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王彬 . 基于多传感器融合的高精度地图生成方法研究 [D ] . 重庆 : 重庆大学 , 2022 : 1 - 6 .

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DIAZ-DIAZ A , OCAÑA M , LLAMAZARES Á , et al. HD maps: exploiting OpenDRIVE potential for path planning and map monitoring [C ] // Proceedings of the 2022 IEEE Intelligent Vehicles Symposium (IV). Aachen , Germany : IEEE , 2022 : 1211 - 1217 .

LIN J R , LIU X Y , ZHANG F . A decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs [C ] // Proceedings of the 2020 IEEE RSJ International Conference on Intelligent Robots and Systems. Las Vegas, NV , US : IEEE , 2020 : 4870 - 4877 .

MI L , ZHAO H , NASH C , et al. HDMapGen: A hierarchical graph generative model of high definition maps [C ] // Proceedings of the 2022 IEEE Conference on Computer Vision and Pattern Recognition. Nashville, TN , US : IEEE , 2021 : 4225 - 4234 .

王淞 . 特定场景轻量级高精地图设计 [D ] . 重庆 : 重庆邮电大学 , 2022 .

WANG S . Lightweight high-precision map for specific scenes [D ] . Chongqing : Chongqing University of Posts and Telecommunications , 2022 . (in Chinese)

吕钊凤 , 李峥嵘 . 无人驾驶,地图先行——盘点中国19家高精度地图厂商 [J ] . 智能网联汽车 , 2019 ( 4 ): 22 - 28 .

LÜ Z F , LI Z R . Unmanned driving, map first-inventory of 19 high definition map manufacturers in China [J ] . Intelligent Connected Vehicles , 2019 ( 4 ): 22 - 28 . (in Chinese)

黄琛 , 尹彤 , 王建明 . 高精度地图标准化建设探讨 [J ] . 中国标准化 , 2021 ( 21 ): 89 - 93 .

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POGGENHANS F , PAULS J , JANOSOVITS J , et al. Lane-let2: a high-definition map framework for the future of automated driving [C ] // Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems. Maui, HI , US : IEEE , 2018 : 1672 - 1679 .

ALTHOFF M , URBAN S , KOSCHI M . Automatic conversion of road networks from OpenDRIVE to lanelets [C ] // Proceedings of the 2018 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) . Singapore : IEEE , 2018 : 157 - 162 .

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ISO/TC204 . Intelligent transport systems—geographic data files(GDF)—GDF 5.1-part 2: map data used in automated driving systems, cooperative ITS, and multimodal transport: ISO 20524-2:2020 [S ] . Warrendale, PA , US : International Standards Organization , 2020 .

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JUNKER M . Announcement: 1st NDS open lane day on May 11, 2017 [EB/OL ] . ( 2017-04-11 )[ 2020-07-17 ] . http://www.openlanemodel.org/news_openlaneday.php http://www.openlanemodel.org/news_openlaneday.php http://www.openlanemodel.org/news_openlaneday.php.

周勇 , 刘尚魁 . 构建基于 Appollo 的高精度地图解决方案 [J ] . 电子技术与软件工程 , 2018 ( 21 ): 139 .

ZHOU Y , LIU S K . Building a high definition map solution based on Apollo [J ] . Electronic Technology & Software Engineering , 2018 ( 21 ): 139 . (in Chinese)

张晓华 . 高精度地图为自主泊车保驾护航 [C ] // 卫星导航定位与北斗系统应用 2019——北斗服务全球融合创新应用 . 北京 : 测绘出版社 , 2019 : 130 - 133 .

ZHANG X H . High Definition map provides protection for autonomous parking [C ] // Proceedings of 2019 Satellite Navigation Positioning and Beidou System Application 2019-Global Integration and Innovative Application of Beidou Service . Beijing : Mapping Press , 2019 : 130 - 133 . (in Chinese)

HOUSTON J , ZUIDHOF G , BERGAMINI L , et al. One thousand and one hours: self-driving motion prediction dataset [C ] // Proceedings of the 4th Conference on Robot Learning.Virtual, Online , US : ML Research Press , 2020 : 409 - 418 .

MARCHANT A . Behind the map:how we keep our maps up to date [EB/OL ] .( 2019-10-22 )[ 2023-03-15 ] . https://www.tomtom.com/newsroom/behind-the-map/continuous-map-processing/ https://www.tomtom.com/newsroom/behind-the-map/continuous-map-processing/ https://www.tomtom.com/newsroom/behind-the-map/continuous-map-processing/.

HERE, 2021. Map data | static map API[Online ] . Available: https://www.here.com/platform/map-data https://www.here.com/platform/map-data https://www.here.com/platform/map-data.

GEIGER A , LENZ P , STILLER C , et al. Vision meets robotics: the KITTI dataset [J ] . The International Journal of Robotics Research , 2013 , 32 ( 11 ): 1231 - 1237 . DOI: 10.1177/0278364913491297 http://doi.org/10.1177/0278364913491297 http://journals.sagepub.com/doi/10.1177/0278364913491297 http://journals.sagepub.com/doi/10.1177/0278364913491297 We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10–100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations, and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets, and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.

AESAR H , BANKITI V , LANG A , et al. nuScenes: a multimodal dataset for autonomous driving [C ] // Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA , US : IEEE , 2020 : 11618 - 11628 .

ZHANG J , SINGH S . Loam: lidar odometry and mapping in real-time [C ] // Proceedings of the 2014 IEEE Robotics:Science and Systems. Berkeley, CA , US : MIT , 2014 : 1 - 7 .

SHAN T , ENDLOT B . LeGO-LOAM: lightweight and ground-optimized LiDAR odometry and mapping on variable terrain [C ] // Proceedings of the 2018 IEEEInternational Conference on Intelligent Robots and Systems(IROS). Madrid, Spain:IEEE , 2018 : 4758 - 4765 .

SHAN T , ENGLOT B , MEYERS D , et al. LIO-SAM: tightly-coupled LiDAR inertial odometry via smoothing and mapping [C ] // Proceedings of the 2020 IEEE International Conference on Intelligent Robots and Systems (IROS).Las Vegas, NV , US : IEEE , 2020 : 5135 - 5142 .

XU W , ZHANG F . FAST-LIO: a fast, robust LiDAR-inertial odometry package by tightly-coupled iterated Kalman filter [J ] . IEEE Robotics and Automation Letters , 2021 , 6 ( 2 ): 3317 - 3324 . DOI: 10.1109/LRA.2021.3064227 http://doi.org/10.1109/LRA.2021.3064227 https://ieeexplore.ieee.org/document/9372856/ https://ieeexplore.ieee.org/document/9372856/

XU W , CAI Y X , HE D J , et al. FAST-LIO2: fast direct LiDAR-inertial odometry [J ] . IEEE Transactions on Robotics , 2022 , 38 ( 4 ): 2053 - 2073 . DOI: 10.1109/TRO.2022.3141876 http://doi.org/10.1109/TRO.2022.3141876 https://ieeexplore.ieee.org/document/9697912/ https://ieeexplore.ieee.org/document/9697912/

SHAN T , ENGLOT B , RATTI C , et al. LVI-SAM: tightly-coupled LiDAR-visual-tnertial odometry via smoothing and mapping [C ] // Proceedings of the 2021 IEEE International Conference on Robotics and Automation. Xi’an , China : IEEE , 2021 : 5692 - 5698 .

GHANDRTH H , BOULILA W , MASOOD S , et al. Semantic segmentation and edge detection—approach to road detection in very high resolution satellite images [J ] . Remote Sens , 2022 , 14 ( 3 ): 613 . DOI: 10.3390/rs14030613 http://doi.org/10.3390/rs14030613 https://www.mdpi.com/2072-4292/14/3/613 https://www.mdpi.com/2072-4292/14/3/613 Road detection technology plays an essential role in a variety of applications, such as urban planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there has been much development in detecting roads in high-resolution (HR) satellite images based on semantic segmentation. However, the objects being segmented in such images are of small size, and not all the information in the images is equally important when making a decision. This paper proposes a novel approach to road detection based on semantic segmentation and edge detection. Our approach aims to combine these two techniques to improve road detection, and it produces sharp-pixel segmentation maps, using the segmented masks to generate road edges. In addition, some well-known architectures, such as SegNet, used multi-scale features without refinement; thus, using attention blocks in the encoder to predict fine segmentation masks resulted in finer edges. A combination of weighted cross-entropy loss and the focal Tversky loss as the loss function is also used to deal with the highly imbalanced dataset. We conducted various experiments on two datasets describing real-world datasets covering the three largest regions in Saudi Arabia and Massachusetts. The results demonstrated that the proposed method of encoding HR feature maps effectively predicts sharp segmentation masks to facilitate accurate edge detection, even against a harsh and complicated background.

CHEN L , PAPANDREOU G , SCHROFF F , et al. Rethinking atrous convolution for semantic image segmentation [C ] // Proceedings of the 2020 IEEE International Symposium on Electronic Imaging Science and Technology. Burlingame, CA , US : IEEE , 2020 : 1 - 11 .

CHIKHAOUI B , RUER P , ÉVELYNE F , et al. A deep learning method for automatic visual attention detection in older drivers [C ] // Proceedings of the 17th International Conference on Smart Living and Public Health. New York, NY , US : Springer Science and Business Media Deutschland GmbH , 2019 : 49 - 60 .

LI X , ZHANG W , DING Q . Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism Signal Process [J ] . Signal Processing , 2019 , 161 : 136 - 154 . DOI: 10.1016/j.sigpro.2019.03.019 http://doi.org/10.1016/j.sigpro.2019.03.019 https://linkinghub.elsevier.com/retrieve/pii/S0165168419301124 https://linkinghub.elsevier.com/retrieve/pii/S0165168419301124

ZANG Z Y , YANG X H , FENG Z Q . Self-attention mechanism fusion for recommendation in heterogeneous information network [C ] // Proceedings of the 8th International Conference on Signal and Information Processing, Network and Computers. Jinan , China : Springer Science and Business Media Deutschland GmbH , 2022 , 917 : 1147 - 1155 .

WANG J , WEI X , LIU X . LMSA: Low-relation multi-head self-attention mechanism in visual transformer [C ] // Proceedings of the 2nd International Conference on Image, Vision and Intelligent Systems. Jinan , China : Springer Science and Business Media Deutschland GmbH , 2022 , 1019 : 609 - 619 .

BASTANI F , HE S T , ABBAR S , et al. RoadTracer: automatic extraction of road networks from aerial images [C ] // Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT , US : IEEE Computer Society , 2018 : 4720 - 4728 .

IBRAHIM M , AKHTAR N , JALWANA M , et al. High definition LiDAR mapping of perth CBD [C ] // Proceedings of 2021 Digit Image Co mput:Techniques and Applications. Gold Coast , Australia : IEEE , 2021 : 1 - 8 .

DING S D , FU Y J , WANG W F , et al. Creation of high definition map for autonomous driving within specific scene [C ] // Proceedings of the 2021 SPIE International Conference on Smart Transportation and City Engineering 2021. Chongqing, China:SPIE , 2021 : 1365 - 1373 .

GU S , ZHANG YG , TANG J H , et al. Road detection through CRF based LiDAR-camera fusion [C ] // Proceedings of the 2019 International Conference on Robotics and Automation. Montreal, QC , Canada : IEEE , 2019 : 3832 - 3838 .

GU S , ZHANG Y G , YANG J H , et al. Two-view fusion based convolutional neural network for urban road detection [C ] //Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Macau, China:IEEE , 2019 : 6144 - 6149 .

PLACHETKA C , FRICKE J . KLINGNER M , et al. DNN-based recognition of pole-like objects in LiDAR point clouds [C ] // Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference. Indianapolis, IN, US:IEEE , 2021 : 2889 - 2896 .

仲跻炜 , 陈大吾 , 朱峰 . 基于北斗高精度定位和车道级高精度地图的车辆监控平台设计与实现 [J ] . 全球定位系统 , 2015 , 40 ( 4 ): 50 - 55 .

ZHONG J W , CHEN D W , ZHU F . Design and implementation of a vehicle monitoring platform based on beidou high precision positioning and lane level high precision maps [J ] . GNSS World of China , 2015 , 40 ( 4 ): 50 - 55 . (in Chinese)

李亮 . 基于点云匹配的智能车定位方法研究 [D ] . 上海 : 上海交通大学 , 2018 : 1 - 10 .

LI L . Research on point cloud matching based localization for intelligent vehicles [D ] . Shanghai : Shanghai Jiao Tong University , 2018 : 1 - 10 . (in Chinese)

王一文 , 钱闯 , 唐健 , 等 . 预建高精度地图的封闭区域UGV 自动驾驶导航定位 [J ] . 测绘通报 , 2020 ( 1 ): 21 - 25 . DOI: 10.13474/j.cnki.11-2246.2020.0005 http://doi.org/10.13474/j.cnki.11-2246.2020.0005 针对封闭区域UGV自动驾驶应用，本文提出了一种基于平面高精度地图的导航定位方法。该方法利用三维激光扫描数据采用预定义的栅格地图概率值建立多分辨率地图，在保证定位精度的同时提高定位效率，采用极大似然估计获取载体位姿初值并将IMU数据用于计算高斯-牛顿法搜索初值。试验结果表明，基于激光扫描的二维地图构建与匹配定位方法能有效解决帧与帧匹配误差快速累积问题，可以高效地使用已有地图进行连续高精度的载体定位。

WANG Y W , QIAN C , TANG J , et al. UGV’s automated driving navigation and localization in closed areas with pre-established high precision planar map [J ] . Bulletin of Surveying and Mapping , 2020 ( 1 ): 21 - 25 . (in Chinese)

周涛 . 单目视觉下车道线检测与跟踪算法的研究与测试 [D ] . 武汉 : 华中师范大学 , 2016 : 1 - 5 .

ZHOU T . Research and testing on lane detection and tracking algorithm based on monocular vision [D ] . Wuhan : Central China Normal University , 2016 : 1 - 5 . (in Chinese)

胡钊政 , 孙莹妹 , 李祎承 . 路面路标高精度地图构建与多尺度车辆定位 [J ] . 哈尔滨工业大学学报 , 2019 , 51 ( 9 ): 149 - 156 .

HU Z Z , SUN Y S , LI Y C . High definition map construction from pavement landmarks for multi-scale vehicle localization [J ] . Journal of Harbin Institute of Technology , 2019 , 51 ( 9 ): 149 - 156 . (in Chinese)

祖似杰 , 张攀 , 罗跃军 . 基于车道驾驶态势的拓扑构建与路径规划 [J ] . 地理空间信息 , 2018 , 16 ( 5 ): 53 - 56 .

ZU S J , ZHANG P , LUO Y J . Topology construction and path planning based on lane driving situation [J ] . Geospatial Information , 2019 , 16 ( 5 ): 53 - 56 . (in Chinese)

訾烨 , 任明武 . 一种基于高精度地图匹配误差的路径规划方法 [J ] . 计算机与数字工程 , 2021 , 49 ( 11 ): 2248 - 2253 .

ZI Y , REN M W . A path planning method based on high precision map matching error [J ] . Computer & Digital Engineering , 2021 , 49 ( 11 ): 2248 - 2253 . (in Chinese)

陈根 . 移动测量技术在高精度地图数据生产方面的应用分析 [J ] . 城市建设理论研究(电子版) , 2018 ( 5 ): 104 - 105 .

CHEN G . Application analysis of mobile measurement technology in high-precision map data production [J ] . Theoretical Research in Urban Construction , 2018 ( 5 ): 104 - 105 . (in Chinese)

曹波 . 基于多传感器的高精度地图生成技术研究 [J ] . 内燃机与配件 , 2019 ( 16 ): 133 - 135 .

CAO B . Research on high precision map generation technology based on multiple sensors [J ] . Internal Combustion Engine & Parts , 2019 ( 16 ): 133 - 135 . (in Chinese)

ZHANG J Y . Application of panoramic laser mobile measurement in highway operation management [C ] // Proceedings of the 2021 IEEE 2021 4th International Symposium on Traffic Transportation and Civil Architecture. Suzhou, China:IEEE , 2021 : 7 - 10 .

CHEN W F , ZHOU C J , SHANG G T , et al. SLAM Overview: from single sensor to heterogeneous fusion [J ] . Remote Sensing , 2022 , 14 ( 23 ): 6033 . DOI: 10.3390/rs14236033 http://doi.org/10.3390/rs14236033 https://www.mdpi.com/2072-4292/14/23/6033 https://www.mdpi.com/2072-4292/14/23/6033 After decades of development, LIDAR and visual SLAM technology has relatively matured and been widely used in the military and civil fields. SLAM technology enables the mobile robot to have the abilities of autonomous positioning and mapping, which allows the robot to move in indoor and outdoor scenes where GPS signals are scarce. However, SLAM technology relying only on a single sensor has its limitations. For example, LIDAR SLAM is not suitable for scenes with highly dynamic or sparse features, and visual SLAM has poor robustness in low-texture or dark scenes. However, through the fusion of the two technologies, they have great potential to learn from each other. Therefore, this paper predicts that SLAM technology combining LIDAR and visual sensors, as well as various other sensors, will be the mainstream direction in the future. This paper reviews the development history of SLAM technology, deeply analyzes the hardware information of LIDAR and cameras, and presents some classical open source algorithms and datasets. According to the algorithm adopted by the fusion sensor, the traditional multi-sensor fusion methods based on uncertainty, features, and novel deep learning are introduced in detail. The excellent performance of the multi-sensor fusion method in complex scenes is summarized, and the future development of multi-sensor fusion method is prospected.

WU W W . Urban road automatic mapping method based on remote sensing image [C ] // Proceedings of the 2023 IEEE 4th Annual Flagship India Council International Subsections Conference:Computational Intelligence and Learning Systems 2023. Mysore, India:IEEE , 2023 : 1 - 6 .

LI X , LI H W . Application of neural network in map annotation recognition [C ] // Proceedings of the 2nd International Conference on Artificial Intelligence, Big Data and Algorithms. Virtual , Online : VDE VERLAG GmbH , 2022 : 76 - 79 .

XU H H , LI W , CAI Z P . Analysis on methods to effectively improve transfer learning performance [J ] . Theoretical Computer Science , 2023 , 940(B) : 90 - 107 .

LIU Q , ZHANG Y R , WANG H X . EdgeMap: CrowdSourcing high definition map in automotive edge computing [C ] //Proceedings of the 2022 IEEE International Conference on Communications. Seoul, Korea:IEEE , 2022 : 4300 - 4305 .

PORTILLA-FIGUERAS A , LLOPIS-SANCHEZ S , JIMENEZ-FERNANDEZ S , et al. Examining 5G technology-based applications for military communications [C ] // Proceedings of the 2023 IEEE Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Copenhagen , Denmark : Springer Science and Business Media Deutschland GmbH , 2023 : 449 - 465 .

LI F D , ZENG Q H , XIONG Z , et al. Development and future direction of unmanned system based on inertial integrated navigation [C ] // Proceedings of the 2023 IEEE Lecture Notes in Electrical Engineering. Harbin , China : Springer Science and Business Media Deutschland GmbH , 2023 : 7152 - 7161 .

YANG B , SONG X W , GAO Z H . A lane level bi-directional hybrid path planning method based on high definition map [J ] . World Electric Vehicle Journal , 2021 , 12 ( 4 ): 227 . DOI: 10.3390/wevj12040227 http://doi.org/10.3390/wevj12040227 https://www.mdpi.com/2032-6653/12/4/227 https://www.mdpi.com/2032-6653/12/4/227 A global reference path generated by a path search algorithm based on a road-level driving map cannot be directly used to complete the efficient autonomous path-following motion of autonomous vehicles due to the large computational load and insufficient path accuracy. To solve this problem, this paper proposes a lane-level bidirectional hybrid path planning method based on a high-definition map (HD map), which effectively completes the high-precision reference path planning task. First, the global driving environment information is extracted from the HD map, and the lane-level driving map is constructed. Real value mapping from the road network map to the driving cost is realized based on the road network information, road markings, and driving behavior data. Then, a hybrid path search method is carried out for the search space in a bidirectional search mode, where the stopping conditions of the search method are determined by the relaxation region in the two search processes. As the search process continues, the dimension of the relaxation region is updated to dynamically adjust the search scope to maintain the desired search efficiency and search effect. After the completion of the bidirectional search, the search results are evaluated and optimized to obtain the reference path with the optimal traffic cost. Finally, in an HD map based on a real scene, the path search performance of the proposed algorithm is compared with that of the simple bidirectional Dijkstra algorithm and the bidirectional BFS search algorithm. The results show that the proposed path search algorithm not only has a good optimization effect, but also has a high path search efficiency.

QU Y Q , FAN Y X , ZHANG X L , et al. Improved A * path planning method based on the grid map [J ] . Sensors , 2022 , 22 ( 16 ): 6198 . DOI: 10.3390/s22166198 http://doi.org/10.3390/s22166198 https://www.mdpi.com/1424-8220/22/16/6198 https://www.mdpi.com/1424-8220/22/16/6198 In obstacle spatial path planning, the traditional A* algorithm has the problem of too many turning points and slow search speed. With this in mind, a path planning method that improves the A* (A-Star) algorithm is proposed. The mobile robot platform was equipped with a lidar and inertial measurement unit (IMU). The Hdl_graph_slam mapping algorithm was used to construct a two-dimensional grid map, and the improved A* algorithm was used for path planning of the mobile robot. The algorithm introduced the path smoothing strategy and safety protection mechanism, and it eliminated redundant points and minimal corner points by judging whether there were obstacles in the connection of two path nodes. The algorithm effectively improved the smoothness of the path and facilitated the robot to move in the actual operation. It could avoid the wear of the robot by expanding obstacles and improving the safety performance of the robot. Subsequently, the algorithm introduced the steering cost model and the adaptive cost function to improve the search efficiency, making the search purposeful and effective. Lastly, the effectiveness of the proposed algorithm was verified by experiments. The average path search time was reduced by 13%. The average search extension node was reduced by 11%. The problems of too many turning points and slow search speed of traditional A* algorithm in path planning were improved.