CenterLineDet: Road Lane CenterLine Graph Detection With Vehicle-Mounted Sensors by Transformer for High-definition Map Creation

• URL: http://arxiv.org/abs/2209.07734v1
• Date: Fri, 16 Sep 2022 06:15:26 GMT
• Title: CenterLineDet: Road Lane CenterLine Graph Detection With Vehicle-Mounted Sensors by Transformer for High-definition Map Creation
• Authors: Zhenhua Xu, Yuxuan Liu, Yuxiang Sun, Ming Liu, Lujia Wang
• Abstract summary: We propose a novel method named CenterLineDet to create the lane centerline HD map automatically. CenterLineDet is trained by imitation learning and can effectively detect the graph of lane centerlines by iterations with vehicle-mounted sensors. The proposed approach is evaluated on a large publicly available dataset Nuscenes.
• Score: 19.263691277963368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of autonomous vehicles, there witnesses a booming demand for high-definition maps (HD maps) that provide reliable and robust prior information of static surroundings in autonomous driving scenarios. As one of the main high-level elements in the HD map, the road lane centerline is critical for downstream tasks, such as prediction and planning. Manually annotating lane centerline HD maps by human annotators is labor-intensive, expensive and inefficient, severely restricting the wide application and fast deployment of autonomous driving systems. Previous works seldom explore the centerline HD map mapping problem due to the complicated topology and severe overlapping issues of road centerlines. In this paper, we propose a novel method named CenterLineDet to create the lane centerline HD map automatically. CenterLineDet is trained by imitation learning and can effectively detect the graph of lane centerlines by iterations with vehicle-mounted sensors. Due to the application of the DETR-like transformer network, CenterLineDet can handle complicated graph topology, such as lane intersections. The proposed approach is evaluated on a large publicly available dataset Nuscenes, and the superiority of CenterLineDet is well demonstrated by the comparison results. This paper is accompanied by a demo video and a supplementary document that are available at \url{https://tonyxuqaq.github.io/projects/CenterLineDet/}.

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)
• DeepAerialMapper: Deep Learning-based Semi-automatic HD Map Creation for Highly Automated Vehicles [0.0]
  We introduce a semi-automatic method for creating HD maps from high-resolution aerial imagery. Our method involves training neural networks to semantically segment aerial images into classes relevant to HD maps. Exporting the map to the Lanelet2 format allows easy extension for different use cases.
  arXiv  Detail & Related papers  (2024-10-01T15:05:05Z)
• LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving [60.55208681215818]
  We introduce LaneSegNet, the first end-to-end mapping network generating lane segments to obtain a complete representation of the road structure. Our algorithm features two key modifications. One is a lane attention module to capture pivotal region details within the long-range feature space. On the OpenLane-V2 dataset, LaneSegNet outperforms previous counterparts by a substantial gain across three tasks.
  arXiv  Detail & Related papers  (2023-12-26T16:22:10Z)
• Prior Based Online Lane Graph Extraction from Single Onboard Camera Image [133.68032636906133]
  We tackle online estimation of the lane graph from a single onboard camera image. The prior is extracted from the dataset through a transformer based Wasserstein Autoencoder. The autoencoder is then used to enhance the initial lane graph estimates.
  arXiv  Detail & Related papers  (2023-07-25T08:58:26Z)
• InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning [6.062751776009753]
  We propose online HD map learning framework that detects HD map elements from onboard sensor observations. InstaGraM, instance-level graph modeling of HD map brings accurate and fast end-to-end vectorized HD map learning. Our proposed network outperforms previous models by up to 13.7 mAP with up to 33.8X faster time.
  arXiv  Detail & Related papers  (2023-01-10T08:15:35Z)
• csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps [10.082536828708779]
  We propose csBoundary to automatically detect road boundaries at the city scale for HD map annotation. Our network takes as input an aerial image patch, and directly infers the continuous road-boundary graph from this image. Our csBoundary is evaluated and compared on a public benchmark dataset.
  arXiv  Detail & Related papers  (2021-11-11T02:04:36Z)
• 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)
• Trajectory Prediction for Autonomous Driving with Topometric Map [10.831436392239585]
  State-of-the-art autonomous driving systems rely on high definition (HD) maps for localization and navigation. We propose an end-to-end transformer networks based approach for map-less autonomous driving.
  arXiv  Detail & Related papers  (2021-05-09T08:16:16Z)
• 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)

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.