Dynamic Map Update Protocol for Highly Automated Driving Vehicles

Highly automated driving vehicles are currently subject of strong research efforts to enable novel mobility experiences. To achieve this goal a high definition street map is required. It provides the vehicles with centimetre accurate references to all geographic objects in its surrounding. So this street map enables driving capabilities of the automated vehicle in terms of safety and comfort for the passengers that could not be obtained while only relying on the cars own inbuilt sensor equipment. This high definition street map has to ensure the accuracy and timeliness of its data, necessary for the task of highly automated driving, at any time. Therefore those maps have to be constantly provided with updates from a remote server. This paper describes a protocol based mainly on preselection of contextual relevant map data to provide a car in an efficient way with such a continuous stream of updates. The capabilities of the protocol have been evaluated on a map database of Berlin. The obtained results verify that it achieves a significant decrease in transmission data and processing time, compared to existing map update approaches.