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Applications of Remote Sensing and GIS to Solve Challenges in Autonomous Driving and Analyze Roadway Safety

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (16 May 2023) | Viewed by 15467

Special Issue Editors

Dr. Mohammad Aldibaja

E-Mail Website
Guest Editor
Sensing and Perception, SMART Mechatronics Research Group, Saxion University of Applied Sciences, Enschede, The Netherlands
Interests: autonomous vehicles; LIDAR/radar-based localization systems; mapping systems; SLAM technologies; eye-based human‒machine interface systems; driver monitoring systems
Special Issues, Collections and Topics in MDPI journals
Dr. Zheng Liu

E-Mail Website
Guest Editor
School of Engineering, University of British Columbia, Okanagan, Kelowna, BC V1V 1V7, Canada
Interests: intelligent sensing, measurement, and instrumentation; diagnostics, prognostics, and health management; predictive maintenance; digital twins; computational intelligence and data/information fusion; non-destructive testing and evaluation; machine/computer vision; data analytics and machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Hanbing Wei

E-Mail Website
Guest Editor
School of Mechatronics and Automotive Engineering, Chongqing Jiaotong University, Chongqing 400074, China
Interests: computational intelligence; machine learning; unsupervised learning; computer vision

Special Issue Information

Dear Colleagues,

In the light of the rapid integration of advanced driver assistance systems (ADAS) in consumer vehicles, the public has become more familiar with the concept of autonomous vehicles. This has motivated companies and researchers to develop robust techniques and demonstrate autonomous driving at a faster pace. As safety is the prime priority and the key issue in commercializing autonomous vehicles, the main challenge in the research field has become to solve the critical and unique problems such as precise localization in snow–rain road conditions, generating accurate and large maps by SLAM technologies, far detection of construction areas for smooth path planning, maneuvering with the existence of unprotected turns, making a robust decision on classifying stationary vehicles as obstacles or temporarily stopped due to traffic jams and traffic signal recognition in sun glare. Otherwise, autonomous driving will stay in the demonstration loop, and the deployment of autonomous vehicles will be limited to certain operating conditions. In addition, these problems may lead to deadly traffic accidents and produce a considerable negative impact on societies to accept running autonomous vehicles in streets.

Analyzing the reasons for these problems and clearly illustrating them are the cornerstone to investigating the relevant effects on the autopilot’s performance and proposing the corresponding optimal solutions. Remote sensing and image processing applications play the main role in designing optimal solutions based on sensory and observation data such as modeling the changes in the pattern distribution of LIDAR 3D point clouds in snowfall weather conditions and improving the localization accuracy by matching map observation environmental features. Therefore, this Special Issue aims to add value to the autonomous vehicle research field by demonstrating and analyzing critical and unique problems of mapping, localization, perception and path-planning modules that are rarely discussed in the literature and currently considered as futuristic matters.

Eventually, we hope to significantly contribute to increasing the safety of autonomous driving and provide prominent and robust solutions through the published papers

Dr. Mohammad Aldibaja
Dr. Zheng Liu
Dr. Hanbing Wei
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. Remote Sensing 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 2700 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.

Keywords

  • autonomous vehicles
  • 3D point cloud analysis
  • path planning with unprotected turns
  • robust perception of construction areas
  • SLAM-based mapping in challenging environments
  • road pavement assessment for driving safety analysis
  • object status classification in urban traffic conditions
  • LIDAR/radar-based localization systems in adverse weather conditions
  • map quality analysis and enhancement

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Published Papers (5 papers)

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Research

29 pages, 9683 KiB  
Article
HDM-RRT: A Fast HD-Map-Guided Motion Planning Algorithm for Autonomous Driving in the Campus Environment
by Xiaomin Guo, Yongxing Cao, Jian Zhou, Yuanxian Huang and Bijun Li
Remote Sens. 2023, 15(2), 487; https://doi.org/10.3390/rs15020487 - 13 Jan 2023
Cited by 11 | Viewed by 3249
Abstract
On campus, the complexity of the environment and the lack of regulatory constraints make it difficult to model the environment, resulting in less efficient motion planning algorithms. To solve this problem, HD-Map-guided sampling-based motion planning is a feasible research direction. We proposed a [...] Read more.
On campus, the complexity of the environment and the lack of regulatory constraints make it difficult to model the environment, resulting in less efficient motion planning algorithms. To solve this problem, HD-Map-guided sampling-based motion planning is a feasible research direction. We proposed a motion planning algorithm for autonomous vehicles on campus, called HD-Map-guided rapidly-exploring random tree (HDM-RRT). In our algorithm, A collision risk map (CR-Map) that quantifies the collision risk coefficient on the road is combined with the Gaussian distribution for sampling to improve the efficiency of algorithm. Then, the node optimization strategy of the algorithm is deeply optimized through the prior information of the CR-Map to improve the convergence rate and solve the problem of poor stability in campus environments. Three experiments were designed to verify the efficiency and stability of our approach. The results show that the sampling efficiency of our algorithm is four times higher than that of the Gaussian distribution method. The average convergence rate of the proposed algorithm outperforms the RRT* algorithm and DT-RRT* algorithm. In terms of algorithm efficiency, the average computation time of the proposed algorithm is only 15.98 ms, which is much better than that of the three compared algorithms. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS to Solve Challenges in Autonomous Driving and Analyze Roadway Safety)
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18 pages, 1641 KiB  
Article
AgentI2P: Optimizing Image-to-Point Cloud Registration via Behaviour Cloning and Reinforcement Learning
by Shen Yan, Maojun Zhang, Yang Peng, Yu Liu and Hanlin Tan
Remote Sens. 2022, 14(24), 6301; https://doi.org/10.3390/rs14246301 - 12 Dec 2022
Cited by 3 | Viewed by 2394
Abstract
Image-to-point cloud registration refers to finding relative transformation between the camera and the reference frame of the 3D point cloud, which is critical for autonomous driving. Recently, a two-stage “frustum point cloud classification + camera pose optimization” pipeline has shown impressive results on [...] Read more.
Image-to-point cloud registration refers to finding relative transformation between the camera and the reference frame of the 3D point cloud, which is critical for autonomous driving. Recently, a two-stage “frustum point cloud classification + camera pose optimization” pipeline has shown impressive results on this task. This paper focuses on the second stage and reformulates the optimization procedure as a Markov decision process. An initial pose is modified incrementally, sequentially aligning a virtual 3D point observation towards a previous classification solution. We consider such an iterative update process as a reinforcement learning task and, to this end, propose a novel agent (AgentI2P) to conduct decision making. To guide AgentI2P, we employ behaviour cloning (BC) and reinforcement learning (RL) techniques: cloning an expert to learn accurate pose movement and reinforcing an alignment reward to improve the policy further. [We demonstrate the effectiveness and efficiency of our approach on Oxford Robotcar and KITTI datasets. The (RTE, RRE) metrics are (1.34m,1.46∘) on Oxford Robotcar and (3.90m,5.94∘) on KITTI, and the inference time is 60 ms, both achieving state-of-the-art performance]. The source code will be publicly available upon publication of the paper. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS to Solve Challenges in Autonomous Driving and Analyze Roadway Safety)
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18 pages, 12087 KiB  
Article
2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments
by Mohammad Aldibaja, Naoki Suganuma and Ryo Yanase
Remote Sens. 2022, 14(22), 5847; https://doi.org/10.3390/rs14225847 - 18 Nov 2022
Cited by 4 | Viewed by 3089
Abstract
This paper proposes a reliable framework to map multilevel road structures in the 2D image domain called layered sub-image maps (LSM). The road is divided into a set of sub-areas providing IDs in the XY plane. Each sub-area is decomposed into several layered [...] Read more.
This paper proposes a reliable framework to map multilevel road structures in the 2D image domain called layered sub-image maps (LSM). The road is divided into a set of sub-areas providing IDs in the XY plane. Each sub-area is decomposed into several layered images using LIDAR intensity and elevation data to form a 2.5D map image. The layered elevation images are given IDs in the Z plane to represent the height of the contained road features in meter-order whereas the elevation pixels indicate the cm-order of the road slope in the range of 200 cm. The layered intensity images are then created to describe the road surface in conjunction with the number of the layered elevation images and the corresponding pixel distributions. A significant map retrieval strategy during autonomous driving has been designed based on the LSM implementation tactic and the IDs in the XYZ plane. The system’s reliability has been proved by a unique localization module to localize an autonomous vehicle in a challenging multilevel environment consisting of four stacked loops with an average accuracy of 5 cm in lateral, longitudinal and altitudinal directions. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS to Solve Challenges in Autonomous Driving and Analyze Roadway Safety)
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19 pages, 17511 KiB  
Article
Challenging Environments for Precise Mapping Using GNSS/INS-RTK Systems: Reasons and Analysis
by Mohammad Aldibaja, Naoki Suganuma, Keisuke Yoneda and Ryo Yanase
Remote Sens. 2022, 14(16), 4058; https://doi.org/10.3390/rs14164058 - 19 Aug 2022
Cited by 10 | Viewed by 2219
Abstract
This paper demonstrates the weakness of GNSS/INS-RTK (GIR) systems in mapping challenging environments because of obstruction and deflection of satellite signals. Thus, it emphasizes that the strategy of mapping companies to commercially provide maps using expensive GIR systems does not always work robustly. [...] Read more.
This paper demonstrates the weakness of GNSS/INS-RTK (GIR) systems in mapping challenging environments because of obstruction and deflection of satellite signals. Thus, it emphasizes that the strategy of mapping companies to commercially provide maps using expensive GIR systems does not always work robustly. This limits the scalability of autonomous vehicle deployment in many road structures and modern cities. Accordingly, different critical environments in Tokyo have been analyzed and investigated to demonstrate the effects of the road structure complexity on the GIR map quality with highlighting the relevant reasons. Therefore, this paper is intended to be a reference to prove that the data of GIR systems cannot always be considered as ground truth and the integration of SLAM technologies into the mapping modules is very necessary to enable the levels four and five of autonomous driving. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS to Solve Challenges in Autonomous Driving and Analyze Roadway Safety)
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19 pages, 2819 KiB  
Article
Semantic Segmentation of Panoramic Images for Real-Time Parking Slot Detection
by Cong Lai, Qingyu Yang, Yixin Guo, Fujun Bai and Hongbin Sun
Remote Sens. 2022, 14(16), 3874; https://doi.org/10.3390/rs14163874 - 10 Aug 2022
Cited by 13 | Viewed by 2923
Abstract
Autonomous parking is an active field of automatic driving in both industry and academia. Parking slot detection (PSD) based on a panoramic image can effectively improve the perception of a parking space and the surrounding environment, which enhances the convenience and safety of [...] Read more.
Autonomous parking is an active field of automatic driving in both industry and academia. Parking slot detection (PSD) based on a panoramic image can effectively improve the perception of a parking space and the surrounding environment, which enhances the convenience and safety of parking. The challenge of PSD implementation is identifying the parking slot in real-time based on images obtained from the around view monitoring (AVM) system, while maintaining high recognition accuracy. This paper proposes a real-time parking slot detection (RPSD) network based on semantic segmentation, which implements real-time parking slot detection on the panoramic surround view (PSV) dataset and avoids the constraint conditions of parking slots. The structural advantages of the proposed network achieve real-time semantic segmentation while effectively improving the detection accuracy of the PSV dataset. The cascade structure reduces the operating parameters of the whole network, ensuring real-time performance, and the fusion of coarse and detailed features extracted from the upper and lower layers improves segmentation accuracy. The experimental results show that the final mIoU of this work is 67.97% and the speed is up to 32.69 fps, which achieves state-of-the-art performance with the PSV dataset. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and GIS to Solve Challenges in Autonomous Driving and Analyze Roadway Safety)
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