{'fraction_non_alphanumeric': 0.0131888836552049, 'fraction_numerical': 0.0023551577955723034, 'mean_word_length': 5.138728323699422, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Novel Method to Construct Lane Accurate Crossroad Maps by Mining Series Sensors of Large Vehicle Fleets

The future of individual mobility lies in intelligent and automated transportation. Current highly automated driving systems heavily rely on accurate map information. To ensure the advanced safety of these systems over human drivers, aggregated knowledge on infrastructure is indispensable. Therefore a lane accurate road map is a key enabler for highly automated driving systems, as well as enabler for novel location based services. To generate such a map current approaches in research are based on single sensor vehicles, equipped with lidar and camera sensors. However this current approach has high deficiencies in both scalability and the ability to adapt to local changes. To achieve the required adaptability, high penetration within the complete road network is necessary. Such a high coverage can only be obtained by an extremely large vehicle fleet. Making use of only the basic sensors of current series vehicles – including wheel speed, yaw rate, turning angle sensors – gave us exactly this advantage. To avoid induced bias of inaccurate GPS measurements, the usage is highly constrained to limited situations. With this method we accomplished to cover individual vehicle traces in the complete road network of a large city. Road intersections are one of the main components of current road maps. In this paper we therefore focus on the construction of crossroads maps, by identifying and localizing all individual lane courses. All covered crossroads are unique and hence require an automated and unsupervised mapping algorithm.In this paper we analyzed more than 30000 individual vehicle traces recorded by a diverse set of vehicle types, driven by a representative group of different drivers. We prove the feasibility of our approach by comparing the results with ground truth data of several real world crossroads on satellite images. This paper furthermore proves that even very basic sensor information reveals road geometries on lane level accuracy usable for creating a lane accurate road map.