Automated driving systems are in need of accurate localization, i.e., achieving accuracies below 0.1 m at confidence levels above 95%. Although during the last decade numerous localization techniques have been proposed, a common methodology to validate their accuracies in relation to a ground-truth dataset is missing so far. This work aims at closing this gap by evaluating four different methods for validating localization accuracies of a vehicle’s position trajectory to different ground truths: (1) a static driving-path, (2) the lane-centerline of a high-definition (HD) map with validated accuracy, (3) localized vehicle body overlaps of the lane-boundaries of a HD map, and (4) longitudinal accuracy at stop points. The methods are evaluated using two localization test datasets, one acquired by an automated vehicle following a static driving path, being additionally equipped with roof-mounted localization systems, and a second dataset acquired from manually-driven connected vehicles. Results show the broad applicability of the approach for evaluating localization accuracy and reveal the pros and cons of the different methods and ground truths. Results also show the feasibility of achieving localization accuracies below 0.1 m at confidence levels up to 99.9% for high-quality localization systems, while at the same time demonstrate that such accuracies are still challenging to achieve.

Volunteered geographic information (VGI) data-sets are characterised by heterogeneity due to influences from technical, social, environmental or economic factors. As a result, mapping progress does neither follow a spatially nor a temporally equal distribution, and thus can be hardly measured or predicted. Positively stated, heterogeneity leads to interesting VGI data-sets revealing regional peculiarities such as diverse community activities. This work proposes an approach for identifying regionally and temporally different developments with respect to mapping progress. Regional mapping progress is measured with a modified version of a previously proposed model for classifying activity stages, which has been used as foundation for a massive spatial and temporal analysis of the worldwide OpenStreetMap contributions between the years 2006 and 2013. It also allows the evaluation of rural and unpopulated areas. Results reveal that regional mapping progress heavily depends on a number of distinct influences such as geographical or legal borders, data imports, unexpected events or diverse community developments. The work highlights regions with distinct results by revealing individual mapping stories.

Over the last decade, volunteered geographic information (VGI) has become established as one of the most relevant geographic data sources in terms of worldwide coverage, representation of local knowledge and open data policies. Beside the data itself, data about community activity provides valuable insights into the mapping progress which can be useful for estimating data quality, understanding the activity of VGI communities or predicting future developments. This work proposes a conceptual as well as technical framework for structuring and analyzing mapping activity building on the concepts of activity theory. Taking OpenStreetMap as an example, the work outlines the necessary steps for converting database changes into user- and feature-centered operations and higher-level actions acting as a universal scheme for arbitrary spatio-temporal analyses of mapping activities. Different examples from continent to region and city-scale analyses demonstrate the practicability of the approach. Instead of focusing on the interpretation of specific analysis results, the work contributes on a meta-level by addressing several conceptual and technical questions with respect to the overall process of analyzing VGI community activity.

Trajectory data mining is a lively research field in the domain of spatio-temporal data mining. Trajectory pattern mining comprises a set of specific pattern mining methods, which are applied as consecutive steps on a trajectory with the goal to extract and classify re-occurring spatio-temporal patterns. Despite the common nature and frequent usage of such methods by the GIScience community, a methodological approach is missing so far, especially when it comes to the use of machine learning-based classification methods. The current work closes this gap by proposing and evaluating a machine learning-based 3-steps trajectory data mining methodology using the detection and classification of stop points in vehicle trajectories as example. The work describes in detail the applied methodologies with respect to the three mining steps ‘stop detection’, ‘feature extraction’ and ‘classification in traffic-relevant and non-traffic-relevant stops’ and evaluates six machine learning-based classification algorithms using a real-world dataset of 15,498 vehicle trajectories with 5,899 detected stops (thereof 2,032 manually classified). Due to its exemplary nature, the presented methodology is suited to act as blueprint for similar trajectory data mining problems.

During the last decade, connected and automated driving (CAD) has gained considerable attention. For example, automated shuttles, a specific category of automated vehicles (AVs), intend driverless operation for passenger or goods transport in constrained operational design domains (ODDs). So far, these shuttles predominately follow a static driving path, but for reaching higher automation levels, a more comprehensive digital representation of the driving environment, a so-called high-definition (HD) map is needed. However, when it comes to the definition of the scope as well as the composition workflow, a common method is missing. The current work proposes and evaluates a 4-steps workflow including sub-workflows for creating a HD map for AVs in constrained ODDs. The workflow includes sub-workflows for (1) definition of the scope based on use cases, (2) mapping and semi-automatic extraction of objects from a LIDAR point cloud, (3) HD map composition with OpenDRIVE® and Lanelet2 as target formats and (4) testing and iterative refinement with respect to the intended use cases. The workflows are evaluated by applying them on a 2-km-long test track. The resulting HD map is evaluated with two different case studies. Results may serve as guidelines for creating HD maps for AV trials.