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Scene Understanding for Autonomous Driving
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Scene Understanding for Autonomous Driving

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 5816

Special Issue Editors

Dr. Yi Zhang

E-Mail Website
Guest Editor
Department of Computer Science, Sichuan University, Chengdu 610065, China
Interests: computer vision; machine intelligence; robotics
Dr. Yan Yan

E-Mail Website
Guest Editor
School of Informatics, Xiamen University, Xiamen 361005, China
Interests: image processing; machine intelligence
Dr. Li Yuan

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119077, Singapore
Interests: artificial intelligence; machine learning

Special Issue Information

Dear Colleagues,

One of the basic requirements of autonomous driving is for the vehicle to fully understand its surroundings (e.g., traffic scene). The complex task of outdoor scene understanding involves several sub-tasks such as depth estimation, segmentation, object detection and tracking, 3D reconstruction, etc. Each of these tasks describes a particular aspect of a scene. It is beneficial to model some of these aspects jointly to exploit the relations between different elements of the scene and obtain a holistic understanding. A successful scene understanding model obtains rich and compact representation of the scene (including elements, layout, and spatial relations among them). Thanks to the recent advancement in deep learning, the development of scene understanding has been brought to a new era. Apart from the traditional visual perception, other sensor devices are also utilized to enhance the environmental awareness, such as LiDAR (light detection and ranging), depth camera, and thermal camera, etc. Another aspect for scene understanding is 3D reconstruction; 3D reasoning plays a significant role in solving geometric problems and results in a more informative representation of the scene in the form of 3D object models, layout elements, and occlusion relationships. One specific challenge in scene understanding is the interpretation of urban and sub-urban traffic scenarios. Compared to highways and rural roads, urban scenarios comprise many independently moving traffic participants, more variability in the geometric layout of roads and crossroads, and an increased level of difficulty due to ambiguous visual features and illumination changes.

This Special Issue consists of the following scopes:

  • Semantic/instance/panoptic segmentation
  • Object classification, detection, and tracking
  • Depth estimation (and recovery)
  • 3D reconstruction

Prof. Dr. Yi Zhang
Dr. Yan Yan
Dr. Li Yuan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

30 pages, 32606 KiB  
Article
A Cluster-Based 3D Reconstruction System for Large-Scale Scenes
by Yao Li, Yue Qi, Chen Wang and Yongtang Bao
Sensors 2023, 23(5), 2377; https://doi.org/10.3390/s23052377 - 21 Feb 2023
Cited by 1 | Viewed by 1638
Abstract
The reconstruction of realistic large-scale 3D scene models using aerial images or videos has significant applications in smart cities, surveying and mapping, the military and other fields. In the current state-of-the-art 3D-reconstruction pipeline, the massive scale of the scene and the enormous amount [...] Read more.
The reconstruction of realistic large-scale 3D scene models using aerial images or videos has significant applications in smart cities, surveying and mapping, the military and other fields. In the current state-of-the-art 3D-reconstruction pipeline, the massive scale of the scene and the enormous amount of input data are still considerable obstacles to the rapid reconstruction of large-scale 3D scene models. In this paper, we develop a professional system for large-scale 3D reconstruction. First, in the sparse point-cloud reconstruction stage, the computed matching relationships are used as the initial camera graph and divided into multiple subgraphs by a clustering algorithm. Multiple computational nodes execute the local structure-from-motion (SFM) technique, and local cameras are registered. Global camera alignment is achieved by integrating and optimizing all local camera poses. Second, in the dense point-cloud reconstruction stage, the adjacency information is decoupled from the pixel level by red-and-black checkerboard grid sampling. The optimal depth value is obtained using normalized cross-correlation (NCC). Additionally, during the mesh-reconstruction stage, feature-preserving mesh simplification, Laplace mesh-smoothing and mesh-detail-recovery methods are used to improve the quality of the mesh model. Finally, the above algorithms are integrated into our large-scale 3D-reconstruction system. Experiments show that the system can effectively improve the reconstruction speed of large-scale 3D scenes. Full article
(This article belongs to the Special Issue Scene Understanding for Autonomous Driving)
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14 pages, 6514 KiB  
Article
Towards Accurate Ground Plane Normal Estimation from Ego-Motion
by Jiaxin Zhang, Wei Sui, Qian Zhang, Tao Chen and Cong Yang
Sensors 2022, 22(23), 9375; https://doi.org/10.3390/s22239375 - 01 Dec 2022
Cited by 2 | Viewed by 1967
Abstract
In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles. In practice, the ground plane is dynamically changed due to braking and unstable road surface. As a result, the vehicle pose, especially the pitch angle, is oscillating [...] Read more.
In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles. In practice, the ground plane is dynamically changed due to braking and unstable road surface. As a result, the vehicle pose, especially the pitch angle, is oscillating from subtle to obvious. Thus, estimating ground plane normal is meaningful since it can be encoded to improve the robustness of various autonomous driving tasks (e.g., 3D object detection, road surface reconstruction, and trajectory planning). Our proposed method only uses odometry as input and estimates accurate ground plane normal vectors in real time. Particularly, it fully utilizes the underlying connection between the ego pose odometry (ego-motion) and its nearby ground plane. Built on that, an Invariant Extended Kalman Filter (IEKF) is designed to estimate the normal vector in the sensor’s coordinate. Thus, our proposed method is simple yet efficient and supports both camera- and inertial-based odometry algorithms. Its usability and the marked improvement of robustness are validated through multiple experiments on public datasets. For instance, we achieve state-of-the-art accuracy on KITTI dataset with the estimated vector error of 0.39°. Full article
(This article belongs to the Special Issue Scene Understanding for Autonomous Driving)
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17 pages, 3921 KiB  
Article
SiamOT: An Improved Siamese Network with Online Training for Visual Tracking
by Xiaomei Gong, Yuxin Zhou and Yi Zhang
Sensors 2022, 22(17), 6597; https://doi.org/10.3390/s22176597 - 01 Sep 2022
Viewed by 1317
Abstract
As a prevailing solution for visual tracking, Siamese networks manifest high performance via convolution neural networks and weight-sharing schemes. Most existing Siamese networks have adopted various offline training strategies to realize precise tracking by comparing the extracted target features with template features. However, [...] Read more.
As a prevailing solution for visual tracking, Siamese networks manifest high performance via convolution neural networks and weight-sharing schemes. Most existing Siamese networks have adopted various offline training strategies to realize precise tracking by comparing the extracted target features with template features. However, their performances may degrade when dealing with unknown targets. The tracker is unable to learn background information through offline training, and it is susceptible to background interference, which finally leads to tracking failure. In this paper, we propose a twin-branch architecture (dubbed SiamOT) to mitigate the above problem in existing Siamese networks, wherein one branch is a classical Siamese network, and the other branch is an online training branch. Especially, the proposed online branch utilizes feature fusion and attention mechanism, which is able to capture and update both the target and the background information so as to refine the description of the target. Extensive experiments have been carried out on three mainstream benchmarks, along with an ablation study, to validate the effectiveness of SiamOT. It turns out that SiamOT achieves superior performance with stronger target discrimination abilities. Full article
(This article belongs to the Special Issue Scene Understanding for Autonomous Driving)
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