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In cooperative, connected, and automated mobility (CCAM), the more automated vehicles can perceive, model, and analyze the surrounding environment, the more they become aware and capable of understanding, making decisions, as well as safely and efficiently executing complex driving scenarios. High-definition (HD) maps represent the road environment with unprecedented centimetre-level precision with lane-level semantic information, making them a core component in smart mobility systems, and a key enabler for CCAM technology. These maps provide automated vehicles with a strong prior to understand the surrounding environment. An HD map is also considered as a hidden or virtual sensor, since it aggregates knowledge (mapping) from physical sensors, i.e. LiDAR, camera, GPS and IMU to build a model of the road environment. Maps for automated vehicles are quickly evolving towards a holistic representation of the digital infrastructure of smart cities to include not only road geometry and semantic information, but also live perception of road participants, updates on weather conditions, work zones and accidents. Deployment of autonomous vehicles at a large scale necessitates building and maintaining these maps by a large fleet of vehicles which work cooperatively to continuously keep maps up-to-date for autonomous vehicles in the fleet to function properly. This article provides an extensive review of the various applications of these maps in highly autonomous driving (AD) systems. We review the state-of-the-art of the different approaches and algorithms to build and maintain HD maps. Furthermore, we discuss and synthesise data, communication and infrastructure requirements for the distribution of HD maps. Finally, we review the current challenges and discuss future research directions for the next generation of digital mapping systems.
Elghazaly et al. (Sun,) studied this question.