HisTrackMap: Global Vectorized High-Definition Map Construction via History Map Tracking

• URL: http://arxiv.org/abs/2503.07168v2
• Date: Wed, 12 Mar 2025 08:21:55 GMT
• Title: HisTrackMap: Global Vectorized High-Definition Map Construction via History Map Tracking
• Authors: Jing Yang, Sen Yang, Xiao Tan, Hanli Wang,
• Abstract summary: We propose a novel end-to-end tracking framework global map construction by temporally tracking map elements' historical trajectories.<n>We introduce a Map-Trajectory Prior Fusion module within this tracking framework, leveraging historical priors for tracked instances to improve temporal smoothness and continuity.<n>Substantial experiments on the nuScenes and Argoverse2 datasets demonstrate that the proposed method outperforms state-of-the-art (SOTA) methods in both single-frame and temporal metrics.
• Score: 24.21124150354725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As an essential component of autonomous driving systems, high-definition (HD) maps provide rich and precise environmental information for auto-driving scenarios; however, existing methods, which primarily rely on query-based detection frameworks to directly model map elements or implicitly propagate queries over time, often struggle to maintain consistent temporal perception outcomes. These inconsistencies pose significant challenges to the stability and reliability of real-world autonomous driving and map data collection systems. To address this limitation, we propose a novel end-to-end tracking framework for global map construction by temporally tracking map elements' historical trajectories. Firstly, instance-level historical rasterization map representation is designed to explicitly store previous perception results, which can control and maintain different global instances' history information in a fine-grained way. Secondly, we introduce a Map-Trajectory Prior Fusion module within this tracking framework, leveraging historical priors for tracked instances to improve temporal smoothness and continuity. Thirdly, we propose a global perspective metric to evaluate the quality of temporal geometry construction in HD maps, filling the gap in current metrics for assessing global geometric perception results. Substantial experiments on the nuScenes and Argoverse2 datasets demonstrate that the proposed method outperforms state-of-the-art (SOTA) methods in both single-frame and temporal metrics. The project page is available at: https://yj772881654.github.io/HisTrackMap.

Related papers

• InteractionMap: Improving Online Vectorized HDMap Construction with Interaction [0.4551615447454768]
  State-of-the-art map vectorization methods are mainly based on DETR-like framework to generate HD maps in an end-to-end manner. In this paper, we propose InteractionMap, which improves previous map vectorization methods by fully leveraging local-to-global information interaction.
  arXiv  Detail & Related papers  (2025-03-27T16:23:15Z)
• IC-Mapper: Instance-Centric Spatio-Temporal Modeling for Online Vectorized Map Construction [18.975185033472968]
  IC-Mapper is an instance-centric online mapping framework, which comprises two primary components.<n>We perform point sampling on the historical global map from a spatial dimension and integrate it with the detection results of instances corresponding to the current frame to achieve real-time expansion and update of the map.
  arXiv  Detail & Related papers  (2025-03-05T20:28:34Z)
• Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments [18.7565126823704]
  We introduce an online metric-semantic mapping system that generates a global metric-semantic mesh map of large-scale outdoor environments.<n>Our mapping process achieves exceptional speed, with frame processing taking less than 7ms, regardless of scenario scale.<n>We integrate the resultant map into a real-world navigation system, enabling metric-semantic-based terrain assessment and autonomous point-to-point navigation within a campus environment.
  arXiv  Detail & Related papers  (2024-11-30T00:05:10Z)
• 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)
• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• Towards Real-World Visual Tracking with Temporal Contexts [64.7981374129495]
  We propose a two-level framework (TCTrack) that can exploit temporal contexts efficiently. Based on it, we propose a stronger version for real-world visual tracking, i.e., TCTrack++. For feature extraction, we propose an attention-based temporally adaptive convolution to enhance the spatial features. For similarity map refinement, we introduce an adaptive temporal transformer to encode the temporal knowledge efficiently.
  arXiv  Detail & Related papers  (2023-08-20T17:59:40Z)
• TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement [64.11385310305612]
  We present a novel model for Tracking Any Point (TAP) that effectively tracks any queried point on any physical surface throughout a video sequence. Our approach employs two stages: (1) a matching stage, which independently locates a suitable candidate point match for the query point on every other frame, and (2) a refinement stage, which updates both the trajectory and query features based on local correlations. The resulting model surpasses all baseline methods by a significant margin on the TAP-Vid benchmark, as demonstrated by an approximate 20% absolute average Jaccard (AJ) improvement on DAVIS.
  arXiv  Detail & Related papers  (2023-06-14T17:07:51Z)
• Neural Map Prior for Autonomous Driving [17.198729798817094]
  High-definition (HD) semantic maps are crucial in enabling autonomous vehicles to navigate urban environments. Traditional method of creating offline HD maps involves labor-intensive manual annotation processes. Recent studies have proposed an alternative approach that generates local maps using online sensor observations. In this study, we propose Neural Map Prior (NMP), a neural representation of global maps.
  arXiv  Detail & Related papers  (2023-04-17T17:58:40Z)
• SpOT: Spatiotemporal Modeling for 3D Object Tracking [68.12017780034044]
  3D multi-object tracking aims to consistently identify all mobile time. Current 3D tracking methods rely on abstracted information and limited history. We develop a holistic representation of scenes that leverage both spatial and temporal information.
  arXiv  Detail & Related papers  (2022-07-12T21:45:49Z)
• Learning to Track with Object Permanence [61.36492084090744]
  We introduce an end-to-end trainable approach for joint object detection and tracking. Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI, and MOT17 datasets.
  arXiv  Detail & Related papers  (2021-03-26T04:43:04Z)
• Radar-based Dynamic Occupancy Grid Mapping and Object Detection [55.74894405714851]
  In recent years, the classical occupancy grid map approach has been extended to dynamic occupancy grid maps. This paper presents the further development of a previous approach. The data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied.
  arXiv  Detail & Related papers  (2020-08-09T09:26:30Z)

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.