High-Definition Map Generation Technologies For Autonomous Driving: A Review

• URL: http://arxiv.org/abs/2206.05400v1
• Date: Sat, 11 Jun 2022 02:32:11 GMT
• Title: High-Definition Map Generation Technologies For Autonomous Driving: A Review
• Authors: Zhibin Bao, Sabir Hossain, Haoxiang Lang, Xianke Lin
• Abstract summary: High-definition (HD) maps have drawn lots of attention in recent years. This paper reviews recent HD map generation technologies that leverage both 2D and 3D map generation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• TopoSD: Topology-Enhanced Lane Segment Perception with SDMap Prior [70.84644266024571]
  We propose to train a perception model to "see" standard definition maps (SDMaps) We encode SDMap elements into neural spatial map representations and instance tokens, and then incorporate such complementary features as prior information. Based on the lane segment representation framework, the model simultaneously predicts lanes, centrelines and their topology.
  arXiv  Detail & Related papers  (2024-11-22T06:13:42Z)
• Exploring Navigation Maps for Learning-Based Motion Prediction [9.919575841909962]
  We describe an approach to integrate navigation maps into learning-based motion prediction models. Our approach shows a significant improvement over not using a map at all. Our publicly available navigation map API for Argoverse enables researchers to develop and evaluate their own approaches using navigation maps.
  arXiv  Detail & Related papers  (2023-02-13T09:06:27Z)
• THMA: Tencent HD Map AI System for Creating HD Map Annotations [12.554528330142732]
  We introduce the Tencent HD Map AI (THMA) system, an end-to-end, AI-based, active learning HD map labeling system. In THMA, we train AI models directly from massive HD map datasets via supervised, self-supervised, and weakly supervised learning. More than 90 percent of the HD map data in Tencent Map is labeled automatically by THMA, accelerating the traditional HD map labeling process by more than ten times.
  arXiv  Detail & Related papers  (2022-12-14T08:36:31Z)
• ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints [94.60414567852536]
  Long-range navigation requires both planning and reasoning about local traversability. We propose a learning-based approach that integrates learning and planning. ViKiNG can leverage its image-based learned controller and goal-directed to navigate to goals up to 3 kilometers away.
  arXiv  Detail & Related papers  (2022-02-23T02:14:23Z)
• HDMapNet: An Online HD Map Construction and Evaluation Framework [23.19001503634617]
  HD map construction is a crucial problem for autonomous driving. Traditional HD maps are coupled with centimeter-level accurate localization which is unreliable in many scenarios. Online map learning is a more scalable way to provide semantic and geometry priors to self-driving vehicles.
  arXiv  Detail & Related papers  (2021-07-13T18:06:46Z)
• HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps [81.86923212296863]
  HD maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. There are only a small amount of real-world road topologies and geometries, which significantly limits our ability to test out the self-driving stack. We propose HDMapGen, a hierarchical graph generation model capable of producing high-quality and diverse HD maps.
  arXiv  Detail & Related papers  (2021-06-28T17:59:30Z)
• MP3: A Unified Model to Map, Perceive, Predict and Plan [84.07678019017644]
  MP3 is an end-to-end approach to mapless driving where the input is raw sensor data and a high-level command. We show that our approach is significantly safer, more comfortable, and can follow commands better than the baselines in challenging long-term closed-loop simulations.
  arXiv  Detail & Related papers  (2021-01-18T00:09:30Z)
• DAGMapper: Learning to Map by Discovering Lane Topology [84.12949740822117]
  We focus on drawing the lane boundaries of complex highways with many lanes that contain topology changes due to forks and merges. We formulate the problem as inference in a directed acyclic graphical model (DAG), where the nodes of the graph encode geometric and topological properties of the local regions of the lane boundaries. We show the effectiveness of our approach on two major North American Highways in two different states and show high precision and recall as well as 89% correct topology.
  arXiv  Detail & Related papers  (2020-12-22T21:58:57Z)
• HDNET: Exploiting HD Maps for 3D Object Detection [99.49035895393934]
  We show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. We design a single stage detector that extracts geometric and semantic features from the HD maps. As maps might not be available everywhere, we also propose a map prediction module that estimates the map on the fly from raw LiDAR data.
  arXiv  Detail & Related papers  (2020-12-21T21:59:54Z)
• Automatic Building and Labeling of HD Maps with Deep Learning [18.9340830352492]
  We propose a novel method capable of generating labelled HD maps from raw sensor data. The results show that the pro-posed deep learning based method can produce highly accurate HD maps.
  arXiv  Detail & Related papers  (2020-06-01T00:02:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.