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A Zoox autonomous prototype vehicle, outfitted with sensors for sensing and high-definition mapping.

A high-definition map (HD map) is a highly accurate map used primarily in the field of autonomous driving, containing details not normally present on traditional maps. HD maps are often captured using an array of sensors, such as LiDARs, radars, digital cameras, and GPS, and they can also be constructed using aerial imagery. Such maps can be precise at a centimetre level.

High-definition maps for self-driving cars usually include map elements such as road shape, road marking, traffic signs, and barriers. Maintaining high accuracy is one of the biggest challenges in building HD maps of real-world roads. With regard to accuracy, there are two main focus points that determine the quality of an HD map:

  • Global accuracy (positioning of a feature on the surface of the Earth).
  • Local accuracy (positioning of a feature in relation to road elements around it).

In areas with good GPS reception it is possible to achieve a global accuracy of less than 3 cm (1.2 in) deviation using satellite signals and correction data from base stations.

In GPS-denied areas, however, inaccuracy rises with distance traveled through the area, being largest in its middle. This means that the maximum GPS error can be expressed as a percentage of the distance traveled through a GPS-denied area: this value is less than 0.5%.

References

  1. Liu, Rong; Wang, Jinling; Zhang, Bingqi (27 August 2019). "High Definition Map for Automated Driving: Overview and Analysis". Journal of Navigation. 73 (2): 324–341. doi:10.1017/S0373463319000638. hdl:1959.4/unsworks_77182. ISSN 0373-4633. S2CID 202906063. Retrieved 2024-11-08.
  2. Elghazaly, Gamal; Frank, Raphael; Harvey, Scott; Safko, Stefan (2023). "High-Definition Maps: Comprehensive Survey, Challenges, and Future Perspectives". IEEE Open Journal of Intelligent Transportation Systems. 4: 527–550. doi:10.1109/OJITS.2023.3295502. S2CID 259915452.
  3. ^ Vardhan, Harsha (2017-09-22). "HD Maps: New age maps powering autonomous vehicles". Geospatial World. Retrieved 2021-01-20.
  4. ^ Matthews, Kayla (September 16, 2019). "What are HD maps, and how will they get us closer to autonomous cars?". EETimes. Retrieved 2024-11-08.
  5. Holmes, Freddie (14 March 2019). "HD maps—the hidden sensors that help autonomous vehicles see round corners". Automotive World. Retrieved 2021-01-20.
  6. Mueck, Markus; Karls, Ingolf (9 January 2018). Networking vehicles to everything : evolving automotive solutions. Boston: De Gruyter. ISBN 978-1-5015-0724-3. OCLC 1021887635. Retrieved 2024-11-08.
  7. Javanmardi, Mahdi; Javanmardi, Ehsan; Gu, Yanlei; Kamijo, Shunsuke (2017-09-21). "Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery". Remote Sensing. 9 (10): 975. Bibcode:2017RemS....9..975J. doi:10.3390/rs9100975. ISSN 2072-4292.
  8. Zang, Andi; Xu, Runsheng; Li, Zichen; Doria, David (2017-11-07). "Lane boundary extraction from satellite imagery". Proceedings of the 1st ACM SIGSPATIAL Workshop on High-Precision Maps and Intelligent Applications for Autonomous Vehicles. AutonomousGIS '17. Redondo Beach California: ACM. pp. 1–8. arXiv:2002.02362. doi:10.1145/3149092.3149093. ISBN 978-1-4503-5497-4. S2CID 11512991. Retrieved 2024-11-08.
  9. Jiao, Jialin (22 June 2018). "Machine Learning Assisted High-Definition Map Creation". 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). Vol. 01. pp. 367–373. doi:10.1109/COMPSAC.2018.00058. ISBN 978-1-5386-2666-5. S2CID 52058583. Retrieved 2024-11-08.
  10. Zang, Andi; Chen, Xin; Trajcevski, Goce (2018-06-05). "High definition maps in urban context". SIGSPATIAL Special. 10 (1): 15–20. doi:10.1145/3231541.3231546. ISSN 1946-7729. S2CID 47019015. Retrieved 2024-11-08.
  11. "How Accurate Are HD Maps for Autonomous Driving and ADAS Simulation?". Atlatec. 2020-10-22. Retrieved 2021-05-20.


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