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road Boundary Detection Using Multi-channel LiDAR Based on Disassemble-reassemble-merge Algorithm for Autonomous Driving

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2023年第11期21卷 3724-3733页

作者： Kim, Eui-Young Pae, Dong-Sung Lim, Myo-Taeg Yujin Robot 33 Harmony Ro187beonGil Incheon 22013 South Korea Sangmyung Univ Dept Software 31 Sangmyungdae Gil Cheonan 31066 Chungnam South Korea Korea Univ Sch Elect Engn 145 Anam Ro Seoul 02841 South Korea

To ensure the safe operation of self-driving cars, it is necessary to identify the location and layout of the road around the vehicle. Moreover, because road accidents pose a considerable risk to human life, accurate environmental awareness is paramount in autonomous driving, especially under unfavorable ambient conditions. Various sensors have been employed to realize road area recognition under such conditions. Among these sensors, multi-channel LiDAR has recently attracted attention as it is robust against illumination changes and can obtain accurate distance information. However, this system requires a large amount of data, rendering real-time processing difficult. To overcome this limitation, feature extraction methods have been used that can extract meaningful implicit information from a large amount of data. However, the application of feature extraction to small areas of the road for real-time processing can result in reduced accuracy. To enhance the level of accuracy, an algorithm was adopted in this study that can apply feature extraction to large areas of the road. This algorithm disassembles the LiDAR data for prompt processing at a low level and then reassembles the data to manage the exceptional parts in each scan line. Subsequently, the algorithm merges the road area parts in each scan line and distinguishes the road areas, sidewalks, and obstacles in real-time. To realize these three computational processes, the algorithm is termed a disassemble-reassemble-merge (DRM) algorithm. The DRM algorithm is unique as it only uses the LiDAR sensor, which differs from existing algorithms that employ LiDAR sensors for road boundary detection. To demonstrate the effectiveness of the proposed method, we conducted an experiment using the KITTI road Benchmark authorized dataset. Using the proposed method, accuracy was 10% higher compared to other algorithms. To validate the developed algorithm and its utility, we applied it to the Complex Yolo and obstacle-dependen

关键词： DRM multi-channel LiDAR risk map road boundary road segmentation

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Expressway rear-end crash risk evolution mechanism analysis under different traffic states

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TRANSPORTMETRICA B-TRANSPORT DYNAMICS 2023年第1期11卷 510-527页

作者： Wang, Ling Zou, Lingjie Abdel-Aty, Mohamed Ma, Wanjing Tongji Univ Minist Educ Key Lab Rd & Traff Engn 4800 Caoan Rd Shanghai 201804 Peoples R China Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA

The proportion of rear-end crashes is the highest for expressways. An effective ways to reduce the rear-end crash risk is Active Traffic Management (ATM), and knowing the mechanism of how contributing factors affect crash risk in space and time is the foundation of ATM. However, the existing studies are mainly based on highly aggregated traffic data. It is hard to capture the evolution mechanisms of crash risk. Meanwhile, crash risk mechanisms might be heterogeneous in smooth and congestion states. This study explored the crash mechanisms in different traffic states with high-resolution trajectory data. First, an ordered clustering method is used to divide a four km expressway section into several segments as the spatial unit. Second, the spatial-temporal ranges are decided by the spatial-temporal correlations analyses between crash risk and potential contributing factors. Thirdly, three types of time-series models are established to quantitatively obtain the impacts of contributing factors on the crash risks. The results showed that crash risk is mainly decided by the upstream contributing factors under smooth states, and determined by the downstream factors under congestion conditions.

关键词： Traffic crash road segmentation time-series analysis crash risk evolution mechanism

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SuP-SLiP: Subsampled Processing of Large-scale Static LIDAR Point Clouds 3

SuP-SLiP: Subsampled Processing of Large-scale Static LIDAR ...

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3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data (GeoSearch)

作者： Jaisankar, Vijay Sreevalsan-Nair, Jaya Int Inst Informat Technol Bangalore Graph Visualizat Comp Lab Bangalore Karnataka India

ISBN: (纸本)9798400711480

Annotation is a crucial component of point cloud analysis. However, due to the sheer number of points in large-scale static point clouds, it is an expensive and time-consuming process. We address this issue using a novel lightweight approach to reduce the overall annotation time for unlabelled static point clouds in the binary road segmentation task. By leveraging models trained on other point clouds in the same distribution, and radius sampling, our approach determines a small fraction of points for annotation. It automatically labels the remaining points using nearest-neighbor aggregation. We implement this approach in an end-to-end system for mobile laser scanning (MLS) or mobile LiDAR point clouds, SuP-SLiP, i.e., Subsampled Processing of Large-scale Static LIDAR Point Clouds. We validate the robustness of this method through the bit-flipping adversarial attack and account for varying budgets by providing a feature that suggests a custom number of points to annotate for a given point cloud.

关键词： Point cloud processing Nearest neighbors Statistical analysis Annotation Adversarial attack Bit flipping Binary classification road segmentation LIDAR point clouds

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Marked and unmarked speed bump detection for autonomous vehicles using stereo vision

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第5期42卷 4685-4697页

作者： Luisa Ballinas-Hernandez, Ana Olmos-Pineda, Ivan Arturo Olvera-Lopez, Jose Benemerita Univ Autonoma Puebla Fac Comp Sci Puebla Mexico

A current challenge for autonomous vehicles is the detection of irregularities on road surfaces in order to prevent accidents;in particular, speed bump detection is an important task for safe and comfortable autonomous navigation. There are some techniques that have achieved acceptable speed bump detection under optimal road surface conditions, especially when signs are well-marked. However, in developing countries it is very common to find unmarked speed bumps and existing techniques fail. In this paper a methodology to detect both marked and unmarked speed bumps is proposed, for clearly painted speed bumps we apply local binary patterns technique to extract features from an image dataset. For unmarked speed bump detection, we apply stereo vision where point clouds obtained by the 3D reconstruction are converted to triangular meshes by applying Delaunay triangulation. A selection and extraction of the most relevant features is made to speed bump elevation on surfaces meshes. Results obtained have an important contribution and improve some of the existing techniques since the reconstruction of three-dimensional meshes provides relevant information for the detection of speed bumps by elevations on surfaces even though they are not marked.

关键词： Speed bump detection road segmentation stereo vision triangular surface meshes machine learning

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Multi-objective modelling of a roadside mowing problem: a case study in France 28

Multi-objective modelling of a roadside mowing problem: a ca...

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Joint Conference of the IEEE 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) / 31st Conference of the International-Association-for-Management-of-Technology (IAMOT)

作者： Mihaita, Adriana-Simona Marche, Brunelle Camargo, Mauricio Rahimi, Iman Bachmann, Chirstophe Univ Technol Sydney Sydney NSW Australia Univ Lorraine ERPI F-54000 Nancy France NOREMAT SA F-54710 Ludres France

ISBN: (纸本)9781665488174

roadside maintenance is a significant challenge for many territories worldwide. For safety reasons, these roadsides have to be maintained regularly, which has a substantial economic impact requiring a significant investment of staff and material. This paper addresses the problem of planning optimal trajectories of mowing machines within a dedicated territory in order to meet several objective criteria such as: minimising the travel distance, the number of mowing machines, as well as minimising the time and the cost associated with each yearly operation. Firstly, we define the mathematical methodology of the multi-objective criteria that we need to calculate. Secondly, we propose a clustering approach based on the k-means algorithm in order to identify the best sub-sectors of road sections that each technical centre needs to plan in order and maintain using existing equipment. Thirdly, we apply an optimal routing between each cluster based on TomTom API routing and identify the distance used to formulate our multi-objective optimisation criteria to minimise the cost and the use time associated with various mowing scenarios. Finally, we analyse the best setup to satisfy all criteria in the current case study area and present future projections of a local versus global optimisation approach.

关键词： Mowing multi-objective road maintenance k-means road segmentation optimal routing

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Optimizing Geospatial Data for ML/CV Applications: A Python-Based Approach to Streamlining Map Processing by Removing Irrelevant Areas

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APPLIED SCIENCES-BASEL 2024年第24期14卷 11978-11978页

作者： Kasperek, David Podpora, Michal Opole Univ Technol Dept Comp Sci Proszkowska 76 PL-45758 Opole Poland

Massive image datasets are often required for the proper functioning of Machine Learning (ML) and Computer Vision (CV) applications. This paper offers a solution to computational challenges in the Image Processing of satellite imagery, by proposing an optimization procedure. The presented approach is verified by an exemplary Python implementation, constituting a standalone tool for automating the dataset creation and labeling, including the extraction of road network data from the national satellite cartography provider. The collected data include detailed road maps along with the parcel information obtained via WebMapService endpoints. The method presented in this paper involves three basic steps: road segmentation (using the Shapely module) to facilitate handling high-resolution orthoimagery, and then a modified Region-of-Interest approach, i.e., removing irrelevant areas, with only roads remaining. This results in obtaining file sizes that are significantly smaller. The presented algorithm also involves asynchronous tile downloading, which, combined with the masking of irrelevant areas, improves not only the efficiency but surprisingly also the accuracy of subsequent ML/CV procedures. The research results of the paper reveal substantial file size reduction, and improved processing efficiency, thus making the optimized geospatial graphical data more practical for ML/CV applications, while still maintaining the original data quality and relevance of the analyzed parcels or infrastructure.

关键词： satellite imagery road segmentation geospatial data optimization dataset creation and labeling machine learning image processing

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Novel hawk swarm-optimized deep learning classification with K-nearest neighbor based decision making for autonomous vehicle movement controller

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第26期36卷

作者： Qingmiao, Zhang Dinghua, Zhang No Arizona Univ Comp Sci & Informat Technol POB 4084 Flagstaff AZ 86011 USA

Nowadays, intelligent transportation systems pay a lot of attention to autonomous vehicles it is believed that an autonomous vehicle improves mobility, comfort, safety, and energy efficiency. Making decisions is essential for the development of autonomous vehicles since these algorithms must be able to manage dynamic and complex urban crossings. In this research an optimal deep BiLSTM-GAN classifier to detect the movement of smart vehicles, initially the preprocessing stage is performed to decrease noise in the received data after that the essential regions are next be extracted in the region of interest (ROI) to make the right decision. The extracted data are forwarded to the GAN for road segmentation as well as the optimized deep BiLSTM classifier, which recognizes the traffic sign, simultaneously making it possible to do a modified Hough line-based maneuver prediction using the segmented information from the roads. Finally, the GAN determines the lane, and the BiLSTM predicts the traffic sign. The K-nearest neighbor (KNN)-based autonomous vehicle movement controllers are used to make the decision based on the predicted traffic sign and information about the lane. The proposed HSO algorithm was developed as the outcome of the common fusion of hawk and swarm optimization. Based on lane detecting achievements, at training percentage (TP) 90, the accuracy is 91.75%, Peak signal-to-noise ratio (PSNR) is 64.84%, mean square error (MSE) is 28.78, and mean absolute error (MAE) is 20.20, respectively, similarly based on the traffic sign prediction achievements at TP 90, the accuracy is 93.71%, sensitivity is 95.15%, specificity is 93.91%, and MSE is 28.78%, respectively.

关键词： BiLSTM GAN hawk swarm road segmentation traffic sign prediction

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Building-road Collaborative Extraction From Remote Sensing Images via Cross-Task and Cross-Scale Interaction

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Guo, Haonan Su, Xin Wu, Chen Du, Bo Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Wuhan Univ Inst Artificial Intelligence Sch Comp Sci Hubei Key Lab Multimedia & Network Commun EngnNat Wuhan 430079 Peoples R China

Buildings and roads are the two most basic man-made environments that carry and interconnect human society. Building and road information has important application value in the frontier fields of regional coordinated development, disaster prevention, and auto-driving. Mapping buildings and roads from very high-resolution (VHR) remote sensing images has become a hot research topic. However, the existing methods often extract buildings and roads with separate models, ignoring their strong spatial correlation. To fully utilize their complementary relation, we propose a method that simultaneously extracts buildings and roads from remote sensing images. The accuracy of both tasks can be improved using our proposed multitask feature interaction (MFI) and cross-scale feature interaction modules. To be specific, a multitask interaction (MTI) module is proposed to interact information across building extraction and road extraction tasks while preserving the unique information of each task. Furthermore, a cross-scale interaction (CSI) module is designed to automatically learn the optimal reception field for buildings and roads under varied appearances and structures. Compared with existing methods that train individual models for each task separately, the proposed collaborative extraction method can utilize the complementary advantages between buildings and roads and reduce the inference time by half using a single model. Experiments on a wide range of urban and rural scenarios show that the proposed algorithm can achieve building-road extraction with outstanding performance and efficiency.

关键词： roads Buildings Feature extraction Task analysis Data mining Remote sensing Correlation Building extraction deep learning multitask learning road segmentation

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APPLICATION ORIENTED QUALITY EVALUATION OF GAOFEN-7 OPTICAL STEREO SATELLITE IMAGERY 24

APPLICATION ORIENTED QUALITY EVALUATION OF GAOFEN-7 OPTICAL ...

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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Tian, J. Zhuo, X. Yuan, X. Henry, C. d'Angelo, P. Krauss, T. German Aerosp Ctr DLR Remote Sensing Technol Inst Oberpfaffenhofen Germany

GaoFen-7 (GF-7) satellite mission is further expanding the very high resolution 3D mapping application. Carrying the first civilian Chinese sub-meter resolution stereo satellite sensors, GF-7 satellite was launched on November 7, 2019. With 0.65 meter resolution on backward view and 0.8 meter resolution forward view, GF-7 has been designed to meet the demand of natural resource monitoring, land surveying, and other mapping applications in China. The use of GF-7 for 3D city reconstruction is unfortunately restricted by the fixed large stereo view angle of forward and backward cameras with +26 and -5 degrees respectively which is not optimal for dense stereo matching in urban regions. In this paper we intensively evaluate the quality of the GF-7 datasets by performing a series of urban monitoring applications, including road detection, building extraction and 3D reconstruction. In addition, we propose a 3D reconstruction workflow which uses the land cover classification result to refine the stereo matching result. Six sub-urban regions are selected from the available datasets in the middle of Germany. The results show that basic elements in urban scenes like buildings and roads could be detected from GF-7 datasets with high accuracy. With the proposed workflow, a 3D city model with a visually observed good quality can be delivered.

关键词： GF-7 Deep Learning satellite stereo imagery 3D reconstruction building segmentation road segmentation

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Efficient and de-shadowing approach for multiple vehicle tracking in aerial video via image segmentation and local region matching

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JOURNAL OF APPLIED REMOTE SENSING 2020年第1期14卷

作者： Zhang, Xunxun Zhu, Xu Xian Univ Architecture & Technol Sch Civil Engn Xian Xian Peoples R China Natl Expt Teaching Ctr Civil Engn Virtual Simulat Xian Peoples R China Changan Univ Sch Elect & Control Engn Xian Peoples R China

Multiple vehicle tracking in aerial video has vital applications in intelligent transportation system. However, this is challenging owing to the demand for higher tracking efficiency. Moreover, it is particularly challenging to handle shadows and occlusions in complex traffic scenes. Currently, most approaches mainly address the multiple vehicle tracking problem based on consecutive frame pairs. However, they are inefficient and ineffective with respect to the tracking of vehicles that are shadowed or occluded by trees, buildings, or large vehicles. Therefore, in terms of consecutive multiframe images, we propose an efficient deshadowing approach for multiple vehicle tracking in aerial video via image segmentation and local region matching. By processing a series of frames at a time, this approach will address the occurrence of shadows and occlusions in multiple vehicle tracking. First, image segmentation and road segmentation are combined to construct the region adjacency graph within the road (RAGR) for each frame. Local region matching is then performed among the RAGRs based on the distance transform. In particular, to improve the region matching efficiency, a local matching criterion is proposed by introducing region of interest for the node of the RAGR. Finally, based on the motion dissimilarities and varying spatial distances of the background and moving vehicles, a batch-oriented foreground separation is achieved to separate vehicles (the foreground) from the background. Experiments and comparative analysis conducted on UAV123 and DARPA VIVID data sets demonstrate that the proposed approach for multiple vehicle tracking in aerial video can generate satisfactory and competitive results. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： multiple vehicle tracking aerial video image segmentation road segmentation local region matching foreground separation

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