ExelMap: Explainable Element-based HD-Map Change Detection and Update

• URL: http://arxiv.org/abs/2409.10178v1
• Date: Mon, 16 Sep 2024 11:17:33 GMT
• Title: ExelMap: Explainable Element-based HD-Map Change Detection and Update
• Authors: Lena Wild, Ludvig Ericson, Rafael Valencia, Patric Jensfelt,
• Abstract summary: We propose a novel task of explainable element-based HD map change detection and update. We present ExelMap, an explainable element-based map updating strategy that specifically identifies changed map elements. This is the first comprehensive problem investigation of real-world end-to-end element-based HD map change detection and update.
• Score: 2.79552147676281
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Acquisition and maintenance are central problems in deploying high-definition (HD) maps for autonomous driving, with two lines of research prevalent in current literature: Online HD map generation and HD map change detection. However, the generated map's quality is currently insufficient for safe deployment, and many change detection approaches fail to precisely localize and extract the changed map elements, hence lacking explainability and hindering a potential fleet-based cooperative HD map update. In this paper, we propose the novel task of explainable element-based HD map change detection and update. In extending recent approaches that use online mapping techniques informed with an outdated map prior for HD map updating, we present ExelMap, an explainable element-based map updating strategy that specifically identifies changed map elements. In this context, we discuss how currently used metrics fail to capture change detection performance, while allowing for unfair comparison between prior-less and prior-informed map generation methods. Finally, we present an experimental study on real-world changes related to pedestrian crossings of the Argoverse 2 Map Change Dataset. To the best of our knowledge, this is the first comprehensive problem investigation of real-world end-to-end element-based HD map change detection and update, and ExelMap the first proposed solution.

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)
• M3TR: Generalist HD Map Construction with Variable Map Priors [6.314412580044879]
  We introduce M3TR, a generalist approach for HD map construction both with and without map priors. We propose the first realistic scenarios with semantically diverse map priors. We show that training across all prior scenarios yields a single Generalist model, whose performance is on par with previous Expert models.
  arXiv  Detail & Related papers  (2024-11-15T16:14:48Z)
• Enhancing Vectorized Map Perception with Historical Rasterized Maps [37.48510990922406]
  We propose HRMapNet, leveraging a low-cost Historical Rasterized Map to enhance online vectorized map perception. The historicalized map can be easily constructed from past predicted vectorized results and provides valuable complementary information. HRMapNet can be integrated with most online vectorized map perception methods.
  arXiv  Detail & Related papers  (2024-09-01T05:22:33Z)
• Mind the map! Accounting for existing map information when estimating online HDMaps from sensor [15.275704436439012]
  Estimating HDMaps from sensors promises to significantly lighten costs. We propose to account for existing maps of the precise situation studied when estimating HDMaps. We introduce MapEX, a novel online HDMap estimation framework.
  arXiv  Detail & Related papers  (2023-11-17T13:40:10Z)
• Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps [51.24861159115138]
  Standard Definition (SD) maps are more affordable and have worldwide coverage, offering a scalable alternative. We propose a novel framework to integrate SD maps into online map prediction and propose a Transformer-based encoder, SD Map Representations from transFormers. This enhancement consistently and significantly boosts (by up to 60%) lane detection and topology prediction on current state-of-the-art online map prediction methods.
  arXiv  Detail & Related papers  (2023-11-07T15:42:22Z)
• Updating Street Maps using Changes Detected in Satellite Imagery [28.25061267734934]
  We propose a novel method that leverages the progression of satellite imagery over time to substantially improve accuracy. Our approach first compares satellite images captured at different times to identify portions of the physical road network that have visibly changed. We show that our change-based approach reduces map update error rates four-fold.
  arXiv  Detail & Related papers  (2021-10-13T02:50:26Z)
• 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)
• 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)
• Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps [78.2581910688094]
  This work introduces a novel self-enhancement method to harvest accurate object localization maps and object boundaries with only category labels as supervision. In particular, the proposed Self-Enhancement Maps achieve the state-of-the-art localization accuracy of 54.88% on ILSVRC.
  arXiv  Detail & Related papers  (2020-06-09T12:35:55Z)

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.