Collaborative perception architecture in smart cities
| Autor: | Caules Pons, David |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica, Fraunhofer Institute for Cognitive Systems IKS, Cruz Llopis, Luis Javier de la, Zacchi, Joao-Vitor |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
Model Based Systems Engineering
Ciutats intel·ligents Collaborative Perception Sistemes de transport intel·ligent Intelligent Transport Systems Vehicles autònoms Enginyeria electrònica [Àrees temàtiques de la UPC] Intelligent transportation systems Automated vehicles Smart cities Automated Driving Systems |
| Popis: | Autonomous Driving Systems have become a reality in our society. Everyday, progress is made to increase vehicles' autonomy to drive without restrictions in roads and cities. To achieve that, researchers are always seeking for new methods to ensure the safety of the vehicles. A promising strategy is to improve the quality of the collected perception data as it directly influences the overall performance of the autonomous system. However, despite the advances achieved in detection methods and algorithms, perception is currently physically restricted by the available on-board sensors and their line-of-sight. To overcome this limitation, the autonomous system should not only capture on-board perception data, but also enhance it with data exchanged with other agents in the environment. This is known in research as Collaborative Perception, where mobile and stationary agents share object detection and sensor data inside an Intelligent Transport Systems network. This master's thesis brings together a collection of ETSI standards with the goal of developing a well-defined architecture for future implementation of a Secure Collaborative Perception Network in the context of Smart Cities. The architecture has been designed using the open-source software Capella Arcadia following a Model Based Software Engineering methodology. |
| Databáze: | OpenAIRE |
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