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High Definition Map for Automated Driving: Overview and Analysis

Published online by Cambridge University Press:  27 August 2019

Rong Liu ,
Jinling Wang  and
Bingqi Zhang
Rong Liu*
Affiliation:
(University of New South Wales, Australia)
Jinling Wang
Affiliation:
(University of New South Wales, Australia)
Bingqi Zhang
Affiliation:
(China Transport Telecommunication and Information Centre, China)
*
(E-mail: rongliu1996@gmail.com)
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Abstract

As one of the key enabling technologies for automated driving, High Definition (HD) Maps have become a major research focus in recent years. While increasing research effort has been directed toward HD Map development, a comprehensive review of the overall conceptual framework and development status is still lacking. In this study, we start with a brief review of the highlights of navigation map history, and then present an extensive literature review of HD Map development for automated driving, focusing on HD Map structure, functionalities, and accuracy requirements as well as standardisation aspects. In addition, this study conducts an analysis of HD Map-based vehicle localisation. The numerical results demonstrate the potential capabilities of HD Maps. Some recommendations for further investigation are made.

Keywords

HD MapLocalisationAutomated driving

Information

Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 2 , March 2020 , pp. 324 - 341
Copyright
Copyright © The Royal Institute of Navigation 2019

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References

REFERENCES

Aeberhard, M., Rauch, S., Bahram, M., Tanzmeister, G., Thomas, J., Pilat, Y., Homm, F., Huber, W. and Kaempchen, N. (2015). Experience, Results and Lessons Learned from Automated Driving on Germany's Highways. IEEE Intelligent Transportation Systems Magazine, 7, 42–57.CrossRefGoogle Scholar
Bauer, S., Alkhorshid, Y. and Wanielik, G. (2016). Using High-Definition Maps for Precise Urban Vehicle Localization. Proceedings of IEEE International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.CrossRefGoogle Scholar
Beazley, R. C. (1904). The First True Maps, Nature, 1833(71), 159–161.CrossRefGoogle Scholar
Besl, P.J. and McKay, N.D. (1992). A Method for Registration Of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14.CrossRefGoogle Scholar
Betaille, D. and Toledo-Moreo, R. (2010). Creating Enhanced Maps for Lane-Level Vehicle Navigation. IEEE Transactions on Intelligent Transportation Systems, 11, 786–798.CrossRefGoogle Scholar
Biber, P. and Strasser, W. (2003). The Normal Distributions Transform: A New Approach to Laser Scan Matching. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA.CrossRefGoogle Scholar
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. and Leonard, J. (2016). Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Transactions on Robotics, 32(6): 1309–1332.CrossRefGoogle Scholar
Campbell, T. (1987). Portolan Charts from the Late Thirteenth Century to 1500, Volume 1. The History of Cartography (University of Chicago Press), 371–463.Google Scholar
Chen, Y. and Medioni, G. (1991). Object Modeling by Registration of Multiple Range Images. Proceedings of IEEE International Conference on Robotics and Automation, Sacramento, CA.CrossRefGoogle Scholar
Churchill, W. and Newman, P. (2013). Experience-Based Navigation for Long-Term Localization. International Journal of Robotics Research, 32, 1645–1661.CrossRefGoogle Scholar
Cui, D., Xue, J., Du, S. and Zheng, N. (2014). Real-Time Global Localization of Intelligent Road Vehicles in Lane-Level via Lane Marking Detection and Shape Registration. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, Illinois.CrossRefGoogle Scholar
Dannehy, M. (2016). 3D Maps: Beyond Automotive. TomTom. https://goo.gl/WpwiiX. Accessed on 5 June 2019.Google Scholar
DARPA. (2007). DARPA Urban Challenge Route Network Definition File (RNDF) and Mission Data File (MDF) Formats. https://goo.gl/XbXKfL. Accessed on 5 June 2019.Google Scholar
Deusch, H., Wiest, J., Reuter, S., Nuss, D., Fritzsche, M. and Dietmayer, K. (2014). Multi-Sensor Self-Localization Based on Maximally Stable Extremal Regions. Proceedings of IEEE Intelligent Vehicles Symposium, Dearborn, Michigan.CrossRefGoogle Scholar
EDMap. (2004). Enhanced Digital Mapping Project Final Report. Submitted to the United States Department of Transportation, Federal Highway Administration and National Highway Traffic and Safety Administration. https://goo.gl/SPF7h8. Accessed on 5 June 2019.Google Scholar
El-Sheimy, N. (2015). An overview of mobile mapping systems. https://goo.gl/Kfrf34. Proceedings of FIG (International Federation of Surveyors) Working Week 2005 and GSDI-8, From Pharaohs to Geoinformatics. Cairo, Egypt. Accessed on 5 June 2019.Google Scholar
Elfring, J., Appeldoorn, R., van den Dries, S. and Kwakkernaat, M. (2016). Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving. Sensors, 16(10): 1668.10.3390/s16101668CrossRefGoogle ScholarPubMed
Endres, F., Hess, J., Sturm, J., Cremers, D. and Burgard, W. (2014). 3-D Mapping with an RGB-D Camera. IEEE Transactions on Robotics, 30, 177–187.CrossRefGoogle Scholar
Esri. (2018). GIS Dictionary, Esri Technical Support. https://goo.gl/F25aKH. Accessed on 5 June 2019.Google Scholar
Grimmett, H., Buerki, M., Paz, L., Pinies, P., Furgale, P., Posner, I. and Newman, P. (2015). Integrating metric and semantic maps for vision-only automated parking. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, Washington.CrossRefGoogle Scholar
Grisetti, G., Stachniss, C., Burgard, W. (2009). Nonlinear Constraint Network Optimization for Efficient Map Learning. IEEE Transactions on Intelligent Transportation System, 10, 428–439.CrossRefGoogle Scholar
Guidi, F., Guerra, A. and Dardari, D. (2016). Personal Mobile Radars with Millimeter-Wave Massive Arrays for Indoor Mapping. IEEE Transactions on Mobile Computing, 15, 1471–1484.CrossRefGoogle Scholar
Guo, C., Meguro, J.i., Kojima, Y. and Naito, T. (2014). Automatic Lane-Level Map Generation for Advanced Driver Assistance Systems Using Low-Cost Sensors. Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.CrossRefGoogle Scholar
Gwon, G.P., Hur, W.S., Kim, S.W. and Seo, S.W. (2017). Generation of A Precise and Efficient Lane-Level Road Map for Intelligent Vehicle Systems. IEEE Transactions on Vehicular Technology, 66, 4517–4533.CrossRefGoogle Scholar
HERE. (2018). HERE HD Live Map, The Most Intelligent Sensor for Autonomous Driving. HERE Technologies. https://bit.ly/2Woss4K. Accessed on 5 June 2019.Google Scholar
Herrtwich, R. (2018). The evolution of the HERE HD Live Map at Daimler. HERE Technologies. https://goo.gl/5U9BmD. Accessed on 5 June 2019.Google Scholar
Irie, K., Yoshida, T. and Tomono, M. (2010). Mobile Robot Localization using Stereo Vision in Outdoor Environments under Various Illumination Conditions. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan.CrossRefGoogle Scholar
ISO. (2011). INTERNATIONAL STANDARD ISO 14825 Intelligent transport systems — Geographic Data Files (GDF) — GDF5.0. Reference number ISO 14825:2011(E). Second Edition 2011-07-15. Published in Switzerland.Google Scholar
ISO. (2018). INTERNATIONAL STANDARD ISO 18750 Intelligent transport systems - Co-operative ITS - Local dynamic map. Reference Number ISO 18750:2018(E). First Edition 2018-05. Published in Switzerland.Google Scholar
Jo, K., Jo, Y., Suhr, J.K., Jung, H.G. and Sunwoo, M. (2015). Precise Localization of an Autonomous Car Based on Probabilistic Noise Models of Road Surface Marker Features Using Multiple Cameras. IEEE Transactions on Intelligent Transportation Systems, 16, 3377–3392.CrossRefGoogle Scholar
Kato, S., Takeuchi, E., Ishiguro, Y., Ninomiya, Y., Takeda, K. and Hamada, T. (2015). An Open Approach to Autonomous Vehicles. IEEE Micro, 35, 60–68.CrossRefGoogle Scholar
Kotei. (2016). Discussion on Several Problems of HAD Map, Internal Report via private communication. http://kotei.com.cn/Google Scholar
Kühn, W., Müller, M. and Höppner, T. (2017). Road Data as Prior Knowledge for Highly Automated Driving. Transportation Research Procedia, 27, 222–229.CrossRefGoogle Scholar
Lategahn, H. and Stiller, C. (2012) City GPS Using Stereo Vision. Proceedings of IEEE International Conference on Vehicular Electronics and Safety, Istanbul, Turkey.10.1109/ICVES.2012.6294279CrossRefGoogle Scholar
Leite, J.P. (2018). A brief History of GPS In-Car Navigation. https://goo.gl/vdYyzC. Accessed on 15 May 2018.Google Scholar
Levinson, J., Montemerlo, M. and Thrun, S. (2007). Map-based precision vehicle localization in urban environments. Proceedings of the Robotics: Science and Systems, Atlanta, Georgia.Google Scholar
Levinson, J. and Thrun, S. (2010). Robust vehicle localization in urban environments using probabilistic maps. Proceedings of IEEE International Conference on Robotics and Automation, Anchorage, Alaska.CrossRefGoogle Scholar
Li, L., Yang, M., Wang, C. and Wang, B. (2016). Road DNA Based Localization for Autonomous Vehicles. Proceedings of IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.Google Scholar
Maddern, W., Pascoe, G. and Newman, P. (2015). Leveraging Experience for Large-Scale LiDAR Localization in Changing Cities. Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Seattle, Washington.CrossRefGoogle Scholar
Magnusson, M., Lilienthal, A. and Duckett, T. (2007). Scan Registration for Autonomous Mining Vehicles using 3D-NDT. Journal of Field Robotics, 24, 803–827.10.1002/rob.20204CrossRefGoogle Scholar
Massow, K., Kwella, B., Pfeifer, N., Häusler, F., Pontow, J., Radusch, I., Hipp, J., Dölitzscher, F. and Haueis, M. (2016). Deriving HD maps for highly automated driving from vehicular probe data, Proceedings of IEEE International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, BrazilCrossRefGoogle Scholar
Matthaei, R. and Maurer, M. (2015). Autonomous Driving - A Top-down-approach. Automatisierungstechnik,63, 155–167.CrossRefGoogle Scholar
Máttyus, G, Wang, S., Fidler, S. and Urtasun, R. (2016). In HD Maps: Fine-grained road segmentation by parsing ground and aerial images, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada.Google Scholar
Morales, Y., Tsubouchi, T. and Yuta, S.I. (2010). Vehicle Localization in Outdoor Mountainous Forested Paths and Extension of Two-Dimensional Road Centerline Maps to Three-Dimensional Maps, Advanced Robotics, 24, 489–513.CrossRefGoogle Scholar
NDS. (2016). Navigation Data Standard Open Lane Model Documentation Open Lane Model version 1.0. http://www.openlanemodel.org/. Accessed on 5 June 2019.Google Scholar
Newcomb, D. (2013). From Hand-Cranked Maps to the Cloud: Charting the History of In-Car Navigation. https://bit.ly/2Ksd93E. Accessed 5 June 2019.Google Scholar
NextMap. (2002). Final Report. https://goo.gl/95b4SA. Accessed on 5 June 2019.Google Scholar
OpenDRIVE. (2015). OpenDRIVE® Format Specification (Rev. 1.4). https://goo.gl/epn5Pv. Accessed on 15 May 2018.Google Scholar
Pascoe, G., Maddern, W.P., Stewart, A.D. and Newman, P. (2015). Farlap: Fast Robust Localization using Appearance Priors. Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Seattle, Washington.CrossRefGoogle Scholar
Pink, O. (2008). Visual Map Matching and Localization using a Global Feature Map. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Anchorage, Alaska.CrossRefGoogle Scholar
Roh, H., Jeong, J., Cho Y. and Kim, A. (2016). Accurate mobile urban mapping via digital map-based SLAM. Sensors (Basel, Switzerland), 16(8), 1315.CrossRefGoogle ScholarPubMed
SAE. (2018). Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles (revised June 2018). Downloaded from SAE International web site https://goo.gl/FAY7MQ on Monday, July 09, 2018.Google Scholar
Schreiber, M., Knöppel, C. and Franke, U. (2013). Laneloc: Lane Marking Based Localization using Highly Accurate Maps. Proceedings of IEEE Intelligent Vehicles Symposium (IV), Gold Coast, Australia.CrossRefGoogle Scholar
Stewart, A.D. and Newman, P. (2012). Laps - Localization using Appearance of Prior Structure: 6-DOF Monocular Camera Localization using Prior Pointclouds. Proceedings of IEEE International Conference on Robotics and Automation, Beijing, China.Google Scholar
Takeuchi, E. and Tsubouchi, T. (2006). A 3-D Scan Matching using Improved 3-D Normal Distributions Transform for Mobile Robotic Mapping. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China.CrossRefGoogle Scholar
TomTom. (2018). HD Maps - Highly Accurate Border-to-border Model of the Road. TomTom. https://bit.ly/2WrI1sd. Accessed on 5 June 2019.Google Scholar
Ulbrich, S., Reschka, A., Rieken, J., Ernst, S., Bagschik, G., Dierkes, F., Nolte, M. and Maurer, M. (2017). Towards A Functional System Architecture for Automated Vehicles. arXiv:1703.08557 [cs.SY]. https://arxiv.org/abs/1703.08557 Accessed on 15 May 2018.Google Scholar
Wang, J., Song, J., Chen, M. and Yang, Z. (2015). Road network extraction: A neural-dynamic framework based on deep learning and a finite state machine. International Journal of Remote Sensing, 36, 3144–3169.CrossRefGoogle Scholar
Wolcott, R.W. and Eustice, R.M. (2014). Visual Localization Within LiDAR Maps for Automated Urban Driving. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, Illinois.CrossRefGoogle Scholar
Wolcott, R.W. and Eustice, R.M. (2015). Fast LiDAR Localization using Multiresolution Gaussian Mixture Maps. Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Seattle, Washington.CrossRefGoogle Scholar
Xu, Y., John, V., Mita, S., Tehrani, H., Ishimaru, K. and Nishino, S. (2017). 3D Point Cloud Map Based Vehicle Localization Using Stereo Camera. Proceedings of IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, California.CrossRefGoogle Scholar
Zhang, T., Arrigoni, S., Garozzo, M., Yang, D. and Cheli, F. (2016). A Lane-Level Road Network Model with Global Continuity. Transportation Research Part C: Emerging Technologies, 71, 32–50.CrossRefGoogle Scholar
Zheng, S. and Wang, J. (2017). High Definition Map Based Vehicle Localization for Highly Automated Driving: Geometric Analysis. Proceedings of IEEE International Conference on Localisation and GNSS (ICL-GNSS), Nottingham, United Kingdom. 27–29 June. DOI:10.1109/ICL-GNSS.2017.8376252.CrossRefGoogle Scholar
Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B. and Hu, S. (2016). In Traffic-sign detection and classification in the wild, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada.CrossRefGoogle Scholar
Ziegler, J., Bender, P., Schreiber, M., Lategahn, H., Strauss, T., Stiller, C., Dang, T., Franke, U., Appenrodt, N., Keller, C.G., Kaus, E., Stiller, C. and Herrtwich, R.G. (2014). Making Bertha Drive - An Autonomous Journey on A Historic Route. IEEE Intelligent Transportation Systems Magazine, 6, 8–20.CrossRefGoogle Scholar