High-definition map explained

A high-definition map (HD map) is a highly accurate map used primarily in the field of autonomous driving,^{[1] [2]} containing details not normally present on traditional maps.^{[3] [4]} HD maps are often captured using an array of sensors, such as LiDARs, radars, digital cameras, and GPS,^{[3] [5] [6]} and they can also be constructed using aerial imagery.^{[7] [8]} Such maps can be precise at a centimetre level.^{[3] [9]}

High-definition maps for self-driving cars usually include map elements such as road shape, road marking, traffic signs, and barriers.^{[4] [10]} 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 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%.^[11]

Notes and References

1. Liu . Rong . Wang . Jinling . Zhang . Bingqi . 27 August 2019 . High Definition Map for Automated Driving: Overview and Analysis . Journal of Navigation . en . 73 . 2 . 324–341 . 10.1017/S0373463319000638 . 202906063 . 0373-4633 . 1959.4/unsworks_77182 . free . 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 . en . 4 . 527–550 . 10.1109/OJITS.2023.3295502 . 259915452 . free.
3. Web site: Vardhan . Harsha . 2017-09-22 . HD Maps: New age maps powering autonomous vehicles . 2021-01-20 . Geospatial World . en-US.
4. News: Matthews . Kayla . September 16, 2019 . What are HD maps, and how will they get us closer to autonomous cars? . . 2024-11-08.
5. Web site: Freddie . Holmes . 14 March 2019 . HD maps—the hidden sensors that help autonomous vehicles see round corners . 2021-01-20 . Automotive World.
6. Book: Mueck . Markus . Networking vehicles to everything : evolving automotive solutions . Karls . Ingolf . 9 January 2018 . 978-1-5015-0724-3 . Boston . De Gruyter . 1021887635 . 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 . en . 9 . 10 . 975 . 10.3390/rs9100975 . 2017RemS....9..975J . 2072-4292 . free.
8. Book: Zang . Andi . Xu . Runsheng . Li . Zichen . Doria . David . Proceedings of the 1st ACM SIGSPATIAL Workshop on High-Precision Maps and Intelligent Applications for Autonomous Vehicles . Lane boundary extraction from satellite imagery . 2017-11-07 . https://dl.acm.org/doi/10.1145/3149092.3149093 . AutonomousGIS '17 . en . Redondo Beach California . ACM . 1–8 . 10.1145/3149092.3149093 . 978-1-4503-5497-4 . 2002.02362 . 11512991 . 2024-11-08.
9. Book: Jiao, Jialin . 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) . 22 June 2018 . 978-1-5386-2666-5 . 01 . 367–373 . Machine Learning Assisted High-Definition Map Creation . 10.1109/COMPSAC.2018.00058 . https://ieeexplore.ieee.org/document/8377682 . 52058583 . 2024-11-08.
10. Zang . Andi . Chen . Xin . Trajcevski . Goce . 2018-06-05 . High definition maps in urban context . SIGSPATIAL Special . en . 10 . 1 . 15–20 . 10.1145/3231541.3231546 . 47019015 . 1946-7729 . 2024-11-08.
11. Web site: 2020-10-22 . How Accurate Are HD Maps for Autonomous Driving and ADAS Simulation? . 2021-05-20 . Atlatec . en-US.