TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem via Transformer-Based Architecture

• URL: http://arxiv.org/abs/2306.05419v1
• Date: Thu, 8 Jun 2023 17:58:57 GMT
• Title: TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem via Transformer-Based Architecture
• Authors: M. Esat Kalfaoglu, Halil Ibrahim Ozturk, Ozsel Kilinc, Alptekin Temizel
• Abstract summary: We introduce TopoMask for predicting centerlines in road topology. TopoMask has ranked 4th in the OpenLane-V2 Score (OLS) and 2nd in the F1 score of centerline prediction in OpenLane Topology Challenge 2023.
• Score: 4.970364068620607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driving scene understanding task involves detecting static elements such as lanes, traffic signs, and traffic lights, and their relationships with each other. To facilitate the development of comprehensive scene understanding solutions using multiple camera views, a new dataset called Road Genome (OpenLane-V2) has been released. This dataset allows for the exploration of complex road connections and situations where lane markings may be absent. Instead of using traditional lane markings, the lanes in this dataset are represented by centerlines, which offer a more suitable representation of lanes and their connections. In this study, we have introduced a new approach called TopoMask for predicting centerlines in road topology. Unlike existing approaches in the literature that rely on keypoints or parametric methods, TopoMask utilizes an instance-mask based formulation with a transformer-based architecture and, in order to enrich the mask instances with flow information, a direction label representation is proposed. TopoMask have ranked 4th in the OpenLane-V2 Score (OLS) and ranked 2nd in the F1 score of centerline prediction in OpenLane Topology Challenge 2023. In comparison to the current state-of-the-art method, TopoNet, the proposed method has achieved similar performance in Frechet-based lane detection and outperformed TopoNet in Chamfer-based lane detection without utilizing its scene graph neural network.

Related papers

• 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)
• RoadPainter: Points Are Ideal Navigators for Topology transformER [10.179711440042123]
  Topology reasoning aims to provide a precise understanding of road scenes, enabling autonomous systems to identify safe and efficient routes. We present RoadPainter, an innovative approach for detecting and reasoning the topology of lane centerlines using multi-view images.
  arXiv  Detail & Related papers  (2024-07-22T03:23:35Z)
• 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)
• MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph Fusion [7.835154677901744]
  Existing methods for evaluating the importance of nodes in traffic networks only consider topological information and traffic volumes. We propose a graph learning-based framework (MGL2Rank) that integrates the rich characteristics of road networks to rank the importance of nodes.
  arXiv  Detail & Related papers  (2023-05-20T13:46:44Z)
• OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping [84.65114565766596]
  We present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.
  arXiv  Detail & Related papers  (2023-04-20T16:31:22Z)
• Graph-based Topology Reasoning for Driving Scenes [102.35885039110057]
  We present TopoNet, the first end-to-end framework capable of abstracting traffic knowledge beyond conventional perception tasks. We evaluate TopoNet on the challenging scene understanding benchmark, OpenLane-V2.
  arXiv  Detail & Related papers  (2023-04-11T15:23:29Z)
• Online Lane Graph Extraction from Onboard Video [133.68032636906133]
  We use the video stream from an onboard camera for online extraction of the surrounding's lane graph. Using video, instead of a single image, as input poses both benefits and challenges in terms of combining the information from different timesteps. A single model of this proposed simple, yet effective, method can process any number of images, including one, to produce accurate lane graphs.
  arXiv  Detail & Related papers  (2023-04-03T12:36:39Z)
• PaRK-Detect: Towards Efficient Multi-Task Satellite Imagery Road Extraction via Patch-Wise Keypoints Detection [12.145321599949236]
  We propose a new scheme for multi-task satellite imagery road extraction, Patch-wise Road Keypoints Detection (PaRK-Detect) Our framework predicts the position of patch-wise road keypoints and the adjacent relationships between them to construct road graphs in a single pass. We evaluate our approach against the existing state-of-the-art methods on DeepGlobe, Massachusetts Roads, and RoadTracer datasets and achieve competitive or better results.
  arXiv  Detail & Related papers  (2023-02-26T08:26:26Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)

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.