IEEE International Conference on Intelligent Computer Communication and Processing (16. : 2020 : Online) 2021 IEEE 17th International Conference on Intelligent Computer Communication and Processing (ICCP) Piscataway, NJ : IEEE, 2021 (2021), Seite 109-115 1 Online-Ressource
SAE International SAE international journal of advances and current practices in mobility Warrendale, Pa. : Soc., 2019 3(2021), 6, Seite 3159-3169 Online-Ressource
Die kartenbasierte Lokalisierung ist wichtig für die Entwicklung automatisierter Fahrzeuge. Lokalisierungen basieren oft auf semantischen Objekten wie Verkehrszeichen. Um unabhängig von Infrastrukturelementen zu sein, wird in dieser Arbeit ein Konzept zur Realisierung einer nicht-semantischen Lokalisierung auf der Grundlage von mit LiDAR-Sensoren abgetasteten Daten entwickelt, genannt LiDAR-Feature-based Localization. Dazu werden für jeden Punkt der LiDAR-Punktewolke nicht-semantische Muster bestimmt. Da nicht jeder Punkt für eine robuste Lokalisierung geeignet ist, wird ein automatisiertes Verfahren zur Erkennung signifikanter und persistenter Merkmale, genannt Feature, eingeführt. Mit gründlichen Analysen und einem Fokus auf die Umsetzung in Realdaten wird untersucht, inwieweit die Verwendung nicht-semantischer Merkmale einen Nutzen für die Lokalisierung hat. Die Untersuchungen der LiDAR-Feature-based Localization zeigen, dass der nicht-semantische Ansatz praxistauglich ist.
Map-based localization is crucial for the development of automated vehicles. Localization solutions often rely on semantic objects, like road signs. In order to be independent of these infrastructure elements a generalized concept for the realization of non-semantic localization relying on data sampled with LiDAR sensors, called LiDAR-Feature-based Localization, is developed in this thesis. For this purpose, non-semantic patterns are determined for each point of the LiDAR point cloud. Since not every point of a point cloud is suitable for a robust localization, an automated method for the detection of significant and persistent characteristics, called feature, is introduced. With thorough analyzes and a focus on the realization in real data, it is examined to what extent the use of non-semantic features has a benefit for localization. The investigations of the LiDAR-Feature-based Localization show that the non-semantic approach is suitable for practice.
Management information systems; Computer science; Commercial law; Industries; Economic sociology
Research Approaches and Objectives of Project NEMo -- The Future of Mobility in Rural Areas: Participation and Co-creation in a Real-world laboratory -- The Social Practice of Community Mobility in Rural Areas -- From Empirical Data to Operational Models: An Approach for the Development of a Decision-making Component for an Agent-based Mobility Simulation from Quantitative Survey Data -- Using an Imovative Approach for the Structural Support of Business Idea Generation -- Mobility in Rural Areas as it Relates to the Agricultural and Food Industries and People Employed in Them -- A Sustainable Software Architecture for Mobility Platforms -- Mobility Platforms as a Key Element for Sustainable Mobility -- The Erasure Obligation Independent of a Request. .
This book presents the outcomes of the trans- and interdisciplinary research project NEMo (Nachhaltige Erfüllung von Mobilitätsbedürfnissen im ländlichen Raum - Sustainable Fulfilment of Mobility Needs in Rural Areas). Due to demographic change, it is becoming increasingly difficult for rural districts and communities to maintain a basic set of public transport services such as bus and train transit without encountering issues regarding necessary social participation, sensible regional value creation and, last but not least, achievable environmental protection goals. At the same time, the demand for mobility in rural areas will continue to rise in the future, e.g. due to the concentration of medical care facilities and shopping centres close to cities. Focusing on the development of sustainable and innovative mobility services and business models, this book explains how new mobility offers can be created in which citizens themselves become mobility providers. To do so, it combines the findings of the individual research groups with external contributions from science and practice.