Attention-based U-Net Method for Autonomous Lane Detection

• URL: http://arxiv.org/abs/2411.10902v1
• Date: Sat, 16 Nov 2024 22:20:11 GMT
• Title: Attention-based U-Net Method for Autonomous Lane Detection
• Authors: Mohammadhamed Tangestanizadeh, Mohammad Dehghani Tezerjani, Saba Yousefian Jazi,
• Abstract summary: 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.
• Score: 0.5461938536945723
• License:
• Abstract: Lane detection involves identifying lanes on the road and accurately determining their location and shape. This is a crucial technique for modern assisted and autonomous driving systems. However, several unique properties of lanes pose challenges for detection methods. The lack of distinctive features can cause lane detection algorithms to be confused by other objects with similar appearances. Additionally, the varying number of lanes and the diversity in lane line patterns, such as solid, broken, single, double, merging, and splitting lines, further complicate the task. To address these challenges, Deep Learning (DL) approaches can be employed in various ways. Merging DL models with an attention mechanism has recently surfaced as a new approach. In this context, 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. The advanced model, achieving an extraordinary 98.98% accuracy and far surpassing the basic U-Net model, clearly showcases its superiority over existing methods in a comparative analysis. The groundbreaking findings of this research pave the way for the development of more effective and reliable road lane detection methods, significantly advancing the capabilities of modern assisted and autonomous driving systems.

Related papers

• On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance [70.80612792049315]
  This paper contributes a new large-scale dataset named Traffic Object Importance (TOI) It proposes a model that integrates multi-fold top-down guidance with the bottom-up feature. Our model outperforms state-of-the-art methods by large margins.
  arXiv  Detail & Related papers  (2024-11-26T06:37:10Z)
• 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)
• Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection [5.428120316375907]
  We have performed cross dataset analysis and network architecture repair for the lane detection application in autonomous vehicles. ERFCondLaneNet is an enhancement to the CondlaneNet used for lane identification framework to solve the difficulty of detecting lane lines with complex topologies.
  arXiv  Detail & Related papers  (2024-09-10T20:27:49Z)
• ENet-21: An Optimized light CNN Structure for Lane Detection [1.4542411354617986]
  This study develops an optimal structure for the lane detection problem. It offers a promising solution for driver assistance features in modern vehicles. Experiments on the TuSimple dataset support the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2024-03-28T19:07:26Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• 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)
• RONELDv2: A faster, improved lane tracking method [1.3965477771846408]
  Lane detection is an integral part of control systems in autonomous vehicles and lane departure warning systems. This paper proposes an improved, lighter weight lane detection method, RONELDv2. Experiments using the proposed improvements show a consistent increase in lane detection accuracy results across different datasets and deep learning models.
  arXiv  Detail & Related papers  (2022-02-26T13:12:09Z)
• Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction [71.97877759413272]
  Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. Our work addresses two key challenges in trajectory prediction, learning outputs, and better predictions by imposing constraints using driving knowledge.
  arXiv  Detail & Related papers  (2021-04-16T17:58:56Z)
• 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)
• 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.