The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B4-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 343–348, 2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 343–348, 2022
01 Jun 2022
01 Jun 2022


M. Barsi and A. Barsi M. Barsi and A. Barsi
  • Dept. Photogrammetry and Geoinformatics, Budapest University of Technology and Economics, Hungary

Keywords: autonomous driving, environmental model, HD map, topology description

Abstract. Autonomous driving went through numerous significant improvements over the past couple of years, including driver assistants that are already capable of executing an increasing number of complex tasks without the need for any human intervention. As a result of these changes, manufacturers are relying more and more on fast, cheap, and often better-quality simulations over real-world tests. To create these environments, the real world needs to be transformed to a digital, high-definition model. HD maps – for example, the XML-based, hierarchic OpenDRIVE format – aim to serve this purpose.

The most important element of any realistic map format is the ability to check connectivity on the map in a convenient way, hence the need for topology. In HD maps, the description of junctions poses a significant challenge to the designers of the format, since they are essential yet complex topological elements. The representation of these junctions is still in progress, however, according to our analysis, the use of the current tools in OpenDRIVE can result in anomalies in the map.

In the most recent release of OpenDRIVE (version 1.7), road-road and lane-lane connections are described using links consisting of a predecessor and a successor. These however, has to be described multiple times when the junction tag is used, resulting in duplicates in the model which can be easily exploited. Our proposed solution for this issue is the elimination of the junction tag, which not only gets rid of the anomalies without any loss of information, but it also significantly reduces the size of the model. In this paper, a detailed explanation is provided of this issue and the proposed solution with examples using OpenDRIVE models.