Authors:
Heiko Pikner
1
;
Mohsen Malayjerdi
1
;
Mauro Bellone
2
;
Barış Baykara
1
and
Raivo Sell
1
Affiliations:
1
Department of Mechanical and Industrial Engineering, Tallinn University of Technology, Tallinn, 19086 Estonia
;
2
FinEst Smart City Centre of Excellence, Tallinn University of Technology, Tallinn, 19086 Estonia
Keyword(s):
Autonomous Vehicles, Validation and Verification, Modeling and Simulation, Artificial Intelligence.
Abstract:
With the introduction of autonomous vehicles, there is an increasing requirement for reliable methods to validate and verify artificial intelligence components that are part of safety-critical systems. Validation and verification (V&V) in real-world physical environments is costly and unsafe. Thus, the focus is moving towards using simulation environments to perform the bulk of the V&V task through virtualization. However, the viability and usefulness of simulation is very dependent on its predictive value. This predictive value is a function of the modeling capabilities of the simulator and the ability to replicate real-world environments. This process is commonly known as building the digital twin. Digital twin construction is non-trivial because it inherently involves abstracting particular aspects from the multi-dimensional real world to build a virtual model that can have useful predictive properties in the context of the model-of-computation of the simulator. With a focus on th
e V&V task, this paper will review methodologies available today for the digital twinning process and its connection to the validation and verification process with an assessment of strengths/weaknesses and opportunities for future research. Furthermore, a case study involving our automated driving platforms will be discussed to show the current capabilities of digital twins connected to their physical counterparts and their operating environment.
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