Real-Time LIDAR-Based Urban Road and Sidewalk Detection for Autonomous Vehicles

Horváth, Ernő; Pozna, Claudiu; Unger, Miklós

doi:10.3390/s22010194

Cited by 17 publications

(6 citation statements)

References 42 publications

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“…Recently, Light Detection and Ranging (LiDAR) technology has been increasingly used to create infrastructure inventory. Horváth, Pozna [ 15 ] detected sidewalk edge in real time using point clouds collected by autonomous driving cars. Hou and Ai [ 14 ] extracted sidewalks through segmenting point clouds using deep neural networks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A significant portion of the work in this domain relied on field observations [ 5 , 6 ] and street-view photo-based analysis [ 7 ]. Remote sensing technologies enabled the utilization of satellite images and point-cloud data for digitizing pedestrian infrastructure [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Among these technologies, vehicular mapping systems such as mobile LiDAR have recently gained increased attention from the transportation community due to their capability to collect accurate and dense urban point-cloud sets at street level.…”

Section: Introductionmentioning

confidence: 99%

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Pedestrian-Accessible Infrastructure Inventory: Enabling and Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types

Xia,

Gong,

Liu

et al. 2024

J. Imaging

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In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure segmentation workflow is designed and optimized, which is capable of efficiently processing multi-sourced geospatial data, including LiDAR data and satellite imagery data. We used an expanded definition of pedestrian infrastructure inventory, which goes beyond the traditional transportation elements to include street furniture objects that are important for accessibility but are often omitted from the traditional definition. Our contributions lie in producing the necessary knowledge to answer the following three questions. First, how can mobile LiDAR technology be leveraged to produce comprehensive pedestrian-accessible infrastructure inventory? Second, which data representation can facilitate zero-shot segmentation of infrastructure objects with SAM? Third, how well does the SAM-based method perform on segmenting pedestrian infrastructure objects? Our proposed method is designed to efficiently create pedestrian-accessible infrastructure inventory through the zero-shot segmentation of multi-sourced geospatial datasets. Through addressing three research questions, we show how the multi-mode data should be prepared, what data representation works best for what asset features, and how SAM performs on these data presentations. Our findings indicate that street-view images generated from mobile LiDAR point-cloud data, when paired with satellite imagery data, can work efficiently with SAM to create a scalable pedestrian infrastructure inventory approach with immediate benefits to GIS professionals, city managers, transportation owners, and walkers, especially those with travel-limiting disabilities, such as individuals who are blind, have low vision, or experience mobility disabilities.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pedestrian-Accessible Infrastructure Inventory: Enabling and Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types

Xia,

Gong,

Liu

et al. 2024

J. Imaging

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“…Urban Road Filter [46] tries to detect the road by applying three different techniques to LiDAR pointclouds. The three different techniques differ in the way they divide the pointcloud, by channels, by beams and by sliding windows.…”

Section: Multi-cue [38]mentioning

confidence: 99%

Recent Developments on Drivable Area Estimation: A Survey and a Functional Analysis

Hortelano,

Villagrá,

Godoy

et al. 2023

Sensors

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Most advanced autonomous driving systems (ADS) today rely on the prior creation of high-definition maps (HD maps). This process is expensive and needs to be performed frequently to keep up with the changing conditions of the road environment. Creating accurate navigation maps online is an alternative to reduce the cost and broaden the current operational design domains (ODD) of modern ADS. This paper offers a snapshot of the state of the art in drivable area estimation, which is an essential technology to deploy ADS in ODDs where HD maps are limited or unavailable. The proposed review introduces a novel architecture breakdown that fits learning-based and non-learning-based techniques and allows the analysis of a set of impactful and recent drivable area algorithms. In addition to that, complimentary information for practitioners is provided: (i) an assessment of the influence of modern sensing technologies on the task under study and (ii) a selection of relevant datasets for evaluation and benchmarking purposes.

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“…Curb detection using LiDAR can be classified into manual feature methods and learning based methods [13]. Manual design feature methods [14], [15] typically analyze geometric relationships such as height and angle changes between adjacent points, given the difference between drivable roads and curbs [16], [17].…”

Section: Introductionmentioning

confidence: 99%

SVASeg: Sparse Voxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation

et al. 2022

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3D LiDAR has become an indispensable sensor in autonomous driving vehicles. In LiDAR-based 3D point cloud semantic segmentation, most voxel-based 3D segmentors cannot efficiently capture large amounts of context information, resulting in limited receptive fields and limiting their performance. To address this problem, a sparse voxel-based attention network is introduced for 3D LiDAR point cloud semantic segmentation, termed SVASeg, which captures large amounts of context information between voxels through sparse voxel-based multi-head attention (SMHA). The traditional multi-head attention cannot directly be applied to the non-empty sparse voxels. To this end, a hash table is built according to the incrementation of voxel coordinates to lookup the non-empty neighboring voxels of each sparse voxel. Then, the sparse voxels are grouped into different groups, and each group corresponds to a local region. Afterwards, position embedding, multi-head attention and feature fusion are performed for each group to capture and aggregate the context information. Based on the SMHA module, the SVASeg can directly operate on the non-empty voxels, maintaining a comparable computational overhead to the convolutional method. Extensive experimental results on the SemanticKITTI and nuScenes datasets show the superiority of SVASeg.

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Real-Time LIDAR-Based Urban Road and Sidewalk Detection for Autonomous Vehicles

Cited by 17 publications

References 42 publications

Pedestrian-Accessible Infrastructure Inventory: Enabling and Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types

Pedestrian-Accessible Infrastructure Inventory: Enabling and Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types

Recent Developments on Drivable Area Estimation: A Survey and a Functional Analysis

SVASeg: Sparse Voxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation

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