失展開了討論； 1.A Survey on Deep Learning Techniques for Stereo-based Depth Estimation 2.Deep Learning based Monocular Depth Prediction：Datasets, Methods and Applications 3.Monocular Depth Estimation Based On Deep Learning：An Overview 4.Monocular Depth Estimation：A Survey 5.Outdoor Monocular Depth Estimation：A Research Review 6.Towards Real-Time Monocular Depth Estimation for Robotics：A Survey 7多模態(tài)融合針對Lidar,、Radar,、視覺等數(shù)據(jù)方案進行融合感知； 1.A Survey on Deep Domain Adaptation for LiDAR Perception 2.Automatic Target Recognition on Synthetic Aperture Radar Imagery：A Survey 3.Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving：Datasets, Methods, and Challenges 4.MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving：A Review 5.Multi-Modal 3D Object Detection in Autonomous Driving：A Survey 6.Multi-modal Sensor Fusion for Auto Driving Perception：A Survey 7.Multi-Sensor 3D Object Box Refinement for Autonomous Driving 8.Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving 83D檢測對基于單目圖像,、雙目圖像,、點云數(shù)據(jù)、多模態(tài)數(shù)據(jù)的3D檢測方法進行了梳理,； 1.3D Object Detection for Autonomous Driving：A Review and New Outlooks 2.3D Object Detection from Images for Autonomous Driving A Survey 3.A Survey of Robust LiDAR-based 3D Object Detection Methods for autonomous driving 4.A Survey on 3D Object Detection Methods for Autonomous Driving Applications 5.Deep Learning for 3D Point Cloud Understanding：A Survey 6.Multi-Modal 3D Object Detection in Autonomous Driving：a survey 7.Survey and Systematization of 3D Object Detection Models and Methods 9關鍵點檢測人體關鍵點檢測方法匯總,，對車輛關鍵點檢測具有一定參考價值； 1.2D Human Pose Estimation：A Survey 2.A survey of top-down approaches for human pose estimation 3.Efficient Annotation and Learning for 3D Hand Pose Estimation：A Survey 4.Recent Advances in Monocular 2D and 3D Human Pose Estimation：A Deep Learning Perspective 10Transformer綜述視覺transformer,、輕量級transformer方法匯總,； 1.A Survey of Visual Transformers 2.A Survey on Visual Transformer 3.Efficient Transformers：A Survey 11車道線檢測對2D/3D車道線檢測方法進行了匯總，基于分類,、檢測,、分割、曲線擬合等,； 2D車道線1.A Keypoint-based Global Association Network for Lane Detection 2.CLRNet：Cross Layer Refinement Network for Lane Detection 3.End-to-End Deep Learning of Lane Detection and Path Prediction for Real-Time Autonomous Driving 4.End-to-end Lane Detection through Differentiable Least-Squares Fitting 5.Keep your Eyes on the Lane：Real-time Attention-guided Lane Detection 6.LaneNet：Real-Time Lane Detection Networks for Autonomous Driving 7.Towards End-to-End Lane Detection：an Instance Segmentation Approach 8.Ultra Fast Structure-aware Deep Lane Detection 3D車道線1.3D-LaneNet+：Anchor Free Lane Detection using a Semi-Local Representation 2.Deep Multi-Sensor Lane Detection 3.FusionLane：Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural Networks 4.Gen-LaneNet：A Generalized and Scalable Approach for 3D Lane Detection 5.ONCE-3DLanes：Building Monocular 3D Lane Detection 6.3D-LaneNet：End-to-End 3D Multiple 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