A Collaborative Framework for High-Definition Mapping

Abstract

For connected vehicles to have a substantial effect on road safety, it is required that accurate positions and trajectories can be shared. To this end, all vehicles must be accurately geo-localized in a common frame. This can be achieved by merging GNSS (Global Navigation Satellite System) information and visual observations matched with a map of geopositioned landmarks. Building such a map remains a challenge, and current solutions are facing strong cost-related limitations.We present a collaborative framework for high-definition mapping, in which vehicles equipped with standard sensors, such as a GNSS receiver and a mono-visual camera, update a map of geo-localized landmarks. Our system is composed of two processing blocks: the first one is embedded in each vehicle, and aims at geo-localizing the vehicle and the detected feature marks. The second is operated on cloud servers, and uses observations from all the vehicles to compute updates for the map of geo-positioned landmarks. As the map's landmarks are detected and positioned by more and more vehicles, the accuracy of the map increases, eventually converging in probability towards a null error. The landmarks' geo-positions are estimated in a stable and scalable way, enabling to provide dynamic map updates in an automatic manner.

Multi-Session High-Definition Map-Monitoring System for Map Update

“…As a result of the high workload during an update, the frequency of updates in the necessary areas are relatively low [11]. To avoid the ineffective allocation of resources and reduce the burden of performing these map updates, specialized mapping vehicles should only be dispatched to areas that require updates on the map [12]. In this paper, we aimed to identify these specific out-of-date areas by outsourcing the detection of changes to the road environment to mass-produced vehicles equipped with low-cost sensors such as monocular cameras and GNSS.…”

Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul

“…Massow et al proposed a technical architecture, which updated traffic lanes in highway sections [ 40 ]. Kim et al [ 41 ] and Dubois et al [ 42 ] accurately updated traffic signs on simulation scenarios.…”