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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-todate 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

INTO JAPANESE

協調型、接続型、自動化されたモビリティ (CCAM) では、自動運転車両が周囲の環境を認識、モデル化、分析できるようになると、より認識し、理解して意思決定を行うことができるようになり、複雑な運転シナリオを安全かつ効率的に実行できるようになります。 。道路環境を表す高精細 (HD) 地図

BACK INTO ENGLISH

Collaborative, connected, and automated mobility (CCAM) enables self-driving vehicles to become more aware, understandable, and make decisions by being able to perceive, model, and analyze their surroundings. enables you to perform complex driving scenarios safely and efficiently.

INTO JAPANESE

Collaborative, Connected, and Automated Mobility (CCAM) により、自動運転車は周囲の状況を認識、モデル化、分析できるようになり、より認識し、理解しやすくなり、意思決定を行うことができるようになります。複雑な運転シナリオを安全かつ効率的に実行できるようになります。

BACK INTO ENGLISH

Collaborative, Connected, and Automated Mobility (CCAM) enables self-driving cars to perceive, model, and analyze their surroundings, enabling them to better perceive, understand, and make decisions. Masu. Now you can execute complex driving scenarios safely and efficiently.

INTO JAPANESE

Collaborative, Connected, and Automated Mobility (CCAM) により、自動運転車が周囲を認識、モデル化、分析できるようになり、より適切に認識、理解、意思決定できるようになります。ます。複雑な運転シナリオを安全かつ効率的に実行できるようになりました。

BACK INTO ENGLISH

Collaborative, Connected, and Automated Mobility (CCAM) enables self-driving cars to perceive, model, and analyze their surroundings to better perceive, understand, and make decisions. Masu. Complex driving scenarios can now be performed safely and efficiently.

INTO JAPANESE

Collaborative, Connected, and Automated Mobility (CCAM) を使用すると、自動運転車が周囲を認識、モデル化、分析して、より適切に認識、理解、意思決定できるようになります。ます。複雑な運転シナリオを安全かつ効率的に実行できるようになりました。

BACK INTO ENGLISH

Collaborative, Connected, and Automated Mobility (CCAM) enables self-driving cars to perceive, model, and analyze their surroundings to better perceive, understand, and make decisions. Masu. Complex driving scenarios can now be performed safely and efficiently.