Updating high-definition maps is imperative for the safety of autonomous vehicles.However,positional changes in lane lines are hard to be detected in a timely manner due to a limited number of expensive surveying vehi...Updating high-definition maps is imperative for the safety of autonomous vehicles.However,positional changes in lane lines are hard to be detected in a timely manner due to a limited number of expensive surveying vehicles over a large geo-graphic area.Herein,a novel method is proposed to detect the geometric changes of lane lines using low-cost sensors,such as consumer-grade global navigation satellite system(GNSS)hardware receivers and cameras.The proposed framework geometric change detection using low-cost sensors(GCD-L)and algorithm change segment compare(CSC),which are based on the lane width between the curb line and the adjacent leftmost lane line,can perceive the positional changes of the leftmost lane line on highway and expressway roads.The effectiveness of the proposed method is verified by evaluating it on a real-world typical urban ring road dataset.The experimental results show that 71%detected change segments are valid with only two round crowdsourced maps.展开更多
High-definition(HD)maps are key components that provide rich topologic and semantic information for decision-making in vehicle autonomous driving systems.A complete ground orthophoto is usually used as the base image ...High-definition(HD)maps are key components that provide rich topologic and semantic information for decision-making in vehicle autonomous driving systems.A complete ground orthophoto is usually used as the base image to construct the HD map.The ground orthophoto is obtained through inverse perspective transformation and image mosaicing.During the image mosaicing,multiple consecutive orthophotos are stitched together using pose information and image registration.In this study,wavelet transform is introduced to the image mosaicing process to alleviate the information loss caused by image overlapping.In the orthophoto wavelet transform,high-frequency and low-frequency components are fused using different strategies to form a complete base image with clearer local details.Experimental results show that the accuracy of the orthophotos generated using this method is improved.展开更多
With the growth in the vehicle industry,autonomous driving has become a hot topic worldwide and has attracted increasing attention from both industrial and academic sectors.Maps,as pivotal geospatial information carri...With the growth in the vehicle industry,autonomous driving has become a hot topic worldwide and has attracted increasing attention from both industrial and academic sectors.Maps,as pivotal geospatial information carriers,play a vital role in route planning and navigation service.Compared with conventional maps,high-definition(HD)maps possesses higher precision,richer information,and various services and are regarded as critical infrastructure for autonomous driving.However,heterogeneous HD map data standards and models have different characteristics and advantages,and thus they rarely meet all autonomous driving requirements for different driving objectives.This research presents an interoperable map data model,the Open HD Map Service Model(OHDMSM),to provide a reference for HD map development.The designed OHDMSM,which contains three data layers and a set of corresponding interfaces,demonstrates high interoperability for HD map data fusion and application.As a proof of concept,an HD map data system is implemented with all functions following the designed data model and interfaces of OHDMSM.The design and development of OHDMSM data structures,interfaces and systems will benefit data requesting,updating,and interoperation for HD map data worldwide,which can be helpful for developing autonomous driving and intelligent transportation in the Digital Earth.展开更多
Lane detection is essential for many aspects of autonomous driving,such as lane-based navigation and high-definition(HD)map modeling.Although lane detection is challenging especially with complex road conditions,consi...Lane detection is essential for many aspects of autonomous driving,such as lane-based navigation and high-definition(HD)map modeling.Although lane detection is challenging especially with complex road conditions,considerable progress has been witnessed in this area in the past several years.In this survey,we review recent visual-based lane detection datasets and methods.For datasets,we categorize them by annotations,provide detailed descriptions for each category,and show comparisons among them.For methods,we focus on methods based on deep learning and organize them in terms of their detection targets.Moreover,we introduce a new dataset with more detailed annotations for HD map modeling,a new direction for lane detection that is applicable to autonomous driving in complex road conditions,a deep neural network LineNet for lane detection,and show its application to HD map modeling.展开更多
An accurate and up-to-date High Definition(HD)Map is critical for an intelligent vehicle to drive safely and effectively.Although research in this area is growing,there is still a lack of clarity in defining HD maps f...An accurate and up-to-date High Definition(HD)Map is critical for an intelligent vehicle to drive safely and effectively.Although research in this area is growing,there is still a lack of clarity in defining HD maps for intelligent connected vehicles(ICVs).This gap in knowledge is particularly challenging for new researchers,who often struggle to find suitable HD map datasets due to a lack of comprehensive reviews on current HD map products,as far as the authors’knowledge.Thus,this article aims to bridge this gap by providing a thorough analysis of the core ideas of HD map technology.Initially,this paper presents the brief history of HD map.Following this,it describes the taxonomy and ontology of HD maps,complete with the HD map contents and existing standards.An insight into the mapping process is also given by discussing the algorithms used for creating and updating HD maps.This manuscript also lists current HD map products and the open-sourced dataset available for interested researchers in this space.As part of this study,the authors also describe common applications of HAD maps in ICVs.Finally,the article highlight the key research challenges and potential future directions in this field.Addressing these challenges is vital for the advancement and integration of HD maps for ICVs.展开更多
基金sponsored by the National Natural Science Foundation of China-52102426,U1864203 and 61773234the Project Funded by China Postdoctoral Science Foundation-2019M660622.
文摘Updating high-definition maps is imperative for the safety of autonomous vehicles.However,positional changes in lane lines are hard to be detected in a timely manner due to a limited number of expensive surveying vehicles over a large geo-graphic area.Herein,a novel method is proposed to detect the geometric changes of lane lines using low-cost sensors,such as consumer-grade global navigation satellite system(GNSS)hardware receivers and cameras.The proposed framework geometric change detection using low-cost sensors(GCD-L)and algorithm change segment compare(CSC),which are based on the lane width between the curb line and the adjacent leftmost lane line,can perceive the positional changes of the leftmost lane line on highway and expressway roads.The effectiveness of the proposed method is verified by evaluating it on a real-world typical urban ring road dataset.The experimental results show that 71%detected change segments are valid with only two round crowdsourced maps.
基金the National Natural Science Foundation of China(No.U1764264/61873165)the Shanghai Automotive Industry Science and Technology Development Foundation(No.1807)the Guangxi Key Laboratory of Automobile Components and Vehicle Technology Research Project(No.2020GKLACVTKF02)。
文摘High-definition(HD)maps are key components that provide rich topologic and semantic information for decision-making in vehicle autonomous driving systems.A complete ground orthophoto is usually used as the base image to construct the HD map.The ground orthophoto is obtained through inverse perspective transformation and image mosaicing.During the image mosaicing,multiple consecutive orthophotos are stitched together using pose information and image registration.In this study,wavelet transform is introduced to the image mosaicing process to alleviate the information loss caused by image overlapping.In the orthophoto wavelet transform,high-frequency and low-frequency components are fused using different strategies to form a complete base image with clearer local details.Experimental results show that the accuracy of the orthophotos generated using this method is improved.
基金supported by National Key Research and Development Program of China:[Grant Number 2021YFB2501101]Smart Cities Research Institute(Q-CDA7)at the Hong Kong Polytechnic University:[Grant Number Q-CDA7]Guangdong Science and Technology Strategic Innovation Fund(the Guangdong–Hong Kong-Macao Joint Laboratory Program):Guangdong Science and Technology Strategic Innovation Fund:[Grant Number 2020B12120300092020B1212030009].
文摘With the growth in the vehicle industry,autonomous driving has become a hot topic worldwide and has attracted increasing attention from both industrial and academic sectors.Maps,as pivotal geospatial information carriers,play a vital role in route planning and navigation service.Compared with conventional maps,high-definition(HD)maps possesses higher precision,richer information,and various services and are regarded as critical infrastructure for autonomous driving.However,heterogeneous HD map data standards and models have different characteristics and advantages,and thus they rarely meet all autonomous driving requirements for different driving objectives.This research presents an interoperable map data model,the Open HD Map Service Model(OHDMSM),to provide a reference for HD map development.The designed OHDMSM,which contains three data layers and a set of corresponding interfaces,demonstrates high interoperability for HD map data fusion and application.As a proof of concept,an HD map data system is implemented with all functions following the designed data model and interfaces of OHDMSM.The design and development of OHDMSM data structures,interfaces and systems will benefit data requesting,updating,and interoperation for HD map data worldwide,which can be helpful for developing autonomous driving and intelligent transportation in the Digital Earth.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.61902210 and 61521002a research grant from the Beijing Higher Institution Engineering Research Center,and the Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology.
文摘Lane detection is essential for many aspects of autonomous driving,such as lane-based navigation and high-definition(HD)map modeling.Although lane detection is challenging especially with complex road conditions,considerable progress has been witnessed in this area in the past several years.In this survey,we review recent visual-based lane detection datasets and methods.For datasets,we categorize them by annotations,provide detailed descriptions for each category,and show comparisons among them.For methods,we focus on methods based on deep learning and organize them in terms of their detection targets.Moreover,we introduce a new dataset with more detailed annotations for HD map modeling,a new direction for lane detection that is applicable to autonomous driving in complex road conditions,a deep neural network LineNet for lane detection,and show its application to HD map modeling.
基金National Natural Science Foundation of China(U22A20104,52102464)Beijing Natural Science Foundation(L231008)Young Elite Scientist Sponsorship Program By BAST(BYESS2022153).
文摘An accurate and up-to-date High Definition(HD)Map is critical for an intelligent vehicle to drive safely and effectively.Although research in this area is growing,there is still a lack of clarity in defining HD maps for intelligent connected vehicles(ICVs).This gap in knowledge is particularly challenging for new researchers,who often struggle to find suitable HD map datasets due to a lack of comprehensive reviews on current HD map products,as far as the authors’knowledge.Thus,this article aims to bridge this gap by providing a thorough analysis of the core ideas of HD map technology.Initially,this paper presents the brief history of HD map.Following this,it describes the taxonomy and ontology of HD maps,complete with the HD map contents and existing standards.An insight into the mapping process is also given by discussing the algorithms used for creating and updating HD maps.This manuscript also lists current HD map products and the open-sourced dataset available for interested researchers in this space.As part of this study,the authors also describe common applications of HAD maps in ICVs.Finally,the article highlight the key research challenges and potential future directions in this field.Addressing these challenges is vital for the advancement and integration of HD maps for ICVs.
