mlsanityData to 13 Oct 2026

High-Definition Map Generation Technologies For Autonomous Driving

arXiv 2206.05400 ↗First posted 11 Jun 2022cs.RO cs.CVcited by 14

Not on the map. Why?

Abstract (arXiv)

Autonomous driving has been among the most popular and challenging topics in the past few years. On the road to achieving full autonomy, researchers have utilized various sensors, such as LiDAR, camera, Inertial Measurement Unit (IMU), and GPS, and developed intelligent algorithms for autonomous driving applications such as object detection, object segmentation, obstacle avoidance, and path planning. High-definition (HD) maps have drawn lots of attention in recent years. Because of the high precision and informative level of HD maps in localization, it has immediately become one of the critical components of autonomous driving. From big organizations like Baidu Apollo, NVIDIA, and TomTom to individual researchers, researchers have created HD maps for different scenes and purposes for autonomous driving. It is necessary to review the state-of-the-art methods for HD map generation. This paper reviews recent HD map generation technologies that leverage both 2D and 3D map generation. This review introduces the concept of HD maps and their usefulness in autonomous driving and gives a detailed overview of HD map generation techniques. We will also discuss the limitations of the current HD map generation technologies to motivate future research.

Key facts. Cited by 14 papers in the map (data through 13 Oct 2026). Builds on 50 of its 141 references that are in the map. First posted on arXiv 11 Jun 2022.

Builds on

50 of 141 references.

References · 50

  1. 2017Attention Is All You Needcited by 60,106
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,950
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
  4. 2022Generative Adversarial Networkscited by 5,614
  5. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,231
  6. 2016Feature Pyramid Networks for Object Detectioncited by 5,027
  7. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,498
  8. 2015SSD: Single Shot MultiBox Detectorcited by 3,128
  9. 2018YOLOv3: An Incremental Improvementcited by 2,655
  10. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,159
  11. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,709
  12. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,418
38 more
  1. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  2. 2017Dynamic Routing Between Capsulescited by 689
  3. 2017Wasserstein GANcited by 636
  4. 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 277
  5. 2020One Thousand and One Hours: Self-driving Motion Prediction Datasetcited by 240
  6. 2017LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentationcited by 212
  7. 2017Spatial As Deep: Spatial CNN for Traffic Scene Understandingcited by 187
  8. 2020LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mappingcited by 185
  9. 2018SpaceNet: A Remote Sensing Dataset and Challenge Seriescited by 178
  10. 2021FAST-LIO2: Fast Direct LiDAR-inertial Odometrycited by 170
  11. 2018Towards End-to-End Lane Detection: an Instance Segmentation Approachcited by 129
  12. 2018RoadTracer: Automatic Extraction of Road Networks from Aerial Imagescited by 102
  13. 2017Beyond the Pixel-Wise Loss for Topology-Aware Delineationcited by 83
  14. 2020Key Points Estimation and Point Instance Segmentation Approach for Lane Detectioncited by 77
  15. 2016Finding Tiny Facescited by 72
  16. 2020FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filtercited by 70
  17. 2020Sat2Graph: Road Graph Extraction through Graph-Tensor Encodingcited by 55
  18. 2018Learning Attraction Field Representation for Robust Line Segment Detectioncited by 52
  19. 2021R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping packagecited by 49
  20. 2021LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mappingcited by 44
  21. 2019SegMap: Segment-based mapping and localization using data-driven descriptorscited by 42
  22. 2016Convolutional Gated Recurrent Networks for Video Segmentationcited by 27
  23. 2019Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoVcited by 27
  24. 2016TorontoCity: Seeing the World with a Million Eyescited by 24
  25. 2022FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometrycited by 23
  26. 2018Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunitiescited by 20
  27. 2021Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Drivingcited by 19
  28. 2020Capsules for Biomedical Image Segmentationcited by 18
  29. 2018CELLO-3D: Estimating the Covariance of ICP in the Real Worldcited by 13
  30. 2020LOL: Lidar-Only Odometry and Localization in 3D Point Cloud Mapscited by 13
  31. 2021R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mappingcited by 12
  32. 2021csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Mapscited by 12
  33. 2018Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networkscited by 11
  34. 2019A fast, complete, point cloud based loop closure for LiDAR odometry and mappingcited by 9
  35. 2020Monocular Rotational Odometry with Incremental Rotation Averaging and Loop Closurecited by 7
  36. 2017Towards a Functional System Architecture for Automated Vehiclescited by 6
  37. 2020A decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARscited by 6
  38. 2018Convolutional Gated Recurrent Units for Medical Relation Classificationcited by 1

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 4; 2025: 3; 2026: 3.

14 papers.

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  1. 2024P-MapNet: Far-seeing Map Generator Enhanced by both SDMap and HDMap Priorscited by 54
  2. 2023Towards Knowledge-driven Autonomous Drivingcited by 29
  3. 2022R3LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimatorcited by 3
  4. 2023An Overview about Emerging Technologies of Autonomous Drivingcited by 2
  5. 2023Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous Drivingcited by 2
  6. 2025Inferring Driving Maps by Deep Learning-based Trail Map Extractioncited by 2
  7. 2026SEF-MAP: Subspace-Decomposed Expert Fusion for Robust Multimodal HD Map Predictioncited by 2
  8. 2025Maps for Autonomous Driving: Full-process Survey and Frontierscited by 1
  9. 2026Learning Ego-Centric BEV Representations from a Perspective-Privileged View: Cross-View Supervision for Online HD Map Constructioncited by 1
  10. 2024PLCNet: Patch-wise Lane Correction Network for Automatic Lane Correction in High-definition Mapscited by 0
  11. 2024Automatic Odometry-Less OpenDRIVE Generation From Sparse Point Cloudscited by 0
  12. 2024Automated Parking Planning with Vision-Based BEV Approachcited by 0
2 more
  1. 2025FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Mapscited by 0
  2. 2026Faster Training, Fewer Labels: Self-Supervised Pretraining for Fine-Grained BEV Segmentationcited by 0