DigiT4TAF/en
 |
DigiT4TAF |
|
|
The main aim of the project is to create a digital twin for TAF-BW, which will serve as the basis for future developments and projects in the field of autonomous vehicles. The focus is on the homologation of autonomous vehicles (AVF), the development of a test environment for traffic planning aspects, as well as scaling options by connecting additional software and expanding the digital twin. By publishing via open source and open data, access for small and medium-sized enterprises (KMU) is made as barrier-free and easy as possible.
A key motivation is the observation that the current negative impact of transport on the environment can be significantly reduced through the use of digitalisation and artificial intelligence - without compromising the quality of individual mobility. With the platform developed as part of the project, new innovation potential can be uncovered and sustainable mobility concepts can be developed and safeguarded by intelligent systems. The various mobility concepts will be analysed for their potential in order to evaluate them from different perspectives and derive the necessary recommendations for action.
A central contribution of the ATKS is the automated creation of a variant-rich data set using the TAF infrastructure, as well as the prior conceptualisation of the methods and procedures required for this. In the subsequent step, the resulting scenario catalogue is used in the form of training data for the development of AI-based behaviour models of road users in order to be able to simulate scenarios in as much detail as possible.
In addition, a virtual image of the test area is created in a simulation environment and a framework is developed that makes it possible to easily expand this digital twin in the future and adapt it to new requirements. Furthermore, safety aspects of digitalisation in road traffic are being investigated. In particular, threats to road users and transport infrastructure are identified and evaluated in the context of cyber security.
from: 1 Januar 2024
until: 15 Dezember 2024
Funding: Ministerium für Verkehr Baden-Württemberg
FZI Forschungszentrum Informatik, Hochschule Heilbronn, SSP Consult
Applied Technical Cognitive Systems
Machine Learning, Deep Learning, Artificial Intelligence