一点点更新哦~~ 我的IOV现在只局限在V2X里 

不知道还有没有一起的同学  要不要建个群什么的~ 网上各种资料较少 希望有同行一起交流哦~~

QQ群64104075 / WeChat github  -- whaosoft

一、

V2X 车联网

（Vehicle to everything）是汽车、电子、信息通信、交通运输和交通管理等行业深度融合的新型产业形态。

C-V2X

（Cellular vehicle to everything，蜂窝车联网）无线通信技术，目前包括 LTE-V2X 和 NR-V2X，作为关键使能型技术，将有助于构建 “人-车-路-云” 协同的车联网产业生态体系。

通信: UU PC5

车载终端(TBOX)、车载娱乐系统(IVI)

VBOX 、RSU 、OBU 

二、四跨相关

2018年三跨：

跨通信模组、跨终端、跨整车

2019年四跨：

跨芯片模组、跨终端、跨整车、跨安全平台

2020新四跨：

跨芯片模组、跨终端、跨整车、跨安全平台+高精地图和定位

去了2020和2021年,2023沈阳的四跨,所以在这里先说明一下哦~ 

三、busybox

四、autosar

五、自动驾驶相关

BEV感知、毫米波雷达视觉融合、多传感器标定、多传感器融合、多模态3D目标检测、点云3D目标检测、目标跟踪、Occupancy、cuda与TensorRT模型部署、协同感知、语义分割、自动驾驶仿真、传感器部署、决策规划、轨迹预测等

点云分割 SemanticKITTI 

六、自动驾驶涉及的视觉相关

多模态融合

针对Lidar、Radar、视觉等数据方案进行融合感知；

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

3D检测

对基于单目图像、双目图像、点云数据、多模态数据的3D检测方法进行了梳理；

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

目标检测综述

主要涉及通用目标检测任务、检测任务中的数据不均衡问题、伪装目标检测、自动驾驶领域检测任务、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

目标检测数据增强与不均衡问题

主要涉及目标检测任务中的数据增强、小目标检测、小样本学习、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

分割综述

主要对实时图像分割、视频分割、实例分割、弱监督/无监督分割、点云分割等方案展开讨论；

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

多任务学习

对检测+分割+关键点+车道线联合任务训练方法进行了汇总；

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

目标跟踪

对单目标和多目标跟踪、滤波和端到端方法进行了汇总；

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

深度估计

针对单目、双目深度估计方法进行了汇总，对户外常见问题与精度损失展开了讨论；

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

关键点检测

人体关键点检测方法汇总，对车辆关键点检测具有一定参考价值；

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

Transformer综述

视觉transformer、轻量级transformer方法汇总；

1. A Survey of Visual Transformers

2. A Survey on Visual Transformer

3. Efficient Transformers：A Survey

车道线检测

对2D/3D车道线检测方法进行了汇总，基于分类、检测、分割、曲线拟合等；

2D车道线

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车道线

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 Lane Detection

SLAM综述

定位与建图方案汇总；

1. A Survey on Active Simultaneous Localization and Mapping-State of the Art and New Frontiers

2. The Revisiting Problem in Simultaneous Localization and Mapping-A Survey on Visual Loop Closure Detection

3. From SLAM to Situational Awareness-Challenges

4. Simultaneous Localization and Mapping Related Datasets-A Comprehensive Survey

模型量化

1. A Survey on Deep Neural Network CompressionChallenges, Overview, and Solutions

七、行泊一体

汽车智能化已然成为行业发展趋势，智能驾驶正在加速融入我们的生活，ADAS功能逐渐成为各类新款车型的标配，近年来，“行泊一体”概念也成为行业内热门话题，越来越多的厂商开始着重发力于研发“行泊一体”方案。我们看到，现阶段智能汽车行业正在大规模力推无限接近于L3的L2++或L2.9自动驾驶量产落地。在这一过程中，各种新技术层出不穷，而融合众多新技术的“行泊一体”方案受到车企们的推崇。

从本质上来说，行泊一体是行车场景与泊车场景的打通，需要实现包括拥堵跟车、自动变道、高速巡航、上下匝道、自动泊车等一系列技术打通，实现关键技术主要包括感知与定位、决策与规划、控制与执行等，行泊一体方案是ADAS向高阶自动驾驶进阶的必经之路。

#自动驾驶常用功能场景

在日常的城市用车过程中常常遇到各种各样的行车泊车场景，根据不同路段的行车泊车场景，组成了智能汽车常见的智能驾驶功能包括：

行车场景：

ACC，自适应巡航控制；ALC，自动变道辅助；TJA，交通拥堵辅助；LCC车道居中；NOA，领航辅助；

泊车场景：

APA，自动泊车辅助功能；RPA，遥控泊车辅助；SS，智能召唤功能；HPA，记忆泊车功能；AVP，自主代客泊车。

#发展形态及趋势

我们可以看到，要实现上述自动驾驶行车、泊车的功能，现阶段市场上大部分车型采用传统的分布式ADAS ECU(比如，智能摄像头一体机、泊车控制器、全景环视控制器)行泊分离式域控制器，即行车域控和泊车域控相分离，行车与泊车调用各自芯片和传感器。然而随着整车电子电气架构由分布式向域集中式演进，以及传感器和域控制器共用技术的成熟，正进一步加快“行泊一体”方案的量产落地。

相较行泊分离方案，行泊一体方案具备如下优势：

• 传感器共用，性能得到提升

• 提升软件开发效率，底层软件、中间件更通用，还可单独定制不同算法模块功能；

• 方便功能迭代，行泊一体域控制器通过部署分层式的软件架构，实现软硬件解耦，配备有百兆甚至千兆以太网接口，并且有更多的算力支持高级算法模型的部署，因此能够更好地支持功能的OTA升级。

• 降本增效，复用了控制器算力，节省控制器的硬件成本，简化域控制器的硬件接口，减少布线长度，进而有效降低整车的复杂度，降低整个控制器的生产成本。