QUALITY ASPECTS OF HIGH-DEFINITION MAPS

Abstract. A self-driving vehicle is one of the most expected inventions in the near future. These vehicles are enabled by several technological developments, like artificial intelligence, robust control, vehicular sensors, and high-speed communication. But beyond all these elements, the essential component is the knowledge about reality. Our profession has answered that question with the development of high-definition (abbreviated as HD) maps. Fully automated driving (also called driverless transportation) must be reliable enough to entrust our lives to the car. This fact indicates that the applied technology and the used map must be of high quality. But how can the quality of such a map be expressed? We are looking for the answer in the current paper.

Following Carlo Batini’s idea, the general approach is based on the triumvirate of data sources – quality dimensions – life cycle phases. Data sources cover aerial, terrestrial and mobile mapping products with the available highest technological care; furthermore, onboard vehicular sensing extends the corresponding data sets. Lifecycle phases focus on the production (data collection and processing technologies) expanded by conceptualization (pre-production) and data delivery and use (post-production). Quality dimensions are strongly related to the dimensionality of the data; they can be measured by dimension metrics.

The first part of the paper summarizes the applied data collection methodologies, emphasizing the output data. This description contains a summary of the processing mechanism – inevitably characterized by quality indicators. The paper aims to give a complete outline for the quality dimensions; we do not limit the resolution and accuracy dimensions, but other significant clusters like completeness or consistency are also discussed. Because the reality changes are enormous in transportation (vehicles, pedestrians, etc., are moving – even at higher speed) and the newly developing HD maps are expected to be live, actuality is a cardinal quality dimension as well. Vehicular technologies like SENSORIS give an excellent option to the equipped vehicles to download and use maps from the cloud and upload their field observations, opening a new way to maintain the map database. The so established crowd-sourced data collection intensely influences the map quality; therefore, this method generates quality-related issues that are also to be analyzed.

The second part of the paper is a case study, where a pilot site close to the university campus was selected. In this area, thousands of images were captured and uploaded into the Mapillary database. Artificial intelligence processes were applied for segmenting, classifying, and evaluating the content of the georeferenced imagery. The map database stores various object categories in the area, for example, pedestrian crossings, traffic signs, or trash cans. All extracted objects are available in georeferenced format, enabling spatial analyses to derive numeric quality indicators. The paper presents the complete results of this study.