Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks

• URL: http://arxiv.org/abs/2404.06860v3
• Date: Mon, 28 Oct 2024 06:03:31 GMT
• Title: Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
• Authors: Fulong Ma, Weiqing Qi, Guoyang Zhao, Linwei Zheng, Sheng Wang, Yuxuan Liu, Ming Liu, Jun Ma,
• Abstract summary: 3D lane detection is essential in autonomous driving as it extracts structural and traffic information from the road in three-dimensional space. Recent advancements in visual perception seem inadequate for the development of fully reliable 3D lane detection algorithms. This review looks back and analyzes the current state of achievements in the field of 3D lane detection research.
• Score: 10.780826266192621
• License:
• Abstract: 3D lane detection is essential in autonomous driving as it extracts structural and traffic information from the road in three-dimensional space, aiding self-driving cars in logical, safe, and comfortable path planning and motion control. Given the cost of sensors and the advantages of visual data in color information, 3D lane detection based on monocular vision is an important research direction in the realm of autonomous driving, increasingly gaining attention in both industry and academia. Regrettably, recent advancements in visual perception seem inadequate for the development of fully reliable 3D lane detection algorithms, which also hampers the progress of vision-based fully autonomous vehicles. We believe that there is still considerable room for improvement in 3D lane detection algorithms for autonomous vehicles using visual sensors, and significant enhancements are needed. This review looks back and analyzes the current state of achievements in the field of 3D lane detection research. It covers all current monocular-based 3D lane detection processes, discusses the performance of these cutting-edge algorithms, analyzes the time complexity of various algorithms, and highlights the main achievements and limitations of ongoing research efforts. The survey also includes a comprehensive discussion of available 3D lane detection datasets and the challenges that researchers face but have not yet resolved. Finally, our work outlines future research directions and invites researchers and practitioners to join this exciting field.

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)
• A Comprehensive Review of 3D Object Detection in Autonomous Driving: Technological Advances and Future Directions [11.071271817366739]
  3D object perception has become a crucial component in the development of autonomous driving systems. This review extensively summarizes traditional 3D object detection methods, focusing on camera-based, LiDAR-based, and fusion detection techniques. We discuss future directions, including methods to improve accuracy such as temporal perception, occupancy grids, and end-to-end learning frameworks.
  arXiv  Detail & Related papers  (2024-08-28T01:08:33Z)
• Vision-based 3D occupancy prediction in autonomous driving: a review and outlook [19.939380586314673]
  We introduce the background of vision-based 3D occupancy prediction and discuss the challenges in this task. We conduct a comprehensive survey of the progress in vision-based 3D occupancy prediction from three aspects. We present a summary of prevailing research trends and propose some inspiring future outlooks.
  arXiv  Detail & Related papers  (2024-05-04T07:39:25Z)
• Surround-View Vision-based 3D Detection for Autonomous Driving: A Survey [0.6091702876917281]
  We provide a literature survey for the existing Vision Based 3D detection methods, focused on autonomous driving. We have highlighted how the literature and industry trend have moved towards surround-view image based methods and note down thoughts on what special cases this method addresses.
  arXiv  Detail & Related papers  (2023-02-13T19:30:17Z)
• HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving [95.42203932627102]
  3D human pose estimation is an emerging technology, which can enable the autonomous vehicle to perceive and understand the subtle and complex behaviors of pedestrians. Our method efficiently makes use of these complementary signals, in a semi-supervised fashion and outperforms existing methods with a large margin. Specifically, we embed LiDAR points into pixel-aligned multi-modal features, which we pass through a sequence of Transformer refinement stages.
  arXiv  Detail & Related papers  (2022-12-15T11:15:14Z)
• 3D Object Detection for Autonomous Driving: A Comprehensive Survey [48.30753402458884]
  3D object detection, which intelligently predicts the locations, sizes, and categories of the critical 3D objects near an autonomous vehicle, is an important part of a perception system. This paper reviews the advances in 3D object detection for autonomous driving.
  arXiv  Detail & Related papers  (2022-06-19T19:43:11Z)
• Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception [59.2014692323323]
  Small, far-away, or highly occluded objects are particularly challenging because there is limited information in the LiDAR point clouds for detecting them. We propose a novel, end-to-end trainable Hindsight framework to extract contextual information from past data. We show that this framework is compatible with most modern 3D detection architectures and can substantially improve their average precision on multiple autonomous driving datasets.
  arXiv  Detail & Related papers  (2022-03-22T00:58:27Z)
• 3D Object Detection from Images for Autonomous Driving: A Survey [68.33502122185813]
  3D object detection from images is one of the fundamental and challenging problems in autonomous driving. More than 200 works have studied this problem from 2015 to 2021, encompassing a broad spectrum of theories, algorithms, and applications. We provide the first comprehensive survey of this novel and continuously growing research field, summarizing the most commonly used pipelines for image-based 3D detection.
  arXiv  Detail & Related papers  (2022-02-07T07:12:24Z)
• Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection [55.12894776039135]
  State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit to domain idiosyncrasies. We propose a novel learning approach that drastically reduces this gap by fine-tuning the detector on pseudo-labels in the target domain. We show, on five autonomous driving datasets, that fine-tuning the detector on these pseudo-labels substantially reduces the domain gap to new driving environments.
  arXiv  Detail & Related papers  (2021-03-26T01:18:11Z)

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.