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https://github.com/autodriving-heart/Awesome-Autonomous-Driving

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https://github.com/autodriving-heart/Awesome-Autonomous-Driving

List: Awesome-Autonomous-Driving

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awesome-autonomous-driving

Host: GitHub
URL: https://github.com/autodriving-heart/Awesome-Autonomous-Driving
Owner: autodriving-heart
Created: 2024-01-14T06:59:01.000Z (6 months ago)
Default Branch: main
Last Pushed: 2024-01-23T13:29:11.000Z (5 months ago)
Last Synced: 2024-03-22T23:01:15.585Z (3 months ago)
Size: 7.81 KB
Stars: 224
Watchers: 6
Forks: 26
Open Issues: 0
Metadata Files:
- Readme: README.md

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awesome-stars - autodriving-heart/Awesome-Autonomous-Driving - awesome-autonomous-driving (Others)
ultimate-awesome - Awesome-Autonomous-Driving - Awesome-autonomous-driving. (Other Lists / Julia Lists)
awesome-ai-list-guide - Awesome-Autonomous-Driving - autonomous-driving (Others)

README

# Awesome-Autonomous-Driving

本仓库为自动驾驶之心团队整理的关于自动驾驶全行业技术栈相关surveys、papers、课程、学习交流社区，欢迎大家一起讨论学习！

## 自动驾驶学习社区

自动驾驶之心知识星球是过国内首个以自动驾驶技术栈为主线的交流学习社区（也是国内最大哦），这是一个前沿技术发布和学习的地方！我们汇总了自动驾驶感知（BEV、多模态感知、Occupancy、毫米波雷达视觉感知、车道线检测、3D感知、目标跟踪、多模态、多传感器融合、Transformer等）、自动驾驶定位建图（在线高精地图、高精地图、SLAM）、多传感器标定（Camera/Lidar/Radar/IMU等近20种方案）、Nerf、视觉语言模型、世界模型、规划控制、轨迹预测、领域技术方案、AI模型部署落地等几乎所有子方向的学习路线！

除此之外，还和数十家自动驾驶公司建立了内推渠道，简历直达！这里可以自由提问交流，许多算法工程师和硕博日常活跃，解决问题！初衷是希望能够汇集行业大佬的智慧，在学习和就业上帮到大家！星球的每周活跃度都在前50内，非常注重大家积极性的调度和讨论，欢迎加入一起成长！

[加入链接：自动驾驶之心知识星球 | 国内首个自动驾驶全栈学习社区，近30+感知/融合/规划/标定/预测等学习路线](https://mp.weixin.qq.com/s?__biz=Mzg2NzUxNTU1OA==&mid=2247580846&idx=1&sn=8bef76bf11bb0d5a92c32efdff315750&chksm=ceb99167f9ce18712e01dbbdb3991405ea619b5c8c57681c4f27debc8cc117cfb6d2690da9da&scene=21&token=85321819&lang=zh_CN#wechat_redirect)

## 自动驾驶课程

### 1）感知算法

[国内首个BEV感知全栈系列学习教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2MjRdDQO8jGkz1Sx4AoJ0sytlIU)

[多模态融合3D目标检测全栈教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2SYwAhLiNWCyKhwUhSDjFihH7tx)

[国内首个基于Transformer的分割检测+视觉大模型教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2TfDGv45mQbTBKS5km4MoQ4Vu66)

[Occupancy从入门到精通全栈教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2TerMWEsK9xR32mtgXwaFdxeSYs)

[国内首个面向量产的车道线感知教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2UdY0Nw3nbRWYDJv9o9lgMcOvti)

[点云3D目标检测理论与实战教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2WTzeULfAL2IZ32ezqrHhIQyrKG)

[单目3D与单目BEV全栈教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2Uc2U3dSQMqMX3FhA498V2x78Nd)

[国内首门毫米波&4D毫米波雷达理论实战教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2WZpSw1X4P6FdSF1OMgj5aAe90p)

### 2）多传感器标定融合

[多传感器融合与目标跟踪全栈教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2SHcc3nnl15j1f7EDGl0B22Vbg4)

[多传感器标定全栈系统学习教程（相机/Lidar/Radar/IMU近20+种在线/离线实战方案）](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2PHSMppQhtUsLZ2R18GWOMOK5GW)

[毫米波雷达和视觉融合感知全栈教程（深度学习+传统方式）](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2OpLfiVIGEGIt0vdHhqie7LqFbG)

### 3）模型部署

[基于TensroRT的CNN/Transformer/检测/BEV模型四大部署代码+CUDA加速全栈学习教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2Qzh2CekDJZMNuZq1Gfp3HOgIqx)

### 4）规划控制与预测

[规划控制理论&实战教程（从0到1彻底搞懂PNC算法）](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2R0KKH62Mo6UrSn6oxV6nL2LlH9)

[轨迹预测理论与实战教程（国内首个轨迹预测系列）](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2VBk5awcjfEpgwzJ0RFdP7ZcIqg)

[轨迹预测论文带读教程（从论文角度分析轨迹预测领域）](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2WOHt6sLxFGUTazapwEVLI3qeqq)

### 5）Nerf与自动驾驶

[国内首个Nerf与自动驾驶论文带读教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2VIPT5xg9bCdQpg2c9GaWELR38N)

### 6）大模型专场

[国内首个大模型与自动驾驶应用论文带读教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2YGQzZEjc5f7Z7zpdlGrsD9gNl7)

[世界模型与自动驾驶论文带读课程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2ZIw7WP2H3SPvnc2PiVCnP0trJv)

### 7）定位与建图

[在线高精地图与自动驾驶论文带读教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2ZXECfaORjGOsNH5ro5kULmfIOA)

### 8）自动驾驶仿真

[自动驾驶离不开的仿真！Carla-Autoware联合仿真实战](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2bCKkNYcHG2Vs9Nnynr5bZZQwBr)

### 9）大专栏系列

[多传感器融合感知标定全栈教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2PiRligVG0RyIohy6A3covkScbw)

[多传感器标定/融合感知/模型部署全栈教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2R2ZQoo9TK4wuCbVsP2Pg0l0dZ8)

[感知算法与模型部署全栈教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2R2VoF87LmdBvw2RnGBSgj2l2zL)

[自动驾驶全栈算法工程师系列](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2RCJlwWnGdgbFJAnXa5vaOK7Nh2)

[多模态融合感知大专栏](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2TkVCn9jpK4b1nQfuil54Cb58hL)

[自动驾驶全栈大专栏教程](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2VOGLKr9TaAcqURAey1MaX00Rv6)

[规划控制&轨迹预测大专栏](https://www.zdjszx.com/p/t_pc/goods_pc_detail/goods_detail/course_2WmTf6RRZcWqT43ttRMnDBlSKGw)

## 自动驾驶综述&Papers

今年的技术变更实在很快，在线高精地图、大模型、端到端自动驾驶、世界模型、Occ、Nerf这些新兴技术，慢慢走向量产的计划中，今天自动驾驶之心就为大家盘下近百篇综述和经典论文，涉及感知、定位、融Occupancy、大模型、端到端、规划控制、BEV感知、数据相关等，一览自动驾驶发展路线。

所有综述，均可在[【自动驾驶之心】知识星球](https://mp.weixin.qq.com/s?__biz=Mzg2NzUxNTU1OA==&mid=2247580846&idx=1&sn=8bef76bf11bb0d5a92c32efdff315750&chksm=ceb99167f9ce18712e01dbbdb3991405ea619b5c8c57681c4f27debc8cc117cfb6d2690da9da&scene=21&token=85321819&lang=zh_CN#wechat_redirect) 内下载！

### 0）自动驾驶功能与系统综述

1. A review: The Self-Driving Car’s Requirements and The Challenges it Faces
2. Object Detection, Recognition, and Tracking Algorithms for ADAS：A Study on Recent Trends
3. Survey of Technology in Autonomous Valet Parking System
4. Applications of Computer Vision in Autonomous Vehicles: Methods, Challenges and Future Directions
5. Potential sources of sensor data anomalies for autonomous vehicles: An overview from road vehicle safety perspective
6. A review of occluded objects detection in real complex scenarios for autonomous driving
7. A survey on deep learning approaches for data integration in Autonomous Driving System
8. Milestones in Autonomous Driving and Intelligent Vehicles
9. Perception and sensing for autonomous vehicles under adverse weather conditions: A survey

### 1）数据集汇总

1. A Survey on Autonomous Driving Datasets: Data Statistic, Annotation, and Outlook
2. A Survey on Self-evolving Autonomous Driving: a Perspective on Data Closed-Loop Technology
3. A Survey on Datasets for Decision-making of Autonomous Vehicle

### 1）端到端自动驾驶

1. Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey
2. End-to-end Autonomous Driving: Challenges and Frontiers

### 2）在线地图与离线地图

1. High-Definition Maps Construction Based on Visual Sensor: A Comprehensive Survey
2. Data Issues in High Definition Maps Furniture – A Survey
3. HDMapNet：基于语义分割的在线局部高精地图构建 (ICRA2022)
4. VectorMapNet：基于自回归方式的端到端矢量化地图构建（ICML2023）
5. MapTR ：基于固定数目点的矢量化地图构建 (ICLR2023)
6. MapTRv2：一种在线矢量化高清地图构建的端到端框架
7. PivotNet：基于动态枢纽点的矢量化地图构建（ICCV2023）
8. BeMapNet：基于贝塞尔曲线的矢量化地图构建 (CVPR2023)
9. LATR: 无显式BEV 特征的3D车道线检测 (ICCV2023)
10. TopoNet: 基于图的驾驶场景拓扑推理
11. TopoMLP: 先检测后推理（拓扑推理 strong pipeline）
12. LaneGAP：连续性在线车道图构建
13. Neural Map Prior: 神经地图先验辅助在线建图（CVPR2023）
14. MapEX：现有地图先验显著提升在线建图性能

### 3）大模型与自动驾驶

1. A Survey on Multimodal Large Language Models for Autonomous Driving
2. A Survey of Large Language Models for Autonomous Driving
3. CLIP：Learning Transferable Visual Models From Natural Language Supervision
4. BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
5. BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
6. InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
7. MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
8. InstructGPT：Training language models to follow instructions with human feedback
9. ADAPT: Action-aware Driving Caption Transformer
10. BEVGPT：Generative Pre-trained Large Model for Autonomous Driving Prediction, Decision-Making, and Planning
11. DriveGPT4：Interpretable End-to-end Autonomous Driving via Large Language Model
12. Drive Like a Human Rethinking Autonomous Driving with Large Language Models
13. Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving
14. HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving
15. LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
16. Planning-oriented Autonomous Driving
17. WEDGE A multi-weather autonomous driving dataset built from generative vision-language models

### 4）Nerf与自动驾驶

1. NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review
2. Neural Volume Rendering: NeRF And Beyond
3. MobileNeRF：移动端实时渲染，Nerf导出Mesh（CVPR2023）
4. Co-SLAM：实时视觉定位和NeRF建图（CVPR2023）
5. Neuralangelo：当前最好的NeRF表面重建方法（CVPR2023）
6. MARS：首个开源自动驾驶NeRF仿真工具（CICAI2023）
7. UniOcc：NeRF和3D占用网络（AD2023 Challenge）
8. Unisim：自动驾驶场景的传感器模拟（CVPR2023）

### 5）Occupancy占用网络

1. Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review

### 6）BEV感知

1. Vision-Centric BEV Perception: A Survey
2. Vision-RADAR fusion for Robotics BEV Detections: A Survey
3. Surround-View Vision-based 3D Detection for Autonomous Driving: A Survey
4. Delving into the Devils of Bird’s-eye-view Perception: A Review, Evaluation and Recipe

### 7）毫米波雷达感知

1. Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review
2. A Survey of Automotive Radar and Lidar Signal Processing and Architectures
3. Reviewing 3D Object Detectors in the Context of High-Resolution 3+1D Radar
4. Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges
5. Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
6. 4D Millimeter-Wave Radar in Autonomous Driving: A Survey
7. Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review
8. Radar Perception in Autonomous Driving: Exploring Different Data Representations

### 8）多模态/多传感融合

针对Lidar、Radar、视觉等数据方案进行融合感知；

1. A Comparative Review on Multi-modal Sensor Fusion based on Deep Learning
2. Emerging Trends in Autonomous Vehicle Perception: Multimodal Fusion for 3D Object Detection
3. A Survey on Deep Domain Adaptation for LiDAR Perception
4. Automatic Target Recognition on Synthetic Aperture Radar Imagery：A Survey
5. Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving：Datasets, Methods, and Challenges
6. MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving：A Review
7. Multi-Modal 3D Object Detection in Autonomous Driving：A Survey
8. Multi-modal Sensor Fusion for Auto Driving Perception：A Survey
9. Multi-Sensor 3D Object Box Refinement for Autonomous Driving
10. Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving
11. Multimodal Learning With Transformers: A Survey
12. Deep Model Fusion: A Survey
13. Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics

### 9）3D目标检测

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. Emerging Trends in Autonomous Vehicle Perception: Multimodal Fusion for 3D Object Detection
2. 3D Object Detection for Autonomous Driving: A Practical Survey
3. 3D Object Detection for Autonomous Driving：A Review and New Outlooks
4. 3D Object Detection from Images for Autonomous Driving A Survey
5. A Survey of Robust LiDAR-based 3D Object Detection Methods for autonomous driving
6. A Survey on 3D Object Detection Methods for Autonomous Driving Applications
7. Deep Learning for 3D Point Cloud Understanding：A Survey
8. Multi-Modal 3D Object Detection in Autonomous Driving：a survey
9. Survey and Systematization of 3D Object Detection Models and Methods

### 10）多传感器标定

1. A Comprehensive Overview of Fish-Eye Camera Distortion Correction Methods
2. External Extrinsic Calibration of Multi-Modal Imaging Sensors: A Review
3. Deep Learning for Camera Calibration and Beyond: A Survey

### 11）规划控制与轨迹预测

1. Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature
2. Human-Like Decision-Making of Autonomous Vehicles in Dynamic Traffic Scenarios
3. Machine Learning for Autonomous Vehicle’s Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions
4. Trajectory-Prediction with Vision: A Survey

### 12）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

### 13）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
6. Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey

### 14）分割综述

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

### 15）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

### 16）2D/3D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. 3D Multiple Object Tracking on Autonomous Driving: A Literature Review
2. Camouflaged Object Detection and Tracking：A Survey
3. Deep Learning for UAV-based Object Detection and Tracking：A Survey
4. Deep Learning on Monocular Object Pose Detection and Tracking：A Comprehensive Overview
5. Detection, Recognition, and Tracking：A Survey
6. Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation：A Survey
7. Recent Advances in Embedding Methods for Multi-Object Tracking：A Survey
8. Single Object Tracking：A Survey of Methods, Datasets, and Evaluation Metrics
9. Visual Object Tracking with Discriminative Filters and Siamese Networks：A Survey and Outlook

### 17）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

### 18）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

### 19）Transformer综述

视觉transformer、轻量级transformer方法汇总；

1. A Survey of Visual Transformers
2. Efficient Transformers：A Survey
3. Multimodal Learning With Transformers: A Survey
4. A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking
5. A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
6. Transformer-based models and hardware acceleration analysis in autonomous driving: A survey

### 20）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

### 21）SLAM

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
5. LiDAR-Based Place Recognition For Autonomous Driving: A Survey
6. LiDAR Odometry Survey: Recent Advancements and Remaining Challenges
7. A Survey of Vehicle Localization: Performance Analysis and Challenges
8. A Survey on Monocular Re-Localization: From the Perspective of Scene Map Representation
9. LiDAR-based SLAM for robotic mapping: state of the art and new frontiers

### 22）模型部署量化

1. Green Edge AI: A Contemporary Survey
2. A Survey on Deep Neural Network CompressionChallenges, Overview, and Solutions
3. Pruning and Quantization for Deep Neural Network Acceleration A Survey

### 23）协同感知

1. Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review
2. Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception

### 24）自动驾驶仿真

1. A Survey of Integrated Simulation Environments for Connected Automated Vehicles: Requirements, Tools, and Architecture
2. A Survey of Simulators for Autonomous Driving: Taxonomy, Challenges, and Evaluation Metrics
3. Data-driven Traffic Simulation: A Comprehensive Review

### 25）路面检测

1. Pavement Defect Detection with Deep Learning: A Comprehensive Survey

### 26）其它

1. Recent Advances in Multi-modal 3D Scene Understanding: A Comprehensive Survey and Evaluation
2. NeRF: Neural Radiance Field in 3D Vision, Introduction and Review
3. 3D point cloud for objects and scenes classification, recognition, segmentation, and reconstruction: A review

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