Real-Time Lane ID Estimation Using Recurrent Neural Networks With Dual Convention

• URL: http://arxiv.org/abs/2001.04708v1
• Date: Tue, 14 Jan 2020 10:52:30 GMT
• Title: Real-Time Lane ID Estimation Using Recurrent Neural Networks With Dual Convention
• Authors: Ibrahim Halfaoui, Fahd Bouzaraa, Onay Urfalioglu, Li Minzhen
• Abstract summary: We propose a vision-only (i.e. monocular camera) solution to the problem based on a dual left-right convention. We achieve more than 95% accuracy on a challenging test set with extreme conditions and different routes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Acquiring information about the road lane structure is a crucial step for autonomous navigation. To this end, several approaches tackle this task from different perspectives such as lane marking detection or semantic lane segmentation. However, to the best of our knowledge, there is yet no purely vision based end-to-end solution to answer the precise question: How to estimate the relative number or "ID" of the current driven lane within a multi-lane road or a highway? In this work, we propose a real-time, vision-only (i.e. monocular camera) solution to the problem based on a dual left-right convention. We interpret this task as a classification problem by limiting the maximum number of lane candidates to eight. Our approach is designed to meet low-complexity specifications and limited runtime requirements. It harnesses the temporal dimension inherent to the input sequences to improve upon high-complexity state-of-the-art models. We achieve more than 95% accuracy on a challenging test set with extreme conditions and different routes.

Related papers

• Monocular Lane Detection Based on Deep Learning: A Survey [51.19079381823076]
  Lane detection plays an important role in autonomous driving perception systems. As deep learning algorithms gain popularity, monocular lane detection methods based on deep learning have demonstrated superior performance. This paper presents a comprehensive overview of existing methods, encompassing both the increasingly mature 2D lane detection approaches and the developing 3D lane detection works.
  arXiv  Detail & Related papers  (2024-11-25T12:09:43Z)
• Attention-based U-Net Method for Autonomous Lane Detection [0.5461938536945723]
  Two deep learning-based lane recognition methods are proposed in this study. The first method employs the Feature Pyramid Network (FPN) model, delivering an impressive 87.59% accuracy in detecting road lanes. The second method, which incorporates attention layers into the U-Net model, significantly boosts the performance of semantic segmentation tasks.
  arXiv  Detail & Related papers  (2024-11-16T22:20:11Z)
• Sketch and Refine: Towards Fast and Accurate Lane Detection [69.63287721343907]
  Lane detection is a challenging task due to the complexity of real-world scenarios. Existing approaches, whether proposal-based or keypoint-based, suffer from depicting lanes effectively and efficiently. We present a "Sketch-and-Refine" paradigm that utilizes the merits of both keypoint-based and proposal-based methods. Experiments show that our SRLane can run at a fast speed (i.e., 278 FPS) while yielding an F1 score of 78.9%.
  arXiv  Detail & Related papers  (2024-01-26T09:28:14Z)
• 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)
• Multi Lane Detection [12.684545950979187]
  Lane detection is a basic module in autonomous driving. Our work is based on CNN backbone DLA-34, along with Affinity Fields. We investigate novel decoding methods to achieve more efficient lane detection algorithm.
  arXiv  Detail & Related papers  (2022-12-22T08:20:08Z)
• RCLane: Relay Chain Prediction for Lane Detection [76.62424079494285]
  We present a new method for lane detection based on relay chain prediction. Our strategy allows us to establish new state-of-the-art on four major benchmarks including TuSimple, CULane, CurveLanes and LLAMAS.
  arXiv  Detail & Related papers  (2022-07-19T16:48:39Z)
• 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)
• Detecting 32 Pedestrian Attributes for Autonomous Vehicles [103.87351701138554]
  In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes. We introduce a Multi-Task Learning (MTL) model relying on a composite field framework, which achieves both goals in an efficient way. We show competitive detection and attribute recognition results, as well as a more stable MTL training.
  arXiv  Detail & Related papers  (2020-12-04T15:10:12Z)
• Lane Detection Model Based on Spatio-Temporal Network With Double Convolutional Gated Recurrent Units [11.968518335236787]
  Lane detection will remain an open problem for some time to come. A-temporal network with double Conal Gated Recurrent Units (ConvGRUs) proposed to address lane detection in challenging scenes. Our model can outperform the state-of-the-art lane detection models.
  arXiv  Detail & Related papers  (2020-08-10T06:50:48Z)
• Where can I drive? A System Approach: Deep Ego Corridor Estimation for Robust Automated Driving [2.378161932344701]
  We propose to classify specifically a drivable corridor of the ego lane on pixel level with a deep learning approach. Our approach is kept computationally efficient with only 0.66 million parameters allowing its application in large scale products.
  arXiv  Detail & Related papers  (2020-04-16T13:04:18Z)
• Multi-lane Detection Using Instance Segmentation and Attentive Voting [0.0]
  We propose a novel solution to multi-lane detection, which outperforms state of the art methods in terms of both accuracy and speed. We are able to obtain a lane segmentation accuracy of 99.87% running at 54.53 fps (average)
  arXiv  Detail & Related papers  (2020-01-01T16:48:42Z)

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.